esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Performance of Dam Piers Retrofitted with Reinforced Polymer Cement Mortar
## 3 Outline of Seismic Response Analysis for Dam Piers of Existing Dams
### Boundary Condition  
As shown in Figure 17, the boundary conditions of the dam pier were set based on actual mechanical conditions. Specifically, for both dams, the dam axis direction and the upstream and downstream directions were fixed at the bottom surface of the model. In this analysis, we focused on the dynamic behavior of only one dam pier and modeled only the levee body. The ground acceleration, as shown in Figure 18, was input in the dam axis direction of the full analysis model. In this study, seismic wave A and seismic wave B were applied to TYPE-A, -B, -C and -D. These acceleration waveforms were created from the waveforms used to verify the seismic performance of the dams and assumed a level 2 seismic motion. Seismic wave A had a maximum acceleration of 300 gal (3 m/s\({}^{2}\)) and a duration of approximately 20 s, while seismic wave B had a maximum acceleration of 441 gal (4.41 m/s\({}^{2}\)) and a duration of 24 s.  
For all dams, an additional mass imitating the gate was provided to the gate support of the dam pier. The external force applied to the dam pier due to the vibration of the gate was considered. In addition, a switchgear was installed at the top of the pier, and an additional mass that was equivalent to the mass of the switchgear was applied to the top of the dam pier, as shown in Figure 17. Furthermore, for the TYPE-A dam, hydrostatic pressure and hydrodynamic pressure were applied to the dam pier, considering the amount of water stored. In this analysis, the hydrodynamic pressure was calculated using the Westergaard [14] equation shown in Equation (8). Here, \(m_{d}\) is the additional mass that represents hydrodynamic pressure; \(\gamma_{w}\) is the unit volume weight of water; \(g\) is the gravitational acceleration; \(b\) is the length in the upstream and downstream directions of the part where the dam pier and the water storage are in contact; \(H\) is the water storage level; \(h_{1}\) and \(h_{2}\) indicate the water depth at the representative points.  
\[m_{d}=\int_{h_{1}}^{h_{2}}\frac{7}{8}\frac{\gamma_{w}}{g}b\sqrt{Hh}d\hbar= \frac{7}{12}\frac{\gamma_{w}}{g}b\Big{(}\sqrt{Hh_{2}{}^{3}}-\sqrt{Hh_{1}{}^{ 3}}\Big{)} \tag{8}\]  
Figure 16: Stress\(-\)strain relationship of steel, converted to a single axis.
# Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales
## 2 Materials and Methods
### Modelling
#### 2.2.4 Model 4  
The fourth model is an extension of the third model, based on the same distribution. In our primary assumptions, for each model so far, the intercept \(\alpha\) was a scalar. In other words, it assumed the same value for each of the predicted countries. As we can observe based on the SUV sales percentage differences between countries in the original dataset, presented in the Figure 2, it is not the best solution. That is why we decided to change our strategy for the last model.  
In order to diversify the alpha values, we introduced the following model:  
\[\gamma_{param_{i}} \sim\text{Normal}\;(0,1) \tag{13}\] \[\delta_{param_{i}} \sim\text{HalfNormal}\;(0,0.1)\] (14) \[\alpha_{i} \sim\text{Normal}\;(\gamma_{param_{i}}\delta_{param_{i}})\] \[\beta_{fp} \sim\text{Normal}\;(0,0.25)\] \[\beta_{w} \sim\text{Normal}\;(0,0.25)\] \[\beta_{rq} \sim\text{Normal}\;(0,0.25)\] \[\beta_{ma} \sim\text{Normal}\;(0,0.25)\] (15) \[\sigma \sim\text{HalfNormal}\;(0,0.1)\] \[df \sim\text{HalfNormal}\;(0,20)\] \[\mu_{i} =\alpha_{i}+\beta_{fp}\cdot fp_{i}+\beta_{w}\cdot w_{i}+\beta_{rq} \cdot rq_{i}+\beta_{ma}\cdot ma_{i}\] \[y_{i} \sim\text{Student}\;(df+2,\mu_{i},\sigma)\]  
where the following applies:  
* \(\gamma_{param_{i}}\) is a mean value parameter for the intercept \(\alpha\)'s normal distribution.
* \(\delta_{param_{i}}\) is a variance parameter for the intercept \(\alpha\)'s normal distribution.  
We replaced alpha distribution parameters with another output of another two distributions (15). For \(\gamma_{param_{i}}\) and \(\delta_{param_{i}}\), we opted for normal distributions with the following regular parameters: a mean value equal to zero for both and a smaller variance value for the one responsible for the variance itself. The other factor distributions remained unchanged from the previous model, which was well represented through the similarity of the diagrams from Figures 8 and 10.  
Figure 9: Prior predictive check for three example countries from the model with Student’s t-distribution (third model). The red line indicates the true SUV sales percentage value for the displayed country. In prior predictive checks (PPCs), we allowed for negative values and a spread of values; the main point of the prior selection was ensuring that real data were possible under prior assumptions and providing a sampler with the necessary flexibility in the parameter space.  
The implementation of a parameterised intercept resulted in changes in the appearance of the model's prior predictive check (see Figure 11). The histograms do not resemble the characteristic symmetric, normal-distribution bell shape. The range of values shifted towards positive values, and the vast majority were positive, too. Furthermore, the distribution's peak was between zero and one, and the same applied to the sales percentage data. Similar to the second model, the observed values of the displayed countries were close to their histogram's summit.
###### Abstract  
In this paper, Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) and N-doped Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) photocatalysts with magnetic core-shell structures were prepared using a multi-step synthesis method. The materials were analyzed using various techniques, such as X-ray diffraction (XRD), ultraviolet-visible diffuse reflectance spectroscopy (DRS), transmission electron microscopy (TEM), field-emission scanning electron microscopy (FESEM), selected-area electron diffraction patterns (SAED), and X-ray photoelectron spectroscopy (XPS). The results indicated that the prepared samples had an anatase structure, and N was successfully doped. Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) with different amounts of nitrogen doping were used for the study of photocatalytic degradation of methyl orange (MO) in pure MO solution, and in MO and Na\({}_{2}\)SO\({}_{4}\) (MO-Na\({}_{2}\)SO\({}_{4}\)) mixed solution, respectively. The average photocatalytic degradation rate of MO in pure MO solution with three different batches each of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) (3 mL of NH\({}_{4}\)OH used for doping) under high-pressure mercury lamp irradiation reached 85.25% \(\pm\) 2.23% and 95.53% \(\pm\) 0.53%, respectively. The average photocatalytic degradation rate of MO in the MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution with three different batches each of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) (3 mL of NH\({}_{4}\)OH used for doping) under the same irradiation condition reached 90.46% \(\pm\) 3.33% and 97.79% \(\pm\) 2.09%, respectively. The results showed that Na\({}_{2}\)SO\({}_{4}\) can promote photocatalytic degradation of MO. The experiment of recycling photocatalysts showed that there was still a good degradation effect after five cycles. Finally, the first-order kinetic model and the photocatalytic degradation mechanism were investigated.  
TiO\({}_{2}\); N-doped; photocatalytic degradation; methyl orange
###### Abstract  
Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1-2 weeks. Samples were collected prior to tumor injection and from days 2-19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish prognosis (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (\(R^{2}\) = 0.71 and adjusted \(R^{2}\) = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer.  
volatile organic compounds (VOCs); gas chromatography (GC); mass spectrometry (MS); headspace solid phase microextraction (HS-SPME); breast cancer biomarkers; principal component analysis (PCA); linear discriminant analysis (LDA); principal component regression (PCR)
## 3 Methods and Results
### D Volume Strain
#### 3.2.1 Volume Strain on Planes XY, YZ  
On XY and YZ planes, we used the Mohr circle method for two-dimensional strain analysis. There are fissures among the three trends in this method (Figure 5). Figure 5 shows a digital elevation model (DEM) of a case study of fissures in a part of the area that we obtained to assist Kinematic GPS. We calculated the strain ellipsoid using [PERSON]'s method: (\(\mathrm{R}=\sqrt{\lambda_{2}^{\prime}}/\sqrt{\lambda_{1}^{\prime}}\)).  
We used the Mohr circle method for eight cross-sections of the DEM image (Figure 5) and applied two-dimensional strain types along the cross-sections (Figure 6 and Table 1) to assist in Equations (2), (3) and (5). Finally, \(\mathrm{R}_{\mathrm{XY}}\), \(\mathrm{R}_{\mathrm{YZ}}\), and D are calculated on planes XY and YZ (Table 2).  
Figure 5: Dem image of the study area, obtained by the Kinematic GPS. Volumetric strain was assessed along the sections.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Section** & **e\({}_{a}\)\%** & **e\%** & **e\%** & **S\({}_{a}\)** & **S\({}_{b}\)** & **S\({}_{c}\)** & \(\lambda^{\prime}{}_{a}\)** & \(\lambda^{\prime}{}_{b}\)** & \(\lambda^{\prime}{}_{c}\) & **R** \\ \hline
1 & 1.62 & 0.12 & 0.84 & 2.62 & 0.87 & 1.84 & 0.144 & 0.13 & 0.29 & 2.5 \\
2 & 1.19 & 1.1 & 0.95 & 2.19 & 2.1 & 1.95 & 0.207 & 0.225 & 0.267 & 2.05 \\
3 & 1.08 & 0.96 & 0.8 & 2.08 & 1.96 & 1.08 & 0.229 & 0.25 & 0.307 & 1.75 \\
4 & 1.15 & 1.42 & 1.5 & 2.15 & 2.42 & 2.5 & 0.216 & 0.17 & 0.16 & 1.35 \\
5 & 1.61 & 1.46 & 1.41 & 2.61 & 2.46 & 2.41 & 0.146 & 0.16 & 0.17 & 1.8 \\
6 & 1.95 & 1.9 & 0.43 & 2.95 & 2.9 & 1.43 & 0.114 & 0.118 & 0.48 & 2.24 \\
7 & 0.52 & 0.62 & 1.26 & 1.52 & 1.62 & 2.26 & 0.42 & 0.37 & 0.195 & 2 \\
8 & 0.62 & 0.79 & 0.69 & 1.62 & 1.79 & 1.69 & 0.38 & 0.31 & 0.35 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical values of longitudinal and shear strains along the triangular grid sides (a,b,c) for the desired sections. Abbreviations: e = line elongation; S = line stretching; \(\lambda^{\prime}=1/\) quadratic elongation; R = strain ratio.  
Figure 6: Mohr circles for eight sections which are shown in Figure 5. Part (**a**) is related to section 1, part (**b**) is related to section 2, and so on. Using these circles, the ratio strain ellipsoid was obtained for each section. Abbreviations: \(\alpha\), B, and \(\gamma\) = angles formed by the normal of the plane and each of the principal stress axes (x, y, z) (see Figure 5); \(\lambda^{\prime}=1/\)(quadratic elongation) along (**a–c**) as the triangular grid sides (see Figure 5).
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 4 Development Details
### Preparing Algorithms for HLS  
The first step in our conversion involves translating Python code into C++. While this step implies a certain amount of redundant work, we found it rather straightforward given that the pure Python version of SyDR may be used as a golden reference when creating tests. Moreover, the mixed integration of Python with compiled C/C++ code works well for debugging as it limits the scope of changes to the individual functions of interest, while exploiting the Python environment for I/O and user interactions.  
Nevertheless, it is useful to keep the constraints of the HLS flow in mind already at this stage to avoid unnecessary re-development later on. These constraints involve several key concepts that are common and valid in software development but have no obvious equivalents in hardware; for example, recursion, unbounded loops, and dynamic memory allocation [38]. For C/C++, the former two are relatively easy to avoid while the latter implies eliminating functions such as'malloc/free' or 'new/delete' and declaring all arrays, which typically map to some kind of memory, with sizes known at compile-time. A side effect thereof is that if the code is compiled and executed, the arrays will be allocated on the stack, whose permissible size is usually small. In Vitis HLS, this issue is most easily tackled by using preprocessor directives and the __SYNTHESIS__ macro that is defined only during synthesis to enable dynamic allocation during simulation [38]. Figure 5 illustrates this for the allocation using new and deallocation using delete[] of an integer array arr with SIZE elements.  
The most challenging conversion step involves the FFT in the PCPS acquisition algorithm. While Python includes a well-established, flexible FFT algorithm in numpy, there is no comparably well-documented algorithm for C++. To match the Python implementation, we use the open-source PocketFFT [42], which numpy's algorithm is based on [43], in C SyDR. However, as this algorithm relies on dynamic memory allocation, it is incompatible with the HLS flow. For this reason, we choose to replace it with AMD's synthesizable FFT Internet Protocol (IP) block [44] in Hard SyDR; there will be more details on this later.  
Figure 5: Example of using dynamic memory allocation for simulation only with the __SYNTHESIS__ macro.
## 2 Materials and Methods
### Hydrothermal Synthesis  
The synthesis method follows the reported procedure reported by [PERSON] et al. for MIL-100(Fe) synthesis [28]. Firstly, the syntheses reported in this work do not involve hydrofluoric acid (HF acid) as a mineralizing agent, which is beneficial to the environment. In addition, the hydrothermal reaction was undertaken at 160 \({}^{\circ}\)C for 12 h. Also, 36 mmol of Fe(NO)\({}_{3}\) 9H\({}_{2}\)O and 24 mmol of 1,3,5-benzetricarboxylic acid were mixed together in 36 mL UPW, and the mixture was stirred for 60 min in magnetic stirrer. The mixture was then transferred to a 300 mL Teflon-lined autoclave and hydrothermally treated at 160 \({}^{\circ}\)C for 12 h. After cooling to room temperature, the slurry was filtered via a dynamic vacuum and Buchner flask system and washed under stirring with hot water (70 \({}^{\circ}\)C) and hot ethanol at 65 \({}^{\circ}\)C.  
In this study, the hydrothermal synthesis conditions for MIL-100(Fe) were systematically modified by changing the ratio of reactants and synthesis time. For all synthesis methods used, the iron precursor used was iron nitrate nonahydrate Fe(NO)\({}_{3}\) 9H\({}_{2}\)O, and the linker was 1,3,5-benzentricarboxylic acid (H\({}_{3}\)BTC).  
To investigate the influence of different constituent ratios on the final product's chemical and physical properties, diverse synthesis procedures were executed (as detailed in Table 1).  
Notably, all specimens were uniquely labeled using a prefix--\"MF-S\"--followed by a numerical code reflecting the specific synthesis conditions employed. A reference batch, characterized by a 1:0.1:56 (Fe\({}^{3+}\)/(H3 BTC)/water ratio, was designated as MF-S0. Likewise, other syntheses (MF-S1, MF-S2, and MF-S3) were carried out with varying (H3 BTC)-to-water ratios. Specifically, MF-S1 and MF-S3 had decreased and increased ratios, respectively. While MF-S2 shared the same constituent content as MF-S1, it differed in having a longer synthesis time.  
Furthermore, to optimize the crystallinity and purity of the as-synthesized MOF, three different washing protocols were compared. The washed samples were coded by using the suffixes W1 and W2, depending on the applied washing procedure. The washing procedures were as follows:  
* Washing procedure No. 1: The powder obtained from synthesis protocol No. 1 was dispersed in distilled water. Then, the solution was stirred at room temperature for different stirring times, namely 5 days, 10 days, and 15 days.
* Washing procedure No. 2: The powder obtained from synthesis protocol No. 1 was washed by stirring in hot water several times until the pH of the wastewater of washing became neutral. After that, the MOF was washed three times with hot ethanol. For each water wash, 700 mL of water with a temperature of 70 \({}^{\circ}\)C was used for 3 h. For the ethanol wash, 700 mL of ethanol was heated to 65 \({}^{\circ}\)C. All washing steps used a stirring speed of 200 rpm.  
Therefore, the batch MF-S2-W2 is referred to as a MOF produced according to synthesis protocol No. 2 and washing procedure No. 2.
## 2 System Model and Problem Statement
### Signal Frame Design  
To take advantage of channel correlation between non-reconciled key generation and secure transmission, we design the signal frame to perform these two processes at one channel coherence time. As shown in Figure 4, we take downlink commmunication as an example. The downlink and uplink pilot lengths are \(T_{pa}\) and \(T_{pb}\), respectively, where \(T_{pa}=T_{pb}\). The downlink data length is \(T_{d}\). In addition, \(T_{c}\) represents the interval length  
Figure 3: The proposed non-reconciled keys-assisted secure communication scheme based on channel correlation.  
Figure 2: The existing NRK-SC scheme.  
of one channel coherence time. Firstly, two legitimate users send downlink and uplink pilot signals for channel probing to generate the keys. Subsequently, we transmit data signals after the pilot signals. The keys are used to encrypt the encoded data source bits. In addition, the length of the transmitted signals must satisfy  
\[T_{pa}+T_{pb}+T_{d}<T_{c}. \tag{1}\]  
The design of short-packet communication with limited packet length can meet the low-latency requirements of wireless communication. It is especially suitable for real-time communication scenarios. In addition, we consider that the signal power ratio is equal to the length ratio, i.e., \(\alpha=\frac{T_{pa}}{T_{pa}+T_{d}}\). Hence, we can minimize the BER of the communication system by reasonably setting the power allocation parameters \(\alpha\), which will be further introduced in Section 5.
## 1 Introduction  
Pancreatic ductal adenocarcinoma is a lethal disease and the fourth leading cause of cancer-related deaths in Japan. Pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN) are important potential precancerous lesions. PanINs are defined as microscopic alyapia of glandular epithelial cells and IPMNs as histologically atypical epithelia with gross papillary lesions [1, 2]. Although these lesions may progress to become invasive adenocarcinomas, a clear assessment of this potential is difficult. Prognostic predictions, and early therapeutic actions are needed, to prevent the progression of these lesions into aggressive invasive carcinoma.  
Mucins (MUC), especially MUC1, -2, -4, and -5 AC, are reliable biomarker candidates for tumor diagnosis and prognosis [3, 4, 5, 6, 7]. MUC1 is upregulated in pancreatic ductal adenocarcinoma and is associated with an increased degree of dysplasia in PanINs. De novo expression of MUC1 in IPMNs suggests a high likelihood of progression to invasive cancer [3, 5, 6, 7].  
MicroRNAs (miRNAs) play an important role in the regulation of crucial cell processes, such as proliferation, differentiation, and development, by causing translational repression or transcript degradation [8,9].  
Despite the many demonstrated roles of MUC1 in pancreatic ductal lesions, highly specific diagnostic markers and therapeutically effective molecular targeted drugs for PDAC have not been fully commercialized, and MUC1, despite its status as a key molecule for PDAC, is also not recognized as clinically practical for diagnosis [3,4,7,10,11,12]. This is due to the fact that MUC1 has also been important in its physiological role and thus has not achieved the status of a cancer-specific marker. Searching for useful markers for diagnosis and treatment of PDAC from miRNAs associated with this important molecule, MUC1, and its target molecules will help in molecular diagnosis and molecular targeted therapy of PDAC.  
In this study, we demonstrated that MUC1, which has been used as a biomarker for predicting tumor capacity and prognosis in pancreatic cancer, acts as a key molecule in maintaining the cell proliferation and invasive capacity of pancreatic carcinomas. Additionally, miR-345 regulates the expression of MUC1 and tight junction protein 2 (TJP2), a putative target of MUC1. Therefore, miR-345 regulates cell proliferation, through apoptosis and the production of reactive oxygen species, and invasive capacity in pancreatic cancer cells.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 6 Discussion  
As shown in Figure 41, a comprehensive evaluation and analysis of the track-based automatic welding equipment for small assembly plates is conducted. Summarizing the analysis, this equipment has played a role in reducing costs and increasing efficiency in terms of practicality, specifically by reducing cost waste, shortening construction periods, and stabilizing processing quality. In terms of optimizability, future work can be based on this experimental method to verify and develop new applicable algorithms, working systems, and more intelligent and flexible robotic arms. Furthermore, the development of multiple robotic arms operating simultaneously on the track can be considered, and through structural optimization, the equipment can be designed with a lightweight focus, increasing productivity while enhancing equipment performance and extending service life, thereby further improving economic benefits. In terms of flexible adaptability, the equipment can adapt to various work scenarios by adjusting the installation height. After installation and commissioning without manual handling, it can complete automated continuous batch processing. According to the development trend in the field of automatic welding, this equipment can further be custom-developed to include multi-weld type recognition and tracking methods [31; 32; 33], automatic welding data models [34], real-time control systems, and processing algorithms [35; 36; 37]. In terms of value, this paper provides a certain reference significance for theoretical analysis and practical application in the subsequent research and development of track-based automatic welding equipment.  
Figure 40: Automatic welding equipment welding process renderings. (**a**) Panel multi-layer and multi-pass welding process effect. (**b**) Web multi-layer and multi-pass welding process effect.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Welding Method** & **Types of Welding** & **Labor and Material Costs (Yuan)** & **Average Current/A** & **Average Voltage / / / / / / / / / / / / / / / / / / / / / / / / / / / / /