esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Temperature Differences between COSMIC and Radiosonde Data  
At each station, the bias between the coupled COSMIC and radiosonde temperatures is calculated at all height levels and then averaged. Figure 2 presents the vertically averaged COSMIC temperature biases compared to radiosonde observations at 120 stations in China. The number of radiosonde stations for each bias class is listed in parentheses. In general, the COSMIC temperature bias is negative in northern China and positive in southern China. Moreover, the large temperature differences mostly appear in western China, which is a high-altitude region compared to eastern China. Among the 120 stations, 118 stations (98%) have biases with magnitude less than 1.0 K. The maximum COSMIC temperature bias of 1.03 K occurs at a station on the eastern QTP, and the minimum bias of \(-\)1.45 K occurs at a station on the southern QTP. On average, the COSMIC temperature bias compared to radiosonde observations in China is \(-\)0.1 K. Additionally, a positive correlation is found between the absolute temperature bias and station altitude, with a correlation coefficient of 0.3830 at a significance level of greater than 99%. This indicates that the absolute temperature bias is positively correlated with station altitude in statistics in a certainway, but not on the whole, because many factors may impact on the temperature bias between COSMIC and radiosonde.  
The temperature RMSEs between COSMIC and radiosonde data in China are shown in Figure 3. Overall, 36 stations (30%) have an RMSE less than 4 K, and 65 stations (54%) have an RMSE between 4 K and 6 K. The maximum temperature RMSE occurs at a station in the southern QTP with a value of 10.64 K. In general, the temperature RMSE is larger in western China than in eastern China, especially in the QTP. The average temperature RMSE between COSMIC and radiosonde data in China is 4.84 K. Additionally, a positive correlation is observed between the temperature RMSE and station altitude, with a correlation coefficient of 0.2414 at a significance level of greater than 99%. This implies that, as with the temperature bias, the temperature RMSE between COSMIC and radiosonde is also impacted by station altitude in a certain way.  
Figure 3: The temperature RMSEs between COSMIC and radiosonde data in China. The number of radiosonde stations for each RMSE class is listed in parentheses.  
Figure 2: The COSMIC temperature biases compared to radiosonde observations in China. The red dots are positive biases, and the blue dots are negative biases. The number of radiosonde stations for each bias class is listed in parentheses.
# Experimental Seaborne Passive Radar
## 4 Results of Moving Target Detection
### Cruse Airplane THY9 (DAB Illumination)  
A more in-depth analysis of the acquired signals shows that it is also possible to detect cruise airplanes flying accidentally near the PCL receiver installed on the military ship. Figure 22 shows a situation with the ship sailing northeast slowly and the THY9 [27] cruise airplane (Boeing B789 Dreamliner) flying northwest, ca. 10 km from the ship. The DAB transmitter (218.64 MHz) was selected for illumination, and the vertically polarized antennas with the front reference antenna were employed. Detection results of this cruise flight are shown in Figure 23, with a clear dot traveling in a bistatic \(R=8\) km area and decreasing bistatic velocity. During a period of 77... 86 s, the echo from the airplane is lost in the clutter area due to low bistatic velocity. This moment correlates very well with the THY9 mark crossing on the transmitter-receiver baseline on the map, as shown in Figure 22.  
Figure 21: Detection results of the PILATUS PC-12 and two F-16 jet fighters (scene from Figure 20).
## 2 Summary of the Paper
### Wideband (Multi-GHz) Spectrum Sensing  
There are two classes of cognitive radios: (1) narrowband cognitive radio; (2) wideband cognitive radio. This classification is purely for the convenience of description in the context of spectrum sensing. Two motivating examples are given to illustrate this fundamental difference: (1) 6 MHz cognitive radio for unlicensed digital TV band--IEEE 802.22 [33]; (2) wideband tunable (over several GHz bandwidth) cognitive radio--evolution beyond 802.22.  
The narrowband cognitive radio--band specific (TV band)--is much easier for spectrum sensing. It can exploit _a prior_ information about one particular system configuration, such as air interface, modulation format, symbol rate, pilot, etc. Its wideband counterpart generally has not so much information to use. Spectrum is like disk space, the more one has, the more applications that will use it [3]. The front-end, including A/D, filters, wideband and real-time sensing, is the primary challenge for the wideband case. Spectrum sensing for the physical layer design is another challenge [3]. The wideband CR is the primary focus of this proposed research. We will systematically evaluate the 802.22 technologies of the narrowband CR [3; 34; 35; 36; 37; 38] before their use in the wideband CR.  
Spectrum sensing is one of the major challenges for cognitive radio, since the signal is weak to detect and protect (at low SNRs) [36; 39]. For example, TV broadcasts have set a stringent limit for the digital TV signals to be reliably detected (probability of detection \(>\) 90% with probability of false alarm \(>\) 10%) at a signal strength of 116 dBm translating to roughly -21 dB of signal-to-noise ratio (SNR) based on the receiver noise figure (NF) of around 11 dB and the use of omnidirectional antenna for spectrum sensing [6].
## 2 Additively Manufactured Digital Spare Parts as a Concept
From an OEM's perspective, there are several spare part distribution scenarios possible: Either relevant components are manufactured in-house, using conventional (CM) or AM machines, or parts are ordered from external suppliers (i.e., assembly or component supplier). If end-customers order parts directly from a service provider, the OEM is potentially bypassed (dotted black lines) in the value chain. In this example supply chain network structure, the end customers in tier 3 can demand spare parts (dotted black arrow) directly from an AM component supplier of tier 2, where the component is manufactured using the AM processes and consequently shipped to end customers in tier 3. This modification can have positive effects for customers (e.g., shorter delivery times) and negative effects (e.g., product piracy) for OEMs. Overall, several crucial aspects need to be considered in this approach:  
* All spare parts have to exist in a digital file format, extended by additional part relevant information including raw material specifications (e.g., type, shape) and production parameter settings (e.g., irradiation schemata in PBF, layer thickness) to ensure repeatable high-quality AM part production. Figure 1: Supply chain network structure showing demands and potential distribution scenarios. Adapted and modified from [PERSON] et al. Adapted from [34]. CM: conventional manufacturing; AM: additive manufacturing; ERP: enterprise resource planning. * AM and related post processing should be located at the same production site (consider design modifications for clamps and fixtures); otherwise, an additional tier for post-processing must be implemented. * Increased liability risks associated with distributed manufacturing may occur. * Costs to maintain additional AM-related supply chains. * Potential to strengthen the supply chain by increasing the number of additional suppliers. To expand the existing body of knowledge regarding additively manufactured spare parts and to further analyze the current demand of spare parts and to outline the existing potential of additively manufactured DSPs, a survey was conducted with respondents from the industry. Numerous surveys, as a state-of-the-art exploratory research methodology in the field of AM, have been conducted previously to evaluate challenges and opportunities [35; 36]. Surveys are the most commonly used research approach in operations management to test specific theories [37]. However, no survey studies related to the DSP concept were found in the literature. This survey evaluates the current technical and economic feasibility of the DSP concept as well as related opportunities and challenges. In Section 3, the methodology applied in this study is outlined, followed by results of the survey in Section 4. Finally, in Section 5, conclusions are drawn, including an assessment of the companies' perception of the DSP concept and an evaluation of the readiness level of the DSP concept.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 2 Simulation and Experiment
### Preparation of Optical Fiber SPR Sensing Probe
After a series of experiments, it was verified that the thicknesses of both Au films met this requirement in a simultaneous sputtering process, by only controlling the coating time and coating current. To investigate the coating rule and to obtain a good SPR spectrum, several optical fiber sensing probes with different coating currents and coating times were fabricated and then their SPR spectra were measured by immersing these probes in pure water with a fixed refractive index of 1.3321. The lengths of the SMF in these sensing probes were all controlled to be 10 mm. Figure 3 shows the reflection spectra of the fiber sensor after Au was plated in different coating current conditions. These were a coating time of 90 s, and coating currents were selected as 2 mA, 4 mA, 6 mA, and 8 mA, respectively; the experimental results are shown in Figure 2a. It can be seen that when the current was selected to be 6 mA, the obtained reflection spectrum has the largest resonance depth  
Figure 2: Variation of the simulated SPR spectra along with the changes of the gold film length (**a**), gold film thickness (**b**), and surrounding refractive index (**c**). and the minimum half width. On the other hand, the coating current was 6 mA, and the coating time was selected to be 60 s, 75 s, 90 s, and 105 s, respectively; these experimental results are shown in Figure 3b. It can be seen that when the coating time was 90 s, the obtained reflection spectrum has the largest resonance depth and the minimum half width. According to the above experimental results, the optimal experimental parameters for the Au plating film were 6 mA for the coating current and 90 s for the coating time. By comparing the optimum experiment result shown in Figure 3b (the blue line corresponds to a length of 10 mm, refractive index of 1.3321, coating current of 6 mA, and coating time of 90 s) and the optimum simulation result shown in Figure 2c (the red line corresponds to an Au length of 10 mm, refractive index of 1.33, Au thickness of 40 nm), we can suppose that the thickness of the Au film under the optimal experimental parameters is around 40 nm. Figure 4 shows micrographs of the fiber sensing probe before and after Au coating at this optimal parameter, which demonstrated that the Au film is coated well on the surface of the fiber by using the above method.
# Investigation of the Static Characteristics of a Geogrid-Reinforced Embankment
## 3 Monitoring Data Analysis
### Numerical Simulation of Displacement Analysis  
The software analysis stage is the construction stage, and the original position is reset before the analysis. Because the thickness of the soil filling is 61 layers, the number of numerical simulation analyses is 61. After the numerical simulation analysis, the model is analyzed. Since the construction stage is not affected by time variables, data are obtained through the software extraction results. The location of extracted data in the model is basically the same as that of the actual monitoring point, and the extracted data are compared with the actual observation data.  
Figure 11 shows vertical and horizontal displacement clouds. It can be seen from the vertical displacement cloud diagram in Figure 11a that the maximum vertical displacement is at the top of the structure. Because the vertical displacement of each layer of fill is accumulated under the gravity of the structure after construction, the maximum vertical displacement at the top is 0.0139 m, the middle vertical displacement is 0.0102 m, the minimum vertical displacement at the bottom is 0.0035 m, and the top vertical displacement is 0.046% of the wall height. It can be seen from the displacement cloud in Figure 11b that the horizontal displacement ranges from 0.0023 m at monitoring point \(G\), 0.0017 m at monitoring point \(F\), and 0.0016 m at monitoring point \(E\) due to the maximum lateral earth pressure and gravity at the bottom. It can be seen that the horizontal displacement is smaller than the vertical displacement.  
Figure 12 shows the horizontal and vertical displacement values of the simulation and actual monitoring. It is found that the measured vertical displacement and horizontal displacement are both greater than the measured monitoring displacement. This is because the actual engineering of the filled soil is anisotropic and heterogenous, and the fill in the numerical analysis is an ideal isotropic, uniform, numerical simulation of more accumulated disposable displacement. In general, the simulated displacement values are within the allowable displacement range [36]. From the point of view of numerical simulation, the  
Figure 10: Vertical and horizontal earth pressure curves: (**a-1**) the first group of vertical earth pressure; (**a-2**) the first group of horizontal earth pressure; (**b-1**) the second group of vertical earth pressure; (**b-2**) the second group of horizontal earth pressure.  
displacement of the soil embankment on the same height of plane is basically close, and the left and right sides are basically symmetric, which is slightly different from the actual monitoring, but within the allowable range.
# Research on the Causes and Transmission Mechanisms of Railway Engineering Safety Risks
###### Abstract
According to the China Railway Yearbook (2021) [10], there were 138 responsible train accidents, including occupational injuries, in various engineering departments of the China Railway Group in 2020, including 3 major accidents, 8 general Class B accidents, 13 general Class C accidents, and 114 general Class D accidents. The total number of responsible accidents in 2020 increased by 21 compared to 2019. Accidents occur repeatedly, and any negligence in the management and operation process can easily have severe consequences. Therefore, it is crucial to analyze the accident data, explore the internal mechanisms, potential laws, leading factors, and transmission mechanisms of accidents, and apply these experiences and laws to actual operation management, to improve the overall safety level of the railway operation management system. At the same time, considering the key defects, common deficiencies, frequent emergencies, equipment prone to malfunctions, and operational processes prone to problems, corresponding accident prevention measures should be taken, and efficient response strategies and emergency management measures should be implemented to reduce the occurrence of accidents and risk events. Thus, research in this area is of great significance for improving the overall level of safety management, reducing the number of accidents, ensuring personal and property safety, enhancing passenger satisfaction, and improving the efficiency of train operation organization [11]. Many existing studies have adopted a single method to analyze safety risks in a single scenario using a single indicator, without considering the current situation of various risk-related factors in a railway system, optimization under multi-objective conditions, and coordination problems with multiple different dimensions of demand [12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. There has been relatively little research on optimizing the indicator combinations, and some of the existing methods involve subjective evaluations, leaving them unable to determine the logical relationship between indicators and outputs. To address the aforementioned shortcomings, this paper improves and optimizes the index screening analysis method, and it considers risk correlation factors and optimization under multi-objective conditions. First, the multi-objective particle swarm optimization algorithm is used to determine key risk factors, bringing the indicator screening more in line with the requirements of practical applications. Then, the Bayesian networks are selected, and their structure is optimized to analyze the propagation diagnosis and probability of key risk indicators and obtain the causal logic chain that produces accident results. Finally, the combination of the selected indicators and Bayesian networks is used to obtain more accurate results than those provided by the existing research and fill the gap in research on railway safety risks in risk transmission mechanisms.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Interference Mitigation Methods in Prohibited Zone
#### 4.4.2 Angle Shutoff Method  
The angle shutoff method is only suitable for beamforming antennas. It needs to be carried out separately for the horizontal and vertical prohibited zones. In the horizontal prohibited zone, assuming that the main lobe of the beamforming antenna can sweep through a 360\({}^{\circ}\) range horizontally, the horizontal distance between the base station and the the aircraft is the main factor that affects angle shutoff. In the vertical prohibited zone, the horizontal distances between the base station and the aircraft are 1 km, 3 km, and 5 km. Then, the relationship between the angle shutoff and \(\varphi_{v}\in(0,90^{\circ})\) is analyzed. Figure 10 shows the definition of the horizontal distance (\(d\)) and the definition of \(\varphi_{v}\). The following assumptions are made:  
(1) The horizontal distance between the aircraft and BS varies between 0 and 10 km;  
(2) The steering beam of the BS varies between 0 and 360\({}^{\circ}\) horizontally;  
Figure 23: Tx power control in vertical prohibited zone for different scenarios.  
All possible directions of the beamforming antenna of the base station need to be analyzed;  
All angles of interference at different altitudes AGL need to be analyzed.  
Figure 24 illustrates the relationship between the angle shutoff and the horizontal distance (\(d\)) under three altitude AGL conditions. The angle shutoff decreases gradually as the distance increases. For \(AGL=200\) ft, the angle shutoff may reach up to 270\({}^{\circ}\) at close range, while it decreases to 25\({}^{\circ}\) at a distance of 1 km. For \(AGL=1000\) ft, the angle shutoff gradually decreases from around 150\({}^{\circ}\). Similarly, for \(AGL=2000\) ft, the angle shutoff gradually decreases from around 200\({}^{\circ}\).  
Figure 25a-c illustrate the relationship between the angle shutoff and the \(\varphi_{v}\) at different horizontal distances. At the three distances, the angle shutoff increases with increases in \(\varphi_{v}\). The greatest angle shutoff is at \(d=1\) km, \(AGL=200\) ft, ranging from 40\({}^{\circ}\) to 140\({}^{\circ}\). At \(d=3\) km, the range of the angle shutoff varies from 11\({}^{\circ}\) to 93\({}^{\circ}\), while at \(d=5\) km, the angle shutoff varies from 7\({}^{\circ}\) to 45\({}^{\circ}\). The larger the \(\varphi_{v}\), the stronger the received interference. This is because of the smaller angle between the aircraft and the main lobe of the base station antenna.
# SteXMeC: A Student eXperience Evaluation Methodology with Cultural Aspects
## 6 Student eXperience Evaluation Methodology Validation
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Utility**} & \multicolumn{2}{c}{**Clarity**} & \multicolumn{2}{c}{**Ease of Use**} & \multicolumn{2}{c}{**Lack of Detail**} \\
**Stage** & **AVG** & **SD** & **AVG** & **SD** & **AVG** & **SD** & **AVG** & **SD** \\ \hline S0: Pre-Planning & 4.71 & 0.45 & 4.00 & 1.07 & 3.50 & 0.73 & 2.29 & 1.22 \\ S0.1: Data Collection & 4.86 & 0.35 & 4.21 & 0.86 & 3.36 & 0.89 & 2.07 & 1.16 \\ S0.2: Preliminary Mapping of the & 4.36 & 1.04 & 3.79 & 1.32 & 3.07 & 1.03 & 2.36 & 1.11 \\ Student Journey & 4.79 & 0.41 & 3.64 & 1.34 & 3.50 & 1.05 & 2.36 & 1.34 \\ S0.3: Preliminary Method Selection & 4.86 & 0.35 & 4.07 & 0.88 & 3.50 & 0.91 & 2.14 & 1.06 \\ S1.1: Method Execution Planning & 4.79 & 0.41 & 4.21 & 0.86 & 3.43 & 0.98 & 2.14 & 1.06 \\ S1.2: Evaluator Selection & 4.79 & 0.41 & 4.29 & 0.88 & 3.79 & 0.94 & 2.21 & 1.15 \\ S1.3: Participant Selection & 4.79 & 0.41 & 4.07 & 1.03 & 3.71 & 0.96 & 2.29 & 1.16 \\ S1.4: Experiment Design & 4.86 & 0.35 & 4.00 & 1.07 & 3.29 & 0.96 & 2.21 & 1.21 \\ \hline S2: Execution & 4.79 & 0.41 & 4.07 & 1.10 & 3.57 & 0.82 & 2.29 & 1.28 \\ S2.1: Preliminary Evaluation & 4.79 & 0.41 & 4.07 & 1.03 & 3.79 & 0.94 & 2.21 & 1.15 \\ S2.2: Inspections & 4.64 & 0.61 & 4.07 & 1.03 & 3.57 & 0.90 & 2.21 & 1.15 \\ S2.3: Student tests & 4.93 & 0.26 & 3.93 & 1.10 & 3.71 & 0.80 & 2.14 & 1.06 \\ \hline S3: Results Analysis & 4.93 & 0.26 & 4.07 & 1.16 & 3.36 & 0.97 & 2.21 & 1.26 \\ S3.1: Qualitative Analysis & 4.93 & 0.26 & 4.00 & 1.13 & 3.14 & 0.99 & 2.21 & 1.26 \\ S3.2: Quantitative Analysis & 4.93 & 0.26 & 4.07 & 1.16 & 3.50 & 0.91 & 2.14 & 1.19 \\ \hline S4: Result Interpretation & 4.57 & 1.05 & 3.86 & 1.30 & 3.07 & 0.88 & 2.43 & 1.45 \\ \hline Average per factor & 4.78 & & 4.03 & & 3.46 & & 2.24 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Expert rankings for each stage and substage according to the defined attributes. Considering the Ease of Use scores and the relatively lower ratings for S4--Result Interpretation--combined with the feedback provided by the evaluators (Section 6.3), we have decided to introduce templates and guidance documents to facilitate the efficient application of the methodology. Furthermore, we will expand the explanation of Stage S4 to ensure better clarity and ease of understanding for future users.
## 4 Results
### Validation of Landslide Models  
Currently, the ROC and AUC have been widely applied to validate the performance of determined landslide susceptibility models [64; 69; 105]. The ROC curve can be generated by plotting the false positive rate (100-specificity) in the x-axis versus the sensitivity in the y-axis [71]. The area under the ROC curve (AUC) is an indicator of the global summary measure of the performance of a model [106; 107; 108]. In the present study, to assess the validation of the WoE, WoE-LR, and WoE-RF models, the ROC curves of three models with training and validation datasets are described in Figures 8 and 9.  
Figure 6: Mean decrease accuracy and mean decrease Gini.  
Figure 8: ROC curves using the training dataset.  
Figure 7: Landslide susceptibility map using the WoE-RF model.  
In the case of the training dataset, the WoE-RF model had the best performance with the highest AUC value of 0.802, while the AUC values of the WoE model and WoE-LR model were 0.720 and 0.773, respectively. Meanwhile, the WoE-RF model had the lowest standard error (0.0275) and a 95% confidence interval of 0.729-0.829. Thus, the WoE-RF and WoE-LR models can improve the accuracy of the traditional WoE model in this study, and the WoE-RF model showed a relatively better performance.  
In the case of the validation data, it can be seen that the AUC values of the various models decreased slightly when compared with the training dataset. The AUC values were 0.695, 0.763, and 0.782 for the WoE model, WoE-LR model, and WoE-RF model, respectively. Similarly, the lowest standard error was 0.0430 for the WoE-RF model, followed by the WoE-LR model (0.0440), and the WoE model (0.0484). The detailed results demonstrated that the WoE-RF model had a prominent prediction capacity on landslide susceptibility mapping.