esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
### Hybrid Configuration  
As mentioned before, the novelty of this research is using LiC instead of SC in the HESS. Using the LiC bank instead of SC with the same capacity and voltage level is lighter and less voluminous. These advantages increase the efficiency of the bus and free up more space for other applications.  
As explained in Section 5, the hybrid configuration is composed of LiB pack (37 parallel stacks of LiB cells where each stack has 96 cells in series) in parallel with a LiC pack (one stack of LiC which has 184 cells in series). In this configuration, the total distance which the bus can travel with and without LiC is calculated. As shown in the Table 8, with the HESS, the bus can traverse about 16% more than LiB-EES. As it is seen in Table 8, the maximum charge current for the LiB-ESS is 50% more than the nominal value of the current although these charging pulses are applied in a very short duration, they are repeated thousands of times over the life period of the LiB EES. As these charge pulses are beyond the nominal charge C-rate value based on the datasheet, they may have a negative impact on the lifetime and efficiency of the NMC cell and should be avoided.  
In Figure 16 the capacity degradation trend for LiB-ESS is compared with the HESS. In the LiB system, the charge over-current and high-frequency current pulses are applied to the cells while, with the application of HESS, the high-frequency stress, as well as the charge over-current are lifted from the cell resulting a longer life time and lower temperature raise in the cells. As explained in Section 2.3, the capacity degradation for LiBs is highly dependent to the temperature, DoD, and number of cycles. With the application of the HESS, as shown in Table 8, the cell temperature decreases slightly, but the discharge energy from the cell, which is represented by the number of cycles, decreases by 14%. Moreover, by lifting the peak current pulses over the LiB cells, the effect of high DoD is eliminated. Due to the above-mentioned reasons, the mileage is extended by 70,000 km.  
Figure 15: The simulation environment for both ESS and HESS.
## 3 Results and Discussion  
### Inertial Force  
The PSW masses and natural periods calculated using Equation (3) are shown in Table 2. The largest PSW mass occurs in Model 1 due to its greatest width. Scenario 2 yields a smaller PSW mass and natural period than Scenario 1 because the pile length above the seabed is shorter in Scenario 2 due to the soil slope.  
The SAs generated by the 1D soil-column seismic response analysis using TE and ME waveforms based on the damping coefficient (\(\xi\)) = 0.01-0.07 are illustrated in Figures 9 and 10, respectively. Figure 9 shows that the TE waveform has a large SA in the period shorter than 1.0 s, with the largest SA being 1.0 g at 0.6 s. Figure 10 shows that the ME waveform has a large SA in a period longer than 1.0 s, with the largest SA being 1.9 g at 1.75 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} \\ \cline{2-5}
**Model Number** & **M (kg)** & **Tu (s)** & **M (kg)** & **Tu (s)** \\ \hline
1 & 870,522 & 1.209 & 830,765 & 1.015 \\
2 & 823,790 & 1.206 & 788,711 & 1.014 \\
3 & 777,059 & 1.208 & 746,657 & 1.010 \\
4 & 730,327 & 1.213 & 704,603 & 1.008 \\
5 & 683,596 & 1.212 & 662,549 & 1.002 \\
6 & 636,865 & 1.223 & 620,495 & 0.997 \\
7 & 590,133 & 1.227 & 578,440 & 0.996 \\
8 & 543,402 & 1.238 & 536,386 & 0.995 \\
9 & 478,910 & 1.242 & 466,804 & 0.994 \\
10 & 412,878 & 1.231 & 402,790 & 0.  
The values of the inertial forces in Scenario 1 based on the TE and ME waveforms are shown in Table 3. The ME waveform produced a more significant inertial force than the TE waveform due to its larger SA for the PSW's natural period.  
Figure 10: Ground surface SA of the ME waveform.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\(\xi\)} & \multicolumn{2}{c}{**\(Fi\) (kN)**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{\(\xi\)} & \multicolumn{2}{c}{**\(Fi\) (kN)**} \\ \cline{3-3} \cline{5-8}  & & **TE** & & & & **TE** & **ME** \\ \hline \multirow{4}{*}{1} & 0.04 & 4553 & 5603 & & 0.04 & 2802 & 3536 \\  & 0.05 & 4248 & 5306 & & 0.05 & 2564 & 3220 \\  & 0.06 & 3880 & 5045 & & 0.06 & 2347 & 2998 \\  & 0.07 & 3437 & 4814 & & 0.07 & 1831 & 2812 \\ \hline \multirow{4}{*}{2} & 0.04 & 4308 & 5302 & & 0.04 & 2470 & 3116 \\  & 0.05 & 4020 & 5021 & & 0.05 & 2259 & 2837 \\  & 0.06 & 3671 & 4774 & & 0.06 & 2069 & 2642 \\  & 0.07 & 3252 & 4555 & & 0.07 & 1614 & 2478 \\ \hline \multirow{4}{*}{3} & 0.04 & 4064 & 5002 & & 0.04 & 2139 & 2803 \\  & 0.05 & 3792 & 4736 & & 0.05 & 1970 & 2616 \\ \cline{1-1}  & 0.06 & 3463 & 4503 & & 0.06 & 1802 & 2455 \\ \cline{1-1}  & 0.07 & 3068 & 4297 & & 0.07 & 1471 & 2316 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inertial forces for Scenario 1.  
Figure 9: Ground surface SA of the TE waveform.
## 2 FE Model and Validation
### Validation of Modeling Method  
#### 2.2.1 Self-Centering Pier  
The experiment conducted by [PERSON] et al. [17], as shown in Figure 3, is employed to validate the modeling of the self-centering component. The specimen has a height of 1700 mm with a \(400\times 400\) mm cross section. Grade C60 concrete (Chinese concrete grade, measured uniaxial unconfined compressive strength \(f_{\mathrm{c}}=52.7\) MPa, measured elastic modulus \(E_{\mathrm{c}}=3.8\times 10^{4}\) MPa) is adopted, while 20 mm diameter hot rolled ribbed steel bars (measured yield strength \(f_{\mathrm{y}}=349\) MPa, measured elastic modulus \(E_{\mathrm{s}}=202\) GPa) and 15.2 mm diameter strands (ultimate strength \(f_{\mathrm{PT}}=1860\) MPa, elastic modulus \(E_{\mathrm{s}}=195\) GPa) are used for the internal ED bars and unbonded prestress steel strands, respectively. The average prestress of each strand is 78.26 kN.  
Figure 3: Self-centering pier experiment (redrawn from [17]). (**a**) Design details of the Specim (unit: mm); (**b**) load pattern. ED, energy dissipation.  
Figure 2: Numerical model of the double-column self-centering pier with shear links.  
The FE model is illustrated in Figure 4. The aforementioned modeling method of the columns, the bent cap, the footing, and the unbonded prestress steel strands in Section 2.1 has been adopted. Note that the unbonded parts of the internal ED bars are modeled by elasto-plastic 2-node truss elements, while the bonded parts share nodes with the concrete elements. The initial stress of the unbonded prestress steel strands is set as 431.3 MPa according to the test results.  
The hysteretic curves are shown in Figure 5. The comparison of experimental results and simulation results demonstrates the validity of the self-centering pier model.
## 3 Results
### Habitat Quality Change
#### 3.2.3 Habitat Quality Change Across Different Land Use Classes  
Given the considerable variation in the development characteristics and protection levels of the different land use classes, a further investigation was conducted to identify the underlying cause of the change in habitat quality across the different land use classes (Figure 9). Despite relative stability in cropland habitat quality over the past 40 years, the slight improvement suggests successful agricultural land management practices. In addition, forest land is the dominant land use class in regions with excellent and good habitat quality. More notably, forest quality showed a concerning polarization trend, with accelerated degradation particularly after 2010, suggesting that a potential ecological threshold was reached.  
Moreover, the quality of grasslands showed a critical tipping point in 2010, after which all quality levels showed a significant decline, indicating a cascading degradation process. Meanwhile, unlike other land classes, water bodies showed fluctuating quality patterns rather than unidirectional degradation, with relative stability in excellent- and good-quality areas after 2010, possibly reflecting the effectiveness of water resource management measures. The synchronized expansion of construction land and the degradation of unused land quality during 2010-2020 reveal the compound effects of urbanization on habitat quality. In summary, forests and grassland are important sources of excellent-quality habitats in China, and the interconnected degradation patterns across different land use classes suggest a complex web of ecological interactions that warrant immediate attention.  
Figure 8: Transformation in habitat quality levels (1980–2020).
## 4 Website Interface Architecture
### Back-End Structure  
The back-end architecture of our system consists of two main components: a Python-based API server using FastAPI and a JavaScript file handling front-end interactions and API calls. This structure allows for a separation of concerns between server-side processing and client-side operations.  
The Python back-end is organized as follows:  
1. **API Endpoints:** The FastAPI application defines several key endpoints, including: * check_valid: Validates user queries using the function defined in Section 3.2.2.
* process_query: Initiates the full query processing workflow, using the functions defined in Section 3.2.3 through Section 3.2.9.
* sse: Implements Server-Sent Events (sse) to give the user real-time updates while the generation workflow is running.
* db_sim_search: Performs a similarity search on existing questions, using the algorithm defined in Section 3.2.1.
* db_get: Retrieves stored answers from the database.
2. **Asynchronous Processing:** The back-end utilizes asynchronous programming techniques, specifically Python's asyncio library and FastAPI's asynchronous capabilities to handle concurrent requests efficiently.
3. **Background Tasks:** Long-running processes, such as query processing, are handled as background tasks to prevent blocking the main thread.
4. **Database Interactions:** The system interacts with two MySQL databases to store and retrieve processed question-answer pairs and article analyses.
5. **Hosting:** The FastAPI back-end is deployed on an Amazon Web Services (AWS) Elastic Compute Cloud (EC2) t2.small instance.
# The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice
## 4 Discussion  
Based on the drawings, we observe that in the serviceability limit state, the most significant influence on the probability of failure comes from random variables associated with the beam's geometry: D, d, l. Negative values associated with the random variables d and l indicate that an increase in these quantities will lead to a decrease in the reliability index, both in terms of mean values and standard deviation. An increase in d will reduce the moment of inertia of the cross-sectional area of the rod, consequently increasing displacement. The length of the rod, l, in the displacement expression is raised to the fourth power in the numerator, hence an increase in this quantity will also result in an increase in displacement. An increase in the dimension D of the cross-sectional area of the rod will increase the moment of inertia, significantly reducing displacement. This quantity, both in the \(\alpha\) vector and in the elasticity of reliability index, is positive and has the highest value. The influence of changes in Young's modulus E and load q on the probability of failure is minimal. In further calculations, these quantities can be treated as deterministic.  
The probability of failure associated with the ultimate limit state depends, of course, on changes in the dimensions of the cross-sectional area, D and d. The elasticity of reliability index with respect to the mean value of the random variable D is +7.37, while for the random variable d, it is--4.15. An increase in the mean value of the random variable d will result in a decrease in the reliability index associated with a reduction in the moment of inertia. The influence of changes in the yield strength fy is significant. The influence of changes in the load and length of the beam is significantly smaller.  
The authors present two alternative ways of designing in a probabilistic approach using the FORM (SORM) and Monte Carlo simulation. The approximation FORM cannot be used in every case in connection with gradient determination problems. In such cases, it is worth using the Monte Carlo simulation method. The results of both methods are comparable.
## 4 Seismic Data Interpretation
### Time and Depth Contour Maps and Surfaces Maps
#### 4.3.1 Time Contour Maps  
A contour map is simply defined as a line that exhibits parameters on a 2D map, such as depth or elevation [20]. With the help of time rates, the contours are created to show the time of the waves that reflect back from pronounced condensed reflectors or strata due to the density/velocity contrast or simply acoustic impedance. The time contour is initiated on a map for every reflector. In our research, the reflectors of relevance are the Dunghan, Samana Suk, Datta, and Warcha formations. The time contour maps of the four essential reflectors on five seismic sections are projected on the Base Map. Justified to the depth along with other diverging capacities in the path of the traveling waves, the reflection time is further computed in order to give an idea about the structures and the time delay of the seismic wave feedback.
## Data and Methods
### Remote Sensing Datasets
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Indices** & **Equation** & **References** \\ \hline NDVI—Normalized Difference & \(NDVI=\frac{(\rho NIR-\rho Red)}{(\rho NIR+\rho Red)}\) & [48] \\ \hline NDAVI—Aquatic by & \(NDAVI=\frac{(\rho NIR-\rho Blue)}{(\rho NIR+\rho Blue)}\) & [49] \\ Normalized Difference & \(NDAVI=\frac{(1+L)}{(\rho NIR+\rho Blue)}\) & [50] \\ \hline WAVI—Adjusted to Water & \(WAVI=(1+L)\frac{(\rho NIR-\rho Blue)}{(\rho NIR+\rho Blue+L)}\) & [50] \\ \hline sPRI—Photochemical Reflectance & \(\begin{array}{c}PRI=(\frac{\rho Blue-\rho Green}{Blue+Green})\\ sPRI=\frac{(PRI+1)}{2}\end{array}\) & [51] \\ \hline CO\({}_{2}\)Flux1—Integrated & \(CO_{2}Flux=(NDVI\times sPRI)\) & [52; 53] \\ \hline CO\({}_{2}\)Flux2—Integrated NDAVI & \(CO_{2}FluxDNAVI=(NDAVI\times sPRI)\) & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vegetation indices used in the study.
# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
###### Abstract  
Database systems have a large number of configuration parameters that control functional and non-functional properties (e.g., performance and cost). Different configurations may lead to different performance values. To understand and predict the effect of configuration parameters on system performance, several learning-based strategies have been recently proposed. However, existing approaches usually assume a fixed database version such that learning has to be repeated once the database version changes. Repeating measurement and learning for each version is expensive and often practically infeasible. Instead, we propose the Partitioned Co-Kriging (PCK) approach that transfers knowledge from an older database version (source domain) to learn a reliable performance prediction model fast for a newer database version (target domain). Our method is based on the key observations that performance responses typically exhibit similarities across different database versions. We conducted extensive experiments under 5 different database systems with different versions to demonstrate the superiority of PCK. Experimental results show that PCK outperforms six state-of-the-art baseline algorithms in terms of prediction accuracy and measurement effort.  
database; version; performance modeling; co-kriging; transfer +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion  
The discussion covers the following topics: A--MGF model validation, B--software implementation notes, and C--computation data visualization. The discussion analysis in this section takes into consideration three concluding observations from Figure 2. These observations are: (1) the water channel impulse response \(h_{c}(t)\) based on the models DGF, WDGF, BP, and CEAPF has a bandwidth narrowing impact on the transmitted signal \(s(t)\), (2) the bandwidth narrowing of the four models nearly the same at Full Width at Half Maxima (FWHM), and (3) the bandwidth narrowing in general for Harbour water is more significant than that of the Coastal water.  
The computation in this paper considers the propagation channel impulse response based on the DGF model only. The reason behind such a step can be justified as follows: the channel models' data visualisation in Figure 2 for Coastal and Harbor seawaters helps shed more light on the justification of using just the DGF channel model. Figure 2a,c show the channel models for Coastal and Harbor waters, respectively. From these figures, it is easy to notice that all seawater channel models have the same temporal profile, practically a DGF profile, but with different temporal broadenings (i.e., different frequency bandwidths). To support this observation, we can use the statistical techniques of transforming the random variable [21-Chapter 2] on the WDGF, CEAPF, and BP to produce the DGF distribution.  
Including the DGF impulse response in our computation makes thecomparison with the receiver performance in [10-16] unfruitful. This is because the works in [10-16] considered the propagation channel impulse function a delta function. However, when relevant, we mentioned the other aspects of comparisons in this section.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods
### Measurement and Preprocessing of Point Cloud Data for Radar Dome Surfaces
#### 2.1.2 Point Cloud Data Preprocessing  
During the acquisition of point cloud data on the surface of the aircraft radome, various factors may introduce disturbances. These factors include uneven lighting, vibrations, object occlusions, and aging of the scanning equipment, which could result in noise and voids in the point cloud data. Therefore, preprocessing the point cloud data becomes necessary to ensure its smoothness and uniformity. In order to handle the high-density point cloud data acquired by the 3D sensor, this study employed a voxel filtering method for down sampling [23]. It is a method that effectively reduces the number of data points while preserving the details of the point cloud. Additionally, a statistical filtering technique was employed to remove outliers in the down sampled point cloud [24]. For each point \(Q_{i}\) in the point cloud, its 50 nearest neighboring points (\(N_{i1}\), \(N_{i2}\), \(N_{i3}\), \(N_{i50}\)) are first determined.  
Figure 3: An aircraft radome and its 3D scanned point cloud of aircraft radome.  
Figure 2: Development of an automated robot coating laser cleaning system.  
Subsequently, the Euclidean distance \(d_{ij}\) between the point \(Q_{i}\) and its neighboring points was calculated using the following formula:  
\[d_{ij}=\sqrt{\left(Q_{ix}-N_{ijx}\right)^{2}+\left(Q_{iy}-N_{ijy}\right)^{2}+ \left(Q_{iz}-N_{ijz}\right)^{2}} \tag{1}\]  
where \(Q_{ix},\ Q_{iy},\ Q_{iz},\ N_{ijx},\ N_{ijy},\) and \(N_{ijz}\) represent the coordinates of the points \(Q_{i}\) and \(N_{ij}\) along the x, y, and z axes, respectively. Next, for each point \(Q_{i}\), the average distance \(\overline{d}_{i}\) to its 50 nearest neighboring points is calculated:  
\[\overline{d}_{i}=\frac{1}{50}{\sum_{j=1}^{50}d_{ij}} \tag{2}\]  
Additionally, the global average of the mean distances between all points and their neighboring points was calculated, resulting in two values, denoted as \(\mu\) and \(\sigma\).  
\[\mu=\frac{1}{N}{\sum_{i}^{N}\overline{d}_{i}} \tag{3}\]  
\[\sigma=\sqrt{\frac{1}{N-1}{\sum_{i=1}^{N}\left(\overline{d}_{i}-\mu\right)^{2}}} \tag{4}\]  
here, \(N\) represents the total number of points in the point cloud.  
Lastly, a threshold value \(T=\mu+k\cdot\sigma\) was set, where \(k\) is a constant (in this paper, \(k\) is set to 1). Any point \(Q_{i}\) with an average distance \(\overline{d}_{i}\) exceeding this threshold (i.e., \(\overline{d}_{i}>T\)) was considered an outlier and was removed from the point cloud. The radome point cloud shape after preprocessing is shown in Figure 4.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 2 Materials and Methods
### Prediction-Based Anomaly Detector  
When dealing with one-dimensional time series data, there are two common methods for anomaly detection. The first is window-based anomaly event sequence detection, where the time series data are divided into multiple windows, and then the anomaly score for each time subsequence within each window is calculated [18; 19].  
Figure 1: Data coverage of the HF radar network system. Circular markers indicate the locations where radar data are collected, with colors representing radar acquisition rates (indicated by the color bar). Red stars mark single radar station locations.  
The second method is prediction-based anomaly detection. Since anomalies are events that deviate from expectations, a natural approach is to compare the predicted data with the actual data to perform anomaly detection [20,21]. In our study, we employed the prediction-based anomaly detection method as illustrated in the figure below (Figure 2).
## 1 Introduction
Consequently, research studies regarding these attributes have been primarily motivated  
Figure 1: General structure for anthocyanin pigments (A and B rings were labelled). by the intent to explore new sources of natural antioxidants for use in food supplements or other food applications. In this regard, berry fruits are a very valuable resource due to their phytochemical composition, nutritional value, and antioxidant properties, all of which make it a valuable resource. Lately, research targeted the analysis of the composition and antioxidant properties of different berry fruits (such as blackberries and grapes) in order to discover the optimal extraction methods of the active compounds. The conventional solvent extraction is the most used technique for the extraction of antioxidant compounds [26], although new non-conventional procedures have been applied as environmentally friendly options, such as microwave, ultrasound, or pressure-assisted extraction, applied alone or combined with the solvent method, in order to decrease energy and solvent demand [27]. This is also the case of the present study, in which the solvent extraction is combined with ultrasonication in order to improve the yields of extraction, but also to reduce the procedure requirements in terms of energy and solvent quantity. Practically, the combination of a classical method and a non-conventional one represents both a novelty and an advantage of this study. Over the last years, the analytical techniques saw increased development, contributing to a better knowledge of polyphenols and anthocyanins. Once liquid chromatography mass spectrometry (LC/MS) and multiple mass spectrometry (MS/MS and MS\({}^{\text{Sn}}\)) techniques were developed in the 1990s, polyphenols structures began to be elucidated [28; 29]. Various investigations on anthocyanins from grapes and wine have been developed since, making possible the better understanding of their involvement in wine making and aging [14; 15]. Additionally, because of the widespread consumption of different berry fruits and the potential benefits they may possess related to human health and preventive medicine, studies such as the present approach are both significant and beneficial to the scientific community. The objective of this work was the development of a reverse-phase, high-performance liquid chromatography (RP-HPLC) method coupled with diode array (DAD) and mass spectrometer (MS) detection in order to separate, identify, and quantify flavonoid glycosides from blackberries and black grape skins, but also to elucidate the relationship between antioxidant activity and the levels of total anthocyanin and total phenolic content.