esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 3 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Key Technology Used** & **Area of Research** & **Gaps Identified** &
\begin{tabular}{c} **Test Equipment/Simulation** \\ **Used** \\ \end{tabular} \\ \hline
[14] & \begin{tabular}{c} 5G Interference \\ Analysis \\ \end{tabular} & \begin{tabular}{c} Impact of 5G on \\ aviation altimeters \\ \end{tabular} & \
\begin{tabular}{c} Insufficient real-world \\ implementation data \\ \end{tabular} &
\begin{tabular}{c} Airworthiness Directives, EIRP \\ simulation \\ \end{tabular} \\ \hline
[15] & \begin{tabular}{c} Mathematical \\ Modeling \\ \end{tabular} & \begin{tabular}{c} Compatibility of 5G \\ systems with radar \\ altimeters \\ \end{tabular} & \
\begin{tabular}{c} Need for in-depth field \\ validation \\ \end{tabular} &
\begin{tabular}{c} Monte Carlo Simulations \\ \end{tabular} \\ \hline
[16] & \begin{tabular}{c} Interference Scenario \\ Analysis \\ \end{tabular} & \begin{tabular}{c} Compatibility between \\ 5G and aeronautical \\ systems \\ \end{tabular} & \
\begin{tabular}{c} Challenges in \\ establishing global \\ standards \\ \end{tabular} &
\begin{tabular}{c} Simulation tools for \\ operational scenarios \\ \end{tabular} \\ \hline
[17] & \begin{tabular}{c} Time Difference of \\ Arrival (TDOA) \\ \end{tabular} & \begin{tabular}{c} Locating civil aviation \\ RFI sources \\ \end{tabular} & \
\begin{tabular}{c} Limited deployment in \\ diverse environments \\ \end{tabular} &
\begin{tabular}{c} UAV-based synchronization \\ models \\ \end{tabular} \\ \hline
[18] & \begin{tabular}{c} EMC Standards \\ Evaluation \\ \end{tabular} & \
\begin{tabular}{c} RFI’s impact on \\ integrated circuits \\ \end{tabular} &
\begin{tabular}{c} Integrated \\ immunity-emission \\ testing methods \\ \end{tabular} & EMC spectrum analyzers \\ \hline
[19] & \begin{tabular}{c} PCB Design for EMI \\ Mitigation \\ \end{tabular} & \begin{tabular}{c} EMC in electronic \\ systems \\ \end{tabular} & \
\begin{tabular}{c} Integration of \\ PCB-based EMI \\ mitigation techniques \\ \end{tabular} &
\begin{tabular}{c} Simulation tools for EMI \\ shielding analysis \\ \end{tabular} \\ \hline
[20] & \begin{tabular}{c} Out-of-Band \\ Interference Mitigation \\ \end{tabular} & \
\begin{tabular}{c} Impact of interference \\ on GNSS receivers \\ \end{tabular} &
\begin{tabular}{c} Broader scenarios for \\ GNSS testing \\ \end{tabular} & GNSS signal simulators \\ \hline
[21] & \begin{tabular}{c} UAV Platforms for RFI \\ Monitoring \\ \end{tabular} & \begin{tabular}{c} Monitoring RFI in \\ aviation using UAVs \\ \end{tabular} & \
\begin{tabular}{c} Challenges in UAV \\ airworthiness \\ compliance \\ \end{tabular} &
\begin{tabular}{c} Angle of Arrival (AOA) \\ algorithms \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison table of various research papers and gap identification.
## 3 Results  
### Selection of the Optimal Standardized Drought Index  
As shown in Figure 2a,b, pixel-wise Pearson and Spearman correlation analyses were conducted between four drought indices at different time scales and STI-1. Among these indices, SPEI-3 exhibited the strongest negative correlation with STI-1 in both correlation methods. Therefore, this study selected SPEI-3 as the key drought indicator for CDHEs. As shown in Figure S1a-b, SPEI-3 and STI-1 demonstrated a significant negative correlation (\(p<0.05\)) across all rubber plantation pixels. Spatially, the negative correlation between SPEI-3 and STI-1 in the rubber plantations of northwestern Hainan Island was significantly stronger than in other regions of the island.
Specifically, the heading angle is unobservable during straight-line segments, but it becomes fully observable with the availability of satellite velocity heading angle. Additionally, when the train enters a curved segment, the heading angle gains observability, and the observability strengthens with an increased curvature. Therefore, in a train-positioning system primarily based on inertial navigation, it is necessary to estimate the inertial navigation attitude misalignment angle before satellite navigation fails. One feasible approach is to estimate the attitude misalignment angle before entering a signal-unavailable track segment such as a tunnel. The following measures can be adopted: Firstly, introducing a new responder technology, namely, the attitude position responder, where the solution of a virtual attitude responder has been proposed by France's ixblue company. Secondly, using GNSS velocity heading information for heading correction. The analysis of this strategy will be elaborated below. According to the results of [34], the train tracking, stopping, and starting in the zone of the train's longitudinal accuracy requirements vary between 50 m, 13 m, 5 m, and 0.5 m. This paper adopts the SINS/GNSS integrated navigation framework, which can meet the requirements of the train-positioning accuracy under the circumstance of the unavailability of GNSS. When GNSS is unavailable, it is derived from the observability analysis, after the train travels a distance in a straight line at a constant speed, with the assistance of position measurement, the heading angle is still not observable, but it can be analyzed by simulation depending on which grade of inertial guidance device accuracy is used to meet the final train-positioning accuracy requirements, which is an important basis for the allocation of error margins in the design of the combined navigation system and the selection of the device accuracy. The paper is divided into four sections. Section 1 introduces background information, the problem, and a literature review related to this issue. Section 2 elaborates on the detailed construction process of the system equations, measurement equations, and observability analysis in the proposed method. Section 3 involves the simulation and analysis of the theoretical results. Section 4 summarizes the main findings of the entire paper.
## 3 Methodology  
### Monitoring Method of Glacier Flow Velocity in Wide-Area POT  
SAR data store the intensity information and phase information returned by the radar from the surface to the sensor in the form of complex numbers. Accordingly, the existing POT methods can be divided into interferometric phase offset tracking [26; 27] and intensity offset tracking [28] to obtain the pixel offset measurements within the image. The first type of POT method has a limited monitoring effect in the fast-moving glacier area due to the spatial-temporal decoherence of the glacier surface. The POT method based on intensity information is used to carry out the wide-area glacier flow velocity monitoring. The intensity offset tracking technique involves accurately registering two SAR images to obtain sub-pixel-level registration offsets. This information is then used to estimate the displacement of individual pixels in both the satellite azimuth and range directions. The core concept underlying this technique is the normalized cross-correlation algorithm.
## 3 Results
### Evaluation of Tracking Reliability
#### 3.3.1 Test Site 1: Tunnel  
The monitored section of the cable in the tunnel is from 13,600 m to 25,900 m relative distance from the interrogator device; therefore, we observe a long stretch of cable. This analysis shows that we reach 98% of correct tracks, respectively, for the two evaluated cable lengths. In this test site, no artifact tracks due to non-train-related noise was observed, meaning that the two percent incorrect tracks are attributed to lost tracks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Train ID** & **Error Signal 1 [m]** & **Error Signal 2 [m]** \\ \hline
23468 & 49.64 & 44.20 \\
29515 & 48.96 & 1.36 \\
2330 & 51.68 & 38.76 \\
73 & 79.56 & 37.40 \\
23488 & 71.40 & 23.12 \\
29535 & 44.88 & 17.00 \\
23508 & 31.28 & 86.36 \\
29555 & 31.28 & 19.72 \\
103 & 64.60 & 57.12 \\
90093 & 43.52 & 39.44 \\
23528 & 49.64 & 64.60 \\
29575 & 2.04 & 61.20 \\ \hline Average & 47.37 & 40.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarized performance of tracking algorithm for the two two short term recordings for single trains. Given that the ground truth and the tracking are both accurate down to one second, the evaluation is accurate down to 2 s. Considering the train speed, which is around 30–40 m/s, the errors are in the expected range.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Test Site 1** & **Test Site 2** \\ \hline Cable stretch observed (meters) & 13,600–25,900 & 0–8840 \\ Cable stretch observed (segments) & 20,000–38,000 & 0–13,000 \\ Number of hours observed & 477 & 538 \\ Cable installation method & Cable trench in tunnel & Cable attached to rail on open track \\ Number of tracks & 2174 & 1071 \\ Number of correct tracks & 2141 & 1059 \\ \% of correct trains & 98\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarized performance of tracking algorithm for the two monitored test sites.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Strengthening Authentication Scheme
#### 4.9.2 Two-Factor Authentication  
The idea of obligation to raise an alarm, presented in Section 4.9.1, could potentially be extended to the physical layer as well. In [18], a cooperative authentication protocol was presented in which a set of trusted nodes support a sink node in authenticating transmission from other nodes. The trusted nodes measure physical features of the transmissions, e.g., received power or relative delay. The authors of [18] indicated that \"due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one\".  
In case of the multicast transmission discussed here, the full set of receivers \(\mathcal{R}\) can be treated as trusted nodes. If they detect anomalies in the physical characteristics of the signal transmitted by the (alleged) sender, then they are obligated to send an alarm to the sender via one-to-one channels. Assuming that only a minority of the nodes will be compromised at any time, the feedback from the nodes could be convincing for the sender.  
Note that, if the set \(C\) of the captured nodes does not have access to all the keys \(K_{i}\) needed to assemble the tag \(T_{1}\) defined by (11), then there is a large probability that the alarm concerning the physical layer will be accompanied by the alarm discussed in Section 4.9.1.  
As can be seen, by taking into account the physical layer it is possible to can obtain an analog of the second factor authentication. The applicability of this mechanism depends on the mobility of the nodes, and in case of mobile nodes on how frequently the messages are sent by the sender. Thus, each case seems to require individual treatment.
## 3 Results and Discussions  
The height of the smoke is thought to be a crucial factor in passenger evacuation. The safe smoke height was described in [37]:  
\[H_{\text{s}}=1.6+0.1H, \tag{13}\]  
where 1.6 m represents the average height of passengers, and \(H\) is 3.5 m in the physical model established in this study. Therefore, \(H_{\text{s}}\) was determined to be 1.95 m. When the actual height of the smoke layer is higher than \(H_{\text{s}}\), passengers can safely evacuate the subway station [38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Passageway**} & **Simulation Value of** & **Test Value of Wind** & \multirow{2}{*}{**Mean Relative Error**} \\  & **Wind Speed (m/s)** & & **Speed (m/s)** \\ \hline A & 0.51 & 0.49 \(\pm\) 0.05 & 4.08\% \\ C & 0.59 & 0.56 \(\pm\) 0.06 & 5.36\% \\ D & 0.38 & 0.35 \(\pm\) 0.03 & 8.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between simulation and test value.  
Figure 6: Schematic of the air velocity points at passageways.  
[PERSON] and [PERSON] [39] demonstrated that CO\({}_{2}\) in flames should be classified as a dangerous gas. The CO\({}_{2}\) volume fraction in natural air is typically 0.03-0.04% [40]. However, when the inhaled volume fraction of CO\({}_{2}\) exceeds 1.0%, a person's respiratory volume begins to rise; if the volume fraction of CO\({}_{2}\) rises further, it might cause dyspnea, headache, dizziness, coma, and death [41]. As a result, the CO\({}_{2}\) volume fraction limit was set at 1.0%, and the location in the station hall where the CO\({}_{2}\) volume fraction exceeded 1.0% at 1.95 m was designated as the smoke-diffusion area.  
According to ref. [33], when smoke is emitted from the station hall, it should be prevented from entering neighboring places, such as the passageways of entrances and exits. Therefore, the smoke control system's role is to prevent smoke from spreading into the passageways, to decrease the longitudinal diffusion distance of smoke in the station hall as much as feasible, and to establish an evacuation atmosphere.  
The impacts of the external wind direction and speed were analyzed using the findings of the working conditions listed in Table 5.
## 3 RIM Formulation Summary, Discussion, and Prospects
### RIM Formulation Summary
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **Solid** & **Liquid** & **References** \\ \hline
1. Displacement field of the soil around the laterally loaded pile & Glass sand & Mixing n-dodecane and No. 15 white oil with a mass ratio of 1:12 & [54] \\
2. Observation of square anchor rotation & Fumed silica & A mix of 70\% paraffin and 30\% white spirit by volume & [57] \\
3. Geotechnical properties & A synthetic smectite (LAPONITE RD®) & Distilled water & [58] \\
4. Internal displacement field during pile-soil interaction & Fused quartz & Mixture of Norpar 12 and white mineral oil & [60] \\
5. Sand surrogate for use in physical modeling & Fused quartz & Waterbased sucrose solution & [59] \\
6. Surrogate soil for geotechnical laboratory modelling & Fused quartz & A mixture of hydrophobic funnel silica powder and mineral oil & [55] \\
7. Transparent cemented soil for physical modeling & Fused quartz (purchased Co., Ltd.) & A mixture of n-dodecane and 15\% white oil at a mass ratio of 1:3.5 to 1:8 (both purchased from Guangdong Wengjiang Chemical Reagent Co., Ltd.) & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geotechnical engineering: solid soil particle-groundwater interaction. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **Solid** & **Liquid** & **References** \\ \hline
1. Surfactant flushing in remediation of contaminated soils & Aquabeads (produced by Kuraray Chemical Co.) & White mineral oil and motor oil & [78] \\
2. Remediation of soil contamination & Amorphous silicon powder & Mineral oil solution & [63] \\
3. NAPL 3D distribution & Fused quartz & A mineral oil blend; low color sucrose (LCS) & [62] \\
4. Grout permeation & fused silica & Calcium bromide solution & [61] \\
5. Groundwater contamination by non-aqueous phase liquids & Aquabeads & Mineral oil and motor oil & [64] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Environmental engineering: discrete particle-flow interaction. Table 4 also illustrates some applications of the RIM method in the fields of biology and medicine. The topics are mainly observations at the cell membrane level in biology and blood flow dynamics in medicine. Excellent biocompatibility is often required in the materials used for RIM formulation.
# Structural Health Monitoring of 2D Plane Structures
## 2 Structural System Identification of Structures with 2D Elements by Observability Techniques
### Comparison of the Application of OM to the 1D and 2D Element Structures  
To illustrate the effect of changing the type of element into the observability method, Table 3 is presented. This table compares the application of 1D (2-noded beam element) and 2D (6-noded triangular element, LST) element structures and includes the following information: (1) type of element, (2) degrees of freedom per element, (3) unknown variables, (4) products of unknown variables, and (5) size of the stiffness matrix and, (6) type of analysis of the null space. The information of the 2D elements is presented before and after the change in variables described in Section 1.  
Figure 1: Flow chart of the algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1D OM** & \multicolumn{2}{c}{**2D OM**} \\ \hline  & \multicolumn{3}{c}{**Before Change of Var.**} & \multicolumn{2}{c}{**After Change of Var.**} \\ \hline Type of element & 2-noded beam element & 6-noded triangular element & 6-noded triangular element \\ Degrees of freedom per element & 6 & 12 & 12 \\ Number of unknows & 2 & 2 & 4 \\ Unknown variables per element & \(E_{i}A_{i}\), \(E_{i}I_{i}\) & \(E_{i}\) and \(\hat{\theta}_{i}\) & \(E_{i}\), \(\psi_{i}\), \(N\psi_{i}^{n}\), \(N\psi_{i}^{n}\) \\  & & \((E_{i}h_{i}/\hat{\theta}_{i}+1)\) & \(E_{i}h_{i}\). \(N\psi_{i}^{n}\) \\ Products of unknowns & \((E_{i}A_{i}/L_{i})\)_and_\((E_{i}I_{i}/L_{i})\) & \((E_{i}h_{i}/2\hat{\theta}_{i}^{2}+\hat{\theta}_{i}-1)\), & \(E_{i}h_{i}\). \(N\psi_{i}^{n}\). \(N\psi_{i}^{n}\), \\  & & \((E_{i}h_{i}\hat{\theta}_{i}/2\hat{\theta}_{i}^{2}+\hat{\theta}_{i}-1)\) & \(E_{i}h_{i}\). \(\psi_{i}N\psi_{i}^{n}\). \(N\psi_{i}^{n}\) \\ Size of stiffness matrix & \([3N_{N}\times 3N_{N}]\) & \([2N_{N}\times 2N_{N}]\) & \([2N_{N}\times(2N_{N}\times E_{N}\times 3)]\) \\ Calculation of the null space & Symbolical & Numerical & Numerical \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of 1D and 2D observability methods (OMs).  
The analysis of Table 3 shows that the number of unknown variables per element is doubled (changing from 2 to 4) after applying the change of variables in the 2D elements. Therefore, the number of columns of the stiffness matrix is also consequently increased. This table also illustrates that, unlike the analysis of 1D elements, a numerical simulation of the null space is required for the analysis of 2D element structures.
## 2 Materials and Methods
### Catalyst Characterization  
The micrographs of the SAS and LISA materials were acquired using a ZEISS (Jena, Germany) EVO 15 scanning electron microscope (SEM) with a resolution of 2 nm. To enhance conductivity and image quality, the samples were coated with a thin layer of gold using a sputtering technique prior to imaging.  
The X-ray diffraction (XRD) analyses were performed using a PANalytical Empyrean diffractometer. XRD patterns were acquired using Cu K\(\alpha\)1 radiation (\(\lambda\) = 1.546 A), with a rate scanning of 10\({}^{\circ}\) to 80\({}^{\circ}\) in terms of 20. Phase analysis was carried out using X'Pert HighScore Plus software ([[https://www.malvernanalytical.com/es/products/category/software/x-ray-diffraction-software/highscore-with-plus-option](https://www.malvernanalytical.com/es/products/category/software/x-ray-diffraction-software/highscore-with-plus-option)]([https://www.malvernanalytical.com/es/products/category/software/x-ray-diffraction-software/highscore-with-plus-option](https://www.malvernanalytical.com/es/products/category/software/x-ray-diffraction-software/highscore-with-plus-option)), accessed on 30 November 2024) and the PDF-4 database.  
The thermal stability of the materials was assessed using thermogravimetric analysis (TGA) on a Discovery SDT650 instrument (TA instruments, New Castle, DE, USA). Approximately 20-28 mg of the sample were placed in alumina pans with a capacity of 90 \(\mu\)L. Each sample was subjected to a temperature ramp from 30 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min under an air atmosphere. Prior to heating, the system was equilibrated at 30 \({}^{\circ}\)C to ensure consistent initial conditions.  
The specific surface area (\(\rm{S}_{\rm{BET}}\)) and average pore diameter (D) of the SAS and LiSA materials were measured using Brunauer-Emmett-Teller (BET) method, employing an ASAP 2100 porosimeter (Micromertics Instruments Co., Norcross, GA, USA) at -196 \({}^{\circ}\)C. Prior to analysis, the samples were degassed at 150 \({}^{\circ}\)C for 12 h under vacuum.  
Lithium content in the catalysts was determined by inductively coupled plasma mass spectrometry (ICP-MS) using an Agilent 7900 ICP-MS with an orthogonal detector system (ODS) (Agilent, Santa Clara, CA, USA). Prior to analysis, solid samples were subjected to a two-step microwave-assisted acid digestion. In the first stage, samples were digested with a mixture of concentrated nitric, hydrochloric, and hydrofluoric acids to dissolve the matrix. To eliminate the highly corrosive HF, a second digestion step was performed using 4% (\(w/v\)) boric acid, which effectively neutralizes HF.
## 3 Material and Methods
### Decision Tree (DT)  
DT is an AI technique that uses conditional judgement rules to divide predictor variables into homogeneous categories. The aim of DT specification is to find a set of decision rules for predicting an outcome from a set of input boundaries [62]. The DT is referred to as a predictive data mining tree depending on whether the target variables are objective or subjective [63]. Classifying the FOS of slopes from multiple input parameters is possible because modelling complex relationships between multiple input variables with an output variable is possible with a DT model as it will have both categorical and continuous variables without making any conclusions about the distribution of the provided data [64]. Furthermore, DT models are simple to implement, and the prediction results are simple to understand. The findings of the DT model revealed the relative significance of input parameters to the output parameter [65].  
A root node, internal nodes, and leaf nodes make up a DT structure. All of the input variables are stored in the root node. A decision function is connected with an internal node, which may have two or three branches. The output of a given input vector is represented by a leaf node [42]. Figure 3 shows the flowchart of procedures conducted for the modelling of a DT model. The procedure of modelling a DT model is governed by two steps: tree building and pruning.  
In the first step, the root node of the DT is defined by determining the input vector with the maximum gain ratio. The dataset is then divided into sub-nodes depending on the root values. For discrete input variables, each potential value is represented by a sub-node of the tree [66]. The gain ratio is then calculated for each of the sub-nodes separately in the second process, and the process is replicated until all of the instances in a node are  
Figure 3: Methodology flowchart for DT modelling.  
classified the same way. Leaf nodes are such nodes, and their names are the class values. Since the tree produced during the design process will have a large number of branches, it will be vulnerable to over-fitting [67], it must be pruned in order to improve the prediction performance for new data. Tree pruning can be divided into two categories: pre-pruning and post-pruning. In the case of pre-pruning, the tree's development will be halted before another criterion is true; in the case of post-pruning, the whole tree will be grown first, and then the finished subtrees will be replaced by leaves based on the tree's flaw relation before and after eliminating sub-trees. More explanations regarding DT models can be found in [54].