esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
In Figure 7, the ASC is plotted against the RF link's instantaneous SNR \(\lambda_{SR}\) in the mixed system, while the eavesdropper experiences varying levels of interference SNR \(\lambda_{IE}\) from the optimal user. Simulation results show an ascending trend of the ASC alongside increasing \(\lambda_{IE}\). When \(\lambda_{SR}\)= 30 dB, \(\lambda_{IE}=\)2, 3, 6 and 9 dB, the system's ASC values of 1.95, 1.96, 1.98, and 2.01 support the conclusion that higher interference SNR \(\lambda_{IE}\) from the optimal user boosts the ASC to a certain degree. This affirms that transmitting jamming signals from the optimal user to the eavesdropper enhances the security of the communication system, and higher transmission power bolsters the PLS performance. The graphic in Figure 8 plots the mixed system's ASC against the FSO link's instantaneous SNR \(\lambda_{RD}\) model which is based on the \(\mathcal{F}\) distribution under the optimal user interference for different turbulence conditions. The figure shows that when \(\lambda_{RD}\) = 30 dB, the ASC of the system is 2.08 in weak turbulence conditions of a = 2.33 and b = 4.53. When a = 1.83 and b = 3.94, the ASC of the system is 2.02. The ASC of the system is 1.94 in the case of strong turbulence with a = 1.43 and b = 3.53. As the atmospheric turbulence intensity increases, the ASC of the system decreases. This shows that the FSO link is sensitive to weather conditions, and a bad weather environment will seriously affect the security performance of the FSO link. Figure 8: Simulation of the ASC of the RF/FSO system with different turbulence factors a, b and the instantaneous SNR \(\lambda_{RD}\) of the FSO link under optimal user interference. Figure 7: Simulation of the average secrecy capacity and the instantaneous SNR of the RF link of the RF/FSO system under different interference SNRS \(\lambda_{IE}\) of the optimal user.
# Fire Safety Risk Analysis of Conventional Submarines
## 4 Fire Safety Risk Considerations--Technology Review
### Fire Risk-Stirling Engine
#### 4.8.4 Technology Maturity  
The invention of the Stirling engine dates back to 1816 when it was first patented by inventor [PERSON] [73]. The technology has a long history of development, however, although considered a stepping stone to the development of many other technologies, Stirling engines have limited use in niche applications today. Due to its ability to operate with any heat source and its relatively quiet operation, one of those niches is submarine AIP.  
Despite earlier efforts by the US Navy and others, development of Stirling engines as an AIP solution for submarines did not reach the point of integration until the late 1970s when Kockums Submarine Systems in Sweden began its development program. This resulted in the first fully functional Stirling engine AIP module being integrated into a Nacken class submarine in 1988 [74]. Further development of this technology was subsequently integrated into the Swedish Gotland and Sodermanland class submarines. The Singapore Navy now operates two ex-Gotland class submarines, the Archer class, both with Stirling engine AIP.  
Importantly, Stirling engines are the only AIP technology with a service history in large conventional submarines, having been in-service on the Japanese, 4200 tonne Soryu class submarines since 2009. With several operators, over 30 years' service on medium-sized conventional submarines and over 10 years' service on large conventional submarines, Stirling engines are perhaps the most mature submarine AIP technology.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 5 Simulation Experiment and Result Analysis  
In order to verify the effectiveness of the Multi-Agent Deep Q Network algorithm for joint dynamic hypergraphs proposed in this paper, a series of simulation experiments are conducted. First, the main simulation parameter settings used in the experiments of this paper are listed in detail. Second, the advantages of the proposed DH-MDQN algorithm in improving the system throughput are verified by comparing it with other benchmark algorithms.
## 1 Introduction
### Contribution to Research  
The enhancement of HD video demands and online services has increased the need to employ improved LDOTNs. Therefore, in this paper, modern soft decoding and universal DSP technologies are tested for maximizing LDOTNs' performance against the impact of NLPns. Constellation shaping (CS) is discussed herein for DP16 and 64 QAM modulation formats. The significant contributions of the proposed LDOTN model are summarized below.  
1. High data traffic load has increased the burden on optical networks. As a result, the efficiency of existing systems has become limited due to high NLPn factors. This paper introduces a novel soft detecting technique to tackle the impact of NLPns and improve transmission distance and multichannel propagations.
2. The advanced modulation DP-16/64 QAM is utilized with international telecommunication union and telecommunication (ITU-T) standard low-dispersion fibers (G-652.D and G-657.A1).
3. Universal digital signal processing (DSP) is applied to filter the data before the proposed decision block.
4. In order to validate the theoretical model with simulation analysis, the analytical model is studied and the generation of NLPns and their measuring procedure are explained in this paper.
5. Normalized mutual information (NMI) and the optical signal-to-noise ratio (OSNR) advanced estimating parameters are used for investigating the proposed model's outcomes and their continuity against nonlinear factors.  
The rest part of this paper is organized as follows. Section 2 comprises the analytical model of LDOTNs with the existence of NLPns and how to cope with these impairments; the proposed LDOTNs framework based on soft decoding and universal DSP is discussed in Section 3. Similarly, Section 4 includes the results, their analysis and their discussion using back-to-back and transmission reach stages, before this work is concluded in Section 5.
# Population Bias on Tornado Reports in Europe
## 4 Results  
Based of the data from ESWD a total number of 2319 tornadoes were observed in Europe between 2006-2020 over the NUTS3 regions considered in this article. The predicted number of tornadoes by the model considering the population bias is 3563 tornadoes (Table 1). Thus, 65% of tornadoes predicted by the model were reported.  
The difference between predicted and observed number of tornadoes is low (\(<\)0.027 tornadoes 10,000 km\({}^{-2}\) yr\({}^{-1}\)) over parts of the United Kingdom, the Netherlands, Belgium, Germany, and Italy (Figure 1). As indicated previously [1; 14], these are regions characterized by both a high number of tornado reports and high population density. For some NUTS3 regions there were no tornadoes reported during the study period, for example, parts of northwestern and southeastern France, central Romania, central Italy, Albania, Northern Macedonia, southern Bulgaria, Finland. For these regions the predicting values of tornado counts are less then 2 tornadoes over the 15 years study period. The highest difference between predicted and observed tornadoes (between 0.33-0.41 tornadoes 10,000 km\({}^{-2}\) yr\({}^{-1}\)) is over Iceland and northern parts of the United Kingdom, Norway, Sweden, and Finland (Figure 1). This is not surprising given that the population density of these regions is low compared with other regions of Europe and also given that the number of observed tornadoes by NUTS3 regions in this area is less than 10 tornadoes over the entire study period.  
Low values for standard deviation (\(<\)0.13 tornadoes 10,000 km\({}^{-2}\) yr\({}^{-1}\)) of the tornado occurrence from the posterior distribution are found over most of Europe (Figure 2), with the exception of an area stretching from eastern Germany over Austria, Croatia, Bulgaria, and Greece characterized by values between 0.13-0.30 tornadoes 10,000 km\({}^{-2}\) yr\({}^{-1}\). In these regions the NUTS3 area are characterized by relatively small area and low population density and also a low number of observed tornadoes during the study period.
# Thermodynamic Analysis of In-Cylinder Steam Assist Technology within an Internal Combustion Engine
## 3 Results and Discussion
### Effects of Injection Temperature and Mass on Cycle Performance
However, the work produced by the original gas drops due to the temperature decrease caused by injecting steam into the combustion chamber. The whole amount of work output improves from 1096 J to 1261 J at the same fuel consumption rate, which means the cycle performance and  
Figure 4: Relationship between cycle performance and steam injection mass at various injection temperatures (P\({}_{\text{steam}}\) = 20 MPa; P\({}_{\text{inkate}}\) = 0.1 MPa; CR = 10). (**a**) In-cylinder/exhaust temperatures; (**b**) Thermal efficiency. thermal efficiency are improved. From a thermodynamic perspective, high-temperature steam injection enhances thermal efficiency and cycle performance because the specific heat ratio of H2O is higher than that of CO2 despite both being triatomic molecules. On average, the specific heat ratio of H2O is 0.8 times higher than that of CO2 at various temperatures. In the expansion stroke, steam has a more significant advantage in heat absorption than exhaust, increasing power output. Moreover, without considering the influence of steam on combustion, injected steam is also heated to an overheated state by absorbing combustion heat. The heated water transforms into high-pressure and -temperature steam and then acts as an extra working fluid during the expansion stroke. This is a passive influence of the combined cycle on power output.
## 4 Analysis and Discussion
### Accuracy Verification of Road Longitudinal Slope Automatic Detection
Meanwhile, the percentage of detection values with relative errors less than 10% at 20 km/h, 30 km/h, and 40 km/h speeds are 93.33%, 93.33%, and 100%. Although at 20 km/h and 30 km/h, the proportion of detection values that meet the requirements of relative error is less than 95%, the absolute error of 100% detection values is less than 1.5%. In other words, the proportion of detection values with absolute error less than 1.5% is more than 95%. In summary, the error analysis results under three vehicle speeds comply with the relevant regulations in JTG/T E61-2014 Standard in China [27], proving that the vehicular detection system used in this study meets the longitudinal slope accuracy requirements. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Distance Range/m**} & \multicolumn{3}{c}{\(S_{AM}\)**/\%**} & \multicolumn{3}{c}{\(E_{AE}\)**/\%**} & \multicolumn{3}{c}{\(E_{RE}\)**/\%**} \\ \cline{2-11}  & **20 km/h** & **30 km/h** & **40 km/h** & \multirow{2}{*}{**\(S_{\text{H}\omega}\)**/**} & \multirow{2}{*}{**20 km/h**} & **20 km/h** & **30 km/h** & **40 km/h** & **20 km/h** & **30 km/h** & **40 km/h** \\ \hline
0–10 & \(-8.91\) & \(-9.15\) & \(-9.32\) & \(-10.10\) & \(1.19\) & \(0.95\) & \(0.78\) & \(11.83\) & \(9.41\) & _7.69_ \\
10–20 & \(-9.29\) & \(-9.49\) & \(-9.67\) & \(-10.20\) & \(0.91\) & \(0.71\) & \(0.53\) & \(8.89\) & \(6.99\) & \(5.19\) \\
20–30 & \(-9.55\) & \(-9.92\) & \(-9.71\) & \(-10.20\) & \(0.65\) & \(0.28\) & \(0.49\) & \(6.39\) & \(2.79\) & \(4.79\) \\
30–40 & \(-10.07\) & \(-10.55\) & \(-10.27\) & \(-10.36\) & \(0.29\) & \(0.19\) & \(0.09\) & \(2.82\) & \(1.81\) & \(0.85\) \\
40–50 & \(-10.60\) & \(-10.99\) & \(-10.76\) & \(-10.40\) & \(0.20\) & \(0.59\) & \(0.36\) & \(1.91\) & \(5.63\) & \(3.48\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error analysis between vehicular detection system and manual measurement results. Figure 5: Verification test on the accuracy of automatic detection of longitudinal slope. (**a**) Diagram of manual measurement. (**b**) Manual measurement on site. (**c**) Automatic measurement on site.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 3 Rural Microgrid Modeling
### Load Unit Modeling  
The rural microgrid load unit includes residential loads and agricultural loads. The electricity consumption of agricultural loads accounts for a large part of the total consumption of the rural microgrid. Meanwhile, the electricity consumption of agricultural loads will change with crop types, growth period and climate environment. Therefore, modeling and analysis of agricultural power consumption is of great significance to optimize the electricity dispatching of rural microgrid.
## 3 Results and Discussion
### Prediction of Optimal Conditions by ANN-PSO  
As presented in Table 6, the maximum removal efficiency was 89.50% by using the RSM model corresponding to initial pH of 4.16, temperature of 29.16 \({}^{\circ}\)C, contact time of 114.54 min and initial concentration of 619.61 mg/L. The confirmatory experiment was carried out under the following conditions: 4.20 for initial pH, 29.20 \({}^{\circ}\)C for temperature, 115.00 min for contact time and 620.00 mg/L for initial concentration. As shown in Figure 15, the performance of ANN-PSO demonstrates that the maximum removal efficiency (92.55%) was achieved after 10 iterations. The optimum conditions obtained by the ANN-PSO model are 4.00 for initial pH, 30.00 \({}^{\circ}\)C for temperature, 120.00 min for contact time and 600.00 mg/L for concentration. Based on the process parameters, the confirmatory values of the RSM and ANN-PSO models are 77.37% and 84.87%, respectively. The absolute errors  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of** **Neurons**} & \multicolumn{6}{c}{**\(w_{i}\)**} \\ \cline{2-7}  & \multicolumn{4}{c}{**Input Variables**} & \(b_{i}\) & \(w_{j}\) & \(b_{j}\) \\ \cline{2-7}  & **Initial pH** & **Temperature** & **Contact Time** & **Initial Concentration** & & \\ \hline
1 & 0.2899 & \(-\)1.4754 & \(-\)1.9836 & \(-\)0.05183 & \(-\)2.4896 & \(-\)0.9964 & \\
2 & \(-\)0.04558 & 1.9856 & 1.1144 & 1.0057 & 1.9363 & 0.4235 & \\
3 & \(-\)1.0854 & 0.2040 & \(-\)1.7355 & \(-\)1.4023 & 1.3831 & 0.7355 & \\
4 & 1.9209 & \(-\)0.9750 & \(-\)0.5651 & \(-\)1.1128 & \(-\)0.8299 & \(-\)0.7634 & \\
5 & \(-\)1.3428 & 0.3335 & \(-\)0.4420 & 2.0220 & 0.2766 & \(-\)0.9220 & \\
6 & 1.3265 & 1.5239 & 1.4140 & 0.3420 & 0.2766 & 0.1964 & \\
7 & \(-\)1.4517 & 1.2288 & \(-\)1.4871 & \(-\)0.6076 & \(-\)0.8299 & 0.2086 & \\
8 & \(-\)0.6918 & \(-\)0.07202 & 1.4166 & 1.9255 & \(-\)1.3831 & 0.03286 & \\
9 & 1.1988 & \(-\)0.2760 & \(-\)1.0214 & 1.9083 & 1.9363 & \(-\)0.9850 & \\
10 & 0.7757 & \(-\)1.0114 & \(-\)1.3414 & \(-\)1.6655 & 2.4896 & 0.3779 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The weights and biases of BP-ANN in input-hidden layer (\(w_{i}\) and \(b_{i}\)) and hidden-output layer (\(w_{j}\) and \(b_{j}\)).  
Figure 14: The MSE of the number of epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variables** & **Relative Significance (\%)** & **Order** \\ \hline Initial pH & 7.63 & 3 \\ Temperature & 38.82 & 2 \\ Contact time & 52.19 & 1 \\ Initial concentration & 1.36 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relative significance of input variables.  
between the confirmatory and predicted results (12.13% and 7.68%) illustrate that the ANN-PSO model has a better predictive capacity than the RSM model.
## 3 Methodology
### Questionnaire and Data  
The constructs for our research model are adapted from existing research (cf. Appendix A). Since we conducted the study in Germany, the items had to be translated. We first translated the English questionnaire into German with the help of a certified translator (translations are standardized following the DIN EN 15038 norm). A second independent certified translator retranslated the German version back into English to ensure the equivalence of the translation. Finally, five experts compared the two English versions for equivalence. Such an iterative process of translating questionnaire constructs was done in prior studies [50]. Preliminary reliability and validity of the constructs was tested with students of a Master's course.  
Since we investigate the role of CBN as a positivity bias affecting the use of Pokemon Go, we only sampled active players of the game. We conducted the study with a certified sample provider (certified following the ISO 26362 norm) in order to ensure quality of our data. 9338 participants from the online panel started the online survey. 683 participants remained after asking whether they are older than 18 years, play Pokemon Go and after deleting participants who incorrectly answered a test question. Two additional participants were deleted since they stated to \"never\" play the game. We asked the participants about their current level in Pokemon Go in addition to the test questions. We did this since the game ends at level 40 and we could test the knowledge of the participants about the game. Some participants stated to have a level higher than 40 and we consequently deleted them as well, assuming that they actually do not play the game or did not read the questions carefully. Based on these screen-outs, we only considered participants aged 35 years or younger, as older players are--per definition of the used operationalization of nostalgia--too old to be influenced by childhood brand nostalgia for Pokemon. The final sample used for the data analysis consists of 418 active players (cf. Appendix B, Table A1 for the descriptive statistics).
## 5 Discussion
### Added Value of the ESTARFM Framework for Cloud-Prone Areas  
In West Africa, as in many regions with a distinct rainy season, acquisitions of clear sky observations from polar orbiting instruments are challenging [38]. Landsat data are almost only available during the dry season, when vegetation and land surface in general have a significantly different appearance than during the rainy season. Previous studies indicated the importance of using Landsat input scenes that are temporally as close as possible to the prediction dates to minimize errors in the ESTARFM prediction [3; 32]. Our results confirmed this assumption and additionally showed the value of Landsat input scenes from the same season and that abrupt events such as fires or floods between input and prediction date can decrease the accuracy. In order to avoid large gaps between input and prediction dates, we included an automatic cloud gap filling of Landsat scenes in the ESTARFM framework to make best use of scenes with a partial cloud cover without producing gaps in the output of the fused time series. With this filling approach, a total number of 30 Landsat input scenes from the year 2014 could be used for the study area instead of only the 13 cloud-free scenes. Thus, Landsat scenes from different seasons could be added to the input dataset, significantly improving the prediction quality.  
It should be noted that when using the original STARFM [2] with two shoulder input pairs, cloud gaps in the fusion output can also be avoided as long as at least one of the shoulder observations is cloud free. However, gaps remain for those parts of the image where both shoulder datasets are clouded.  
The different Landsat tiles have thus far been treated individually in the processing of the ESTARFM framework. In order to increase data availability for an application in higher latitudes, where overlapping areas between Landsat paths are considerably bigger than in the study region of this paper, it would be interesting to use the tiles of different paths jointly in the ESTARFM framework. This could be part of future improvements of the framework.
# Single Cell Chemotactic Responses of
## 3 Results and Discussion  
Chemotaxis aids _H. pylori_ in nutrient acquisition, allowing it to persist within the mammalian host [15]. Urea is synthesized in the liver, circulated in the bloodstream, and diffuses into the gastric lumen via capillaries, which generates concentration gradients of urea across the mucous layer [19]. In this study, we conducted two types of chemotaxis assays: spatial assays observing bacterial migration to a particular area in response to a urea gradient and temporal assays monitoring the flagellar switching of a single cell. The most popular assay to investigate the migration of _H. pylori_ is the soft-agar method [28], which requires rich media and serum to support growth and does not evaluate the effect of a single chemotactic agent; this method assesses only general chemotactic ability [15]. Using our microfluidic device, a concentration gradient of urea was generated in the microchannel [27], allowing us to examine the response of _H. pylori_ to a single chemoeffector. Another advantage of our microfluidic device is that the small scale of the observation area allows for the surveillance of the subtle movements of a single cell at high resolution. We thus investigated the chemotactic activities of a single bacterium, including its complex bacterial swimming behavior and the frequency and type of directional changes or tumbles.  
In the viewing chamber, it is required to have no overall flow other than the self-propelled motion of the cells. In our study, we evaluated that the cells were in such an environment by analyzing the forward migration index of the swimming cells in the chamber. Table S1 shows an example of the forward migration index of _H. pylori_ cells in an individual test for baseline, 10 mM and 100 mM urea. The low values indicated that no movement caused by the environment and any movement recorded was caused by the cells' own propulsion.