esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Cyclic Loading Test and Discussions
### Energy Dissipation  
Energy dissipation capability is a fundamental value to assess seismic resistance capacity, it being a structure's ability to absorb seismic energy. The energy dissipation capacity is evaluated as the sum of the areas in the load-displacement curve of the specimen. The energy dissipation capability for each specimen is shown in Table 6. Figure 14 shows the difference between the energy dissipation capacities at previous drift ratios. The ERC specimen showed an energy dissipation of 190.49 kN\(\cdot\)mm with a drift ratio of 0.1%, which increased by about eighteen times at the 0.75% drift ratio to 3575.80 kN\(\cdot\)mm. The NoWS specimen also increased, by about twenty times, from 324.47 kN\(\cdot\)mm to 6423.29 kN\(\cdot\)mm. The reason for the large increase in energy dissipation from the 0.75% drift ratio for both specimens was that the amount of seismic energy dissipated by the structure surged because initial cracks were observed at the drift ratio of 1%. For the NoWS specimen, the energy dissipation was 1.7 times greater at the 0.1% drift ratio compared to the ERC, and this gradually and steadily increased to about 2.5 times greater at the drift ratio of 2%. This confirmed that the NoWS system was effective in improving seismic performance by significantly dissipating the seismic energy compared to the existing RC frame. These results were very similar to the results of previous studies which dealt with welded connection [24]; it indicated that the NoWS system was connected well without bolt loosening.  
Figure 13: Calculation of effective stiffness.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Drift Ratio (\%)**} & \multicolumn{2}{c}{**Values of Effective Stiffness (kN/mm)**} & \multicolumn{2}{c}{**Effective Stiffness Ratio**} \\ \cline{2-4}  & **ERC** & **NoWS** & **of ERC to the NoWS** \\ \hline
0.1 & 22.31 & 36.95 & 1.66 \\
0.25 & 18.07 & 32.88 & 1.82 \\
0.5 & 13.67 & 28.24 & 2.07 \\
0.75 & 11.23 & 25.20 & 2.24 \\
1.0 & 9.62 & 22.70 & 2.36 \\
1.5 & 7.40 & 18.92 & 2.56 \\
2.0 & 5.67 & 15.57 & 2.75 \\
2.5 & - & 12.48 & - \\ \hline \end{tabular}
\end{table}
Table 5: Effective stiffness estimated from the experimental result.
## 4 Results
### Scenario Evaluation Outcomes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ratio** & \begin{tabular}{c} **Betweenness UAM** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **Degree UAM** \\ **Network** \\ \end{tabular} &
\begin{tabular}{c} **Vulnerable UAM** \\ **Network** \\ \end{tabular} \\ \hline
5\% & 0.00293 & 0.00356 & 0.00326 \\
10\% & 0.00440 & 0.00568 & 0.00362 \\
15\% & 0.00510 & 0.00645 & 0.00449 \\
20\% & 0.00550 & 0.00920 & 0.00646 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Network algebraic connectivity results. groups. The rate of this change varies among the betweenness, the degree, and the vulnerability scenarios, highlighting differences in how each strategy impacts the network's community structure. Table 9 presents the results of the degree assortativity analysis. The findings indicate that even with only 5% of stations equipped with vertiports, homophilic connectivity (the tendency for nodes with similar degrees to connect) significantly increases compared to the baseline network. However, as vertiport placement continue to rise, degree assortativity gradually decreases. Among the scenarios, the betweenness scenario exhibits a slower decline in degree assortativity compared to the other scenarios, whereas the vulnerability scenario shows a faster decrease in homophilic connectivity. Table 10 presents the two-way ANOVA results for the network indicators under different strategies and ratios. We conduct a two-way ANOVA to determine if different network strategies (global efficiency, degree centrality, and vulnerability) result in significant variations in network indicators. The results showed that at the 0.05 significance level, most indicators demonstrated statistically significant differences for both the strategy and ratio factors. All indicators demonstrated statistically significant differences between strategies at the 10% significance level, supporting the rejection of the null hypothesis. Therefore, the selected indicators are confirmed to be meaningful criteria for identifying and explaining the structural characteristics of different network strategies.
# Aerial Remote Sensing of Aquatic Microplastic Pollution:
## 2 Methods  
We performed a systematic search for published scholarly papers--including research articles, literature reviews, and conference proceedings--relevant to aerial remote sensing of aquatic microplastics in the Scopus database (Elsevier, Amsterdam, Netherlands). The following string of microplastic-, remote sensing-, satellite-, aerial survey-, and drone-related terms were searched across the combined title, abstract and keywords of indexed documents with the aim of maximizing the discovery of relevant results while limiting the volume of irrelevant results to filter through: \"microplastic\" AND (\"remote* sens**\" OR \"satellite**\" OR \"aerial\" OR \"aircraft\" OR \"drone**\" OR \"unmanned\" OR \"unoccupied\" OR \"uncrewed\" OR \"remetely piloted\" OR \"UAV**\" OR \"UAS**\" OR \"RPA*\").  
The initial search returned 179 results (as of 26 July 2024). These were reviewed in Scopus, and papers that appeared definitely or potentially relevant to aerial remote sensing of aquatic MPs based on the title, abstract, and keywords were exported to EndNote (Clarivate, Philadelphia, USA), resulting in an initial batch of 35 tentatively relevant papers. We then reviewed the full texts of the exported papers to identify and retrieve further relevant papers cited within, and discarded several papers deemed insufficiently relevant upon closer examination.  
We grouped the final selection of relevant papers among broad themes that emerged over the course of the review process, within which we synthesized and summarized key information collectively gleaned from the papers. Finally, we formulated a concluding discussion of the current state of aerial remote sensing of aquatic MPs and recommendations for future research based on a critical assessment of the literature.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Construction of Real-Time Attribute Decision-Making Model of Multirotor Based on TOPSIS
#### 2.4.2 Decision Method Based on TOPSIS Real-Time Attribute Data  
The main principle of the ranking method of the approximate ideal solution is to judge the distances between the decision object and the ideal and negative-ideal solutions according to the ideal and negative-ideal solutions in the decision-making problem, and rank them according to the degree to which they approach the optimal solution and stay away from the worst solution. This method can effectively solve the multi-attribute decision-making problem.  
Based on the attribute-strategy decision-making matrix and the calculation method of the attribute weight, the method provides a real-time attribute decision-making algorithm for the multirotor based on the TOPSIS method. Firstly, the attribute-strategy decision-making matrix expressed by language variables is determined by expert evaluation, and the matrix \(\mathbf{AB}\) shown in Equation (1) is established according to the conversion method in Table 1. Secondly, with the decision matrix \(\mathbf{AB}\) based on this, the evaluation value of each decision element is calculated according to Equation (2), \(W(r_{ij},r_{0j})\). By substituting the evaluation value into Equations (3) and (4), the weight of each attribute in each alternative can be obtained, \(\omega_{ij}\). Using attribute weights as elements, the sum decision matrix shown in Equation (5) can be constructed. The attribute matrix \(\mathbf{AB}\) is of the same scale as \(\mathbf{W}\). Then, in order to eliminate the influence of the attribute value dimension, the Z-score standardization method is used to preprocess the attribute values of the real-time data. \(\rho^{*}\) represents the standardized real-time data. According to the principle of the TOPSIS method, the normalized real-time data attribute values are regarded as ideal solutions, that is, the weights of each attribute in the optimal solution should be infinitely close to the corresponding attributes in the ideal solution. Finally, the proximity \(\rho^{*}\) of each alternative to the real-time data is calculated according to the Euclidean distance by Equation (6):  
\[d_{i}=\sqrt{\sum_{j=1}^{g}\left(\omega_{ij}-\rho_{j}^{*}\right)^{2}},1\leq i \leq s,1\leq j\leq g \tag{6}\]  
When comparing \(d_{i}\) values to sort and compare alternatives, the lower the value, the stronger the ability of the alternative strategy to solve real-time problems. According to the present method, to maintain the consistency of the evaluation methods, as shown in Equation (7), we use \(\beta_{i}\) to denote the decision value of alternatives in the multi-attribute decision-making method based on real-time data. The larger the decision value, the better the alternatives.  
\[\beta_{i}=\frac{1}{d_{i}},1\leq i\leq s \tag{7}\]
## 4 Process and Result
### Identifying Unknown Artifact Types Based on Multiple Linear Regression Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Relic Number** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** & **A7** & **A8** \\ \hline
**Surface Weathering** & **No** & **Yes** & **No** & **No** & **Yes** & **Yes** & **Yes** & **No** \\ \hline SiO\({}_{2}\) & 78.45 & 37.75 & 31.95 & 35.47 & 64.29 & 93.17 & 90.83 & 51.12 \\ \hline Na\({}_{2}\)O & 0.00 & 0.00 & 0.00 & 0.00 & 1.2 & 0.00 & 0.00 & 0.00 \\ \hline K\({}_{2}\)O & 0.00 & 0.00 & 1.36 & 0.79 & 0.37 & 1.35 & 0.98 & 0.23 \\ \hline CaO & 6.08 & 7.63 & 7.19 & 2.89 & 1.64 & 0.64 & 1.12 & 0.89 \\ \hline MgO & 1.86 & 0.00 & 0.81 & 1.05 & 2.34 & 0.21 & 0.00 & 0.00 \\ \hline Al\({}_{2}\)O\({}_{3}\) & 7.23 & 2.33 & 2.93 & 7.07 & 12.75 & 1.52 & 5.06 & 2.12 \\ \hline Fe\({}_{2}\)O\({}_{3}\) & 2.15 & 0.00 & 7.06 & 6.45 & 0.81 & 0.27 & 0.24 & 0.00 \\ \hline CuO & 2.11 & 0.00 & 0.21 & 0.96 & 0.94 & 1.73 & 1.17 & 9.01 \\ \hline PbO & 0.00 & 34.3 & 39.58 & 24.28 & 12.23 & 0.00 & 0.00 & 21.24 \\ \hline BaO & 0.00 & 0.00 & 4.69 & 8.31 & 2.16 & 0.00 & 0.00 & 11.34 \\ \hline P\({}_{2}\)O\({}_{5}\) & 1.06 & 14.27 & 2.68 & 8.45 & 0.19 & 0.21 & 0.13 & 1.46 \\ \hline SrO & 0.03 & 0.00 & 0.52 & 0.28 & 0.21 & 0.00 & 0.00 & 0.31 \\ \hline SnO\({}_{2}\) & 0.00 & 0.00 & 0.00 & 0.00 & 0.49 & 0.00 & 0.00 & 0.00 \\ \hline SO\({}_{2}\) & 0.51 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.11 & 2.26 \\ \hline \end{tabular}
\end{table} 
Table 4: Chemical Composition of Unclassified Cultural Relics. \begin{table}
\begin{tabular}{|c|c|} \hline F(10,55) & 51.41 \\ \hline Prob \(>\) F & 0.0000 \\ \hline R-squared & 0.9633 \\ \hline Adj R-squared & 0.9558 \\ \hline \end{tabular}
\end{table} 
Table 5: Indicators for joint significance testing of F-statistics. We can derive the multiple linear regression equation for glass artifact class, chemical element content, and weathering type as follows:  
\[\hat{y}_{i}=0.7458+0.0480x_{k}-0.0299x_{Al}-0.0165x_{pb}-0.0161x_{Ba}+0.3517\ \text{Suv} \tag{40}\]
# Biosynthesis Progress of High-Energy-Density Liquid Fuels Derived from Terpenes
## 4 Strategies for Constructing Efficient Microbial Cell Factories for Terepenes
### Cell-Level Optimization
#### 4.3.2 Enhancing Product Efflux Efficiency  
Due to the limited storage of terpen inside cells, especially the excessive accumulation of monoterpenes, which have toxic effects on cells, there is a limit to increasing terpene production [156]. A strategy to effectively reduce cell toxicity is to increase the efflux efficiency of products by overexpressing cell transport proteins (Figure 2D). ATP-binding cassette (ABC) transporters are a large class of membrane proteins involved in the selective transport of different compounds. The ABCB or multidrug resistance (MDR) subfamily mainly transports hydrophobic compounds [157]. [PERSON] et al. knocked out 16 ABC transporters in yeast and complemented them with the (MDR)-type ABC transporter LaABCB1 from lavender, significantly increasing yeast tolerance to geraniol [158]. AcrAB-TolC is a member of the resistance-modulation-division (RND) family derived from Gram-negative bacteria, consisting of the inner membrane ATP-binding transporter AcrB, the periplasmic space accessory protein AcrA and the outer membrane channel protein TolC [159]. [PERSON] et al. replaced the acrAB promoter with the strong P\({}_{37}\) promoter, increasing the tolerance of _E. coli_ to pinene [47]. Additionally, [PERSON] et al. significantly increased the tolerance of _E. coli_ to linalool by overexpressing the global transcriptional regulator MarA, resulting in a 104-fold increase in colony formation efficiency of strains when linalool was added to solid plates, while strains knocking out marA or AcrAB-TolC were extremely sensitive to linalool [160].  
In addition to overexpressing cell efflux pumps, in situ product removal (ISPR) strategies have been used in the synthesis of monoterpenes and sesquiterpenes. [PERSON] et al. tested the addition of ten organic solvents during fermentation to reduce the cytotoxicity of monoterpenes and found that several solvents, such as dioctyl phthalate, dibutyl phthalate, isopropyl myristate and farnesene, effectively increased the minimum inhibitory concentration (MIC) by 100-fold compared to the control without the addition of organic solvents [161]. Furthermore, dodecane, decane and isopropyl myristate can be used in the fermentation process of sesquiterpenes and can effectively increase the production of sesquiterpenes [162].
## 3 Simulation and Experiment Results
### Results of Proof Experiments  
We build an experimental system according to Figure 6. The experimental configuration is as the following: at the transmitter, the wavelength and the linewidth of the laser are 1550 nm and 5 kHz. An AWG produces two independent PRBS which drive I and Q branches of the IQ modulator. Then the modulated QPSK and 16 QAM signals are generated. The symbol rate is set to 20 Gbaud and launched optical power to SSMF is 0 dBm. The generated optical signal is amplified by EDFA and then transmitted to a fiber link which transmission distance is 100 km. The CD index and PMD parameter of SSMF are \(16 ps/(nm.km)\) and \(0.1 ps/\sqrt{km}\). At the end of the fiber link, an EDFA is used to add ASE noise to the transmitted optical signal. The reference OSNR for QPSK and 16 QAM is varied in the range of 12-30 dB and 12-24 dB with fixed step of 2 dB for back to back system and 100 km fiber transmission systems respectively. We can obtain the reference OSNR from the optical spectrum analyzer by using out-of-band noise measurement. At the receiver end, the linewidth of local oscillator laser is 5 kHz. We use a real-time scope with 50 GSa/s sampling rate works as ADC to sample the coherent detected signals with following offline DSP blocks including proposed joint algorithm.  
As shown in Figure 9a,\(c\), with the OSNR range not lower than 7% threshold of QPSK and 16 QAM signals, the MFI decision threshold proposed in Section 2.2 can correctly divide two MF signals. In addition, the mean estimated error for QPSK signal is 0.4 dB (back to back system) and 0.4 dB (100 km fiber transmission system) and for 16 QAM is 0.5 dB (back to back system) and 0.5 dB (100 km fiber transmission system). The above results indicate that the proposed method is feasible in practical optical fiber communication systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modulation Format** & \(\epsilon\) & _Minpts_ \\ \hline QPSK & 0.05 & 23 \\ SPSK & 0.09 & 25 \\
16 QAM & 0.07 & 22 \\
32 QAM & 0.1 & 26 \\
64 QAM & 0.05 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neighborhood parameters of five different MFs.  
Figure 9: Experimental results of MFI and OSNR estimation for 20 Gbaud QPSK and 16 QAM optical communication system. (**a**) MFI results for back to back systems; (**b**) Mean absolute error of estimated OSNR for back to back QPSK signal; (**c**) Mean absolute error of estimated OSNR for back to back 16 QAM signal; (**d**) MFI results for 100 km transmission systems; (**e**) Mean absolute error of estimated OSNR for 100 km transmitted QPSK signal; (**f**) Mean absolute error of estimated OSNR for 100 km transmitted 16 QAM signal.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Complete Pipeline of the Proposed Methodology
#### 2.1.7 ResNet101  
ResNet is short for residual network. Profound convolutional neural systems have attained the human-level imagery arrangement consequence. Profound networks extract low, intermediate, and high-level topography and classifiers in an end-to-end multi-layer style. The quantity of stacked layers can augment the \"levels\" of topographies. When a deep network starts to converge, a degradation problem has been observed: as the network depth increases, accuracy becomes saturated and then quickly degrades. This degradation is not caused by overfitting. Adding more layers to a deep network leads to higher training error. The worsening of training accuracy indicates that not all architectures are easy to optimize. To overcome this issue, Microsoft introduced a profound residual knowledge outline. Instead of forcing every few stacked layers to directly fit a desired underlying mapping, residual networks allow these layers to fit a residual mapping. In a feedforward neural network with shortcut (or skip) connections, the architecture learns to approximate functions of the form F(x) + x, where F(x) represents the residual function to be learned, and x is the input to the residual block. The shortcut connections perform identity mapping, and their outputs are added to the outputs of the stacked layers. By maximizing the remaining net, many difficulties can be resolved, such as the following: ResNets are informal to enhance, but the \"plain\" nets (that simply stack layers) show advanced workout errors when the complexity upsurges; and ResNets can rapidly gain correctness from meaningfully augmented depth, manufacturing outcomes that are better than preceding networks.  
To encode the images, we used ResNet101, a CNN with 101 layers, where all layers are directly linked, improving info movement. We started with ResNet-101 pre-trained on ImageNet and fine-tuned it on our medical dataset to achieve improved outcomes. The pictures were rescaled to 224 \(\times\) 224 and regularized with the standard deviation and mean of ImageNet to match the necessities of ResNet-121 and how it was already trained on ImageNet. We increased the number of epochs from 3 to 10, but it did not make any valuable difference. We also attempted to unfreeze some layers of the base model and retrain it; however, this significantly increased the training time complexity. Therefore, we decided to retain the original fine-tuned model configuration.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 6 Simulation Using Actual Field Data  
### Simulated Scenarios  
Using the network simulation model developed in this work, the CCC, MEC, and HCC architectures were modeled and their robustness to task traffic increase was assessed. To evaluate their capabilities in a realistic situation, two different daily data traffic scenarios were generated. The first scenario simulated normal daily traffic patterns for six regions of the same city in Korea, while the second scenario assumed data traffic spike in certain regions of the city. Figures 7 and 8 show the data traffic patterns for the two scenarios.  
Figure 8: Twenty-four hour data traffic patterns for Scenario 2: regional data traffic spike.  
Figure 7: Twenty-four hour data traffic patterns for Scenario 1: normal daily traffic conditions.  
**Scenario 1 (normal daily data traffic):** In this scenario, six data traffic patterns shown in Figure 7 were used as inputs into six base stations. All the data traffic patterns shown are actual normal daily data traffic patterns acquired during the same 24-h period from the six different regions. Data for each region are gathered at regular intervals, and are grouped by an hour segment to determine the amount of data traffic per hour. In the figure, data traffic pattern for each region are labeled for clarification purposes. Unfortunately, further detail regarding these regions were not available due to confidentiality issues. Each data traffic pattern shows a different peak traffic hour. This is attributed to the type of region where data was acquired. Certain regions were residential areas, with data traffic peaks before and after work hours. Some were commercial areas, with data traffic peaks after normal work hours. Other regions were business districts, with peak traffic hours occurring during normal business hours. Using these data, responses for the three network architectures were obtained through the simulation.  
**Scenario 2 (regional data traffic spike):** In this scenario, a crowd gathering event in two of the six regions in the city was simulated. The six data traffic patterns in Figure 8 shows how the city's data traffic pattern was disrupted. During the evening hours (17:00-21:00 h), the data traffic for four regions were decreased, while the other two regions were loaded with data traffic from the four regions. As with Scenario 1, the responses for the three network architectures were obtained through the simulation using these inputs.
# The Symmetry of GPS Orbit Ascending Nodes
## 1 Introduction
The USCG [17] informs that, according to the maintenance procedure, the user may be required to download a new almanac. Further, FCSTDVs are not likely directly used to change the \(\Omega\) of a given satellite. However, there may be some small unintentional change to \(\Omega\) since other parameters such as the orbit's semi-major axis may be slightly adjusted. The orbit separation of each satellite is presented at regular near-current epochs by the USCG [17] in the form of a slant chart. However, the diagram presents only temporary separation values and does not present a long-term process trend for both the satellites and the orbital planes. This work was motivated by the presentation of the symmetry of the position of the ascending nodes of the six GPS orbital planes. In the first phase, the developed algorithm selected and assigned the satellites to individual orbital planes (OPs) based on the SEM almanac. The satellite longitude of the ascending node (\(\Omega\)) parameter, due to perturbations and other external factors, took different values in the OP subsets. Determining the position of the ascending nodes of OPs required the use of estimation methods that take into account the presence of anomalies. In this, the position of the ascending nodes was determined using a robust estimation method [17; 18] with the use of the Huber weight function [19]. In the next step, the algorithm compared the determined values of the six ascending nodes with the nominal symmetric distribution of nodes around the equator. The least-squares method was used to derive the estimators indicating the theoretical reference position of the OP ascending nodes. The use of the algorithm on the set of all 7035 almanacs resulted in the creation of datasets enabling the assessment of the ascending node (AN) separation trend. The limitation of the study is the use of broadcast almanac data rather than processed code and phase observations from worldwide stations. However, taking into account the mean several hundred-meter accuracy of the almanac parameters referred to precisely as Standard Product 3 data and the GPS orbit mean radius of 26,500 km, the impact of the error on \(\Omega\) values is smaller than 0.003\({}^{\circ}\). The work is organized in the following sections: The second section presents the research methodology with a data processing algorithm. The third section contains the AN symmetry analysis results and general trend values for satellites in six OPs. The work ends with a summary containing the main conclusions of the study.