esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 6 Experiments and Results
### Experimental Data  
The data used in this experiment are global one-day ADS-B data, and the Opensky ADS-B interface in the traffic toolbox [43] to supplement the received data with a total of \(\sim\)16 million ADS-B data.  
Specific flight parameters include flight identification information (flight number or call sign), ICAO 24-digit aircraft address (globally unique airframe code), real-time position (latitude and longitude), position integrity and accuracy (GPS horizontal interval limit), barometric and geometric altitude, vertical change rate (climb/descent rate), track angle and ground speed, and emergency indication; specialized position identification information, etc., are shown in Table 5.  
A data distribution overview chart (Figure 9) was formed by randomly sampling data points to study their composition. The chart shows that the information points are primarily concentrated in three regions of East Asia, Central America and Europe. The majority of information points in the airspace are located at altitudes of \(\leq\)12,000 m, with only a few outlier information points falling in the 12,000-20,000 m range. Note that the time distribution is more uniform, and that information regarding all times is expressed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hardware** & & **Software** & \\ \hline \multirow{2}{*}{Processor} & Inter(R) Core i7-7700 & \multirow{2}{*}{OS} & Windows 10 \\  & @ 3.60 GHz & & pro (64\(\times\)) \\ \multirow{2}{*}{Memory} & 16 GB & Tools & Python, JavaScript \\ \multirow{2}{*}{Storage} & 1 TB & Database & MongoDB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Software and hardware environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Attribute** & **Type** & **Example** \\ \hline Track ID & Integer & 3 \\ Flight number & String & CCA4392 \\ Secondary code & String & CA \\ Departure airport & String & ZGSD \\ Landing airport & String & ZUUU \\ Model & String & A320 \\ Longitude & Integer & 104.373 \\ Latitude & Integer & 29.7987 \\ Height (10 m) & Integer & 359.7 \\ Direction & Integer & 315 \\ Speed (km\(\cdot\)h\({}^{-1}\)) & Integer & 680 \\ Time & ISODate & 2020-04-26T00:00:00Z \\ \hline \hline \end{tabular}
\end{table}
Table 5: ADS-B data structure and its example description.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 6 Conclusions  
This paper presents the employment of a DPA attack on the NIST standardized AES algorithm for key retrieval and prevention. To retrieve the secret key, we have applied the DPA attack on AES to obtain a 128-bit secret key by measuring the power traces of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**\# of KeyBytes**} & \multicolumn{2}{c}{**Key Values (In Different Representations)**} & \multirow{2}{*}{**Correlation**} \\ \cline{2-2} \cline{4-4}  & **(In Decimal)** & & **(In Hexadecimal)** \\ \hline KeyByte 1 & 161 & A1 & 0.445 \\ KeyByte 2 & 120 & 78 & 0.493 \\ KeyByte 3 & 91 & 5B & 0.502 \\ KeyByte 4 & 119 & 77 & 0.45 \\ KeyByte 5 & 45 & 2D & 0.525 \\ KeyByte 6 & 205 & CD & 0.356 \\ KeyByte 7 & 212 & D4 & 0.481 \\ KeyByte 8 & 31 & 1F & 0.478 \\ KeyByte 9 & 158 & 9E & 0.505 \\ KeyByte 10 & 85 & 55 & 0.293 \\ KeyByte 11 & 163 & A3 & 0.481 \\ KeyByte 12 & 69 & 45 & 0.45 \\ KeyByte 13 & 124 & 7C & 0.513 \\ KeyByte 14 & 139 & 8B & 0.41 \\ KeyByte 15 & 38 & 26 & 0.512 \\ KeyByte 16 & 236 & EC & 0.447 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The achieved correlated values for 16 bytes of AES-128 key.  
the computations involved in the algorithm. To provide resistance against the DPA attack, we have proposed a countermeasure based on the masking scheme: (i) Boolean and (ii) multiplicative. Moreover, we improved the complexity involved in the Boolean masking by introducing Rebecca's approximation. Additionally, we provided a novel solution to tackle the zero mask problem in multiplicative masking. We proposed our custom correlation technique, which resulted in a decrease in the calculation time. The synthesis results for DESIGN-I (an open-source core selected from GitHub) and DESIGN-II (inclusion of our countermeasure) are provided on a Zynq 7020 FPGA device. DESIGN-I takes 424 FPGA slices and achieves a clock frequency of 250 MHz. DESIGN-II requires 714 slices and achieves 205 MHz clock frequency. For both implementations (DESIGN-I and DESIGN-II), 12 clock cycles are required to execute one encryption and decryption operation, respectively. Therefore, the implementation results for our DESIGN-II provide the acceptability of this work for area-constrained applications that require prevention against DPA attacks.  
Conceptualization, H.T. and M.A.H.; methodology, M.M.H.; software, A.J.; validation, H.T. and A.R.A.; formal analysis, A.A.; investigation, A.A.; resources, M.M.H. and M.A.H.; data curation, H.T.; writing--original draft preparation, M.A.H. and H.T.; writing--review and editing, A.J. and M.M.H.; visualization, M.M.H.; supervision, A.R.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
The author [PERSON] extends his gratitude to the Deanship of Scientific Research at King Khalid University for funding this work through a research group program under grant number R.G.P. 2/150/42.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
Symmetrical cell assemblies were formed consisting of composite electrodes made of La\({}_{4}\)Ni\({}_{3}\)O\({}_{10:\pm 6}\) (LNO, synthesised elsewhere [19]) and of BaCe\({}_{0.9}\)Y\({}_{0.1}\)O\({}_{3-8}\) (BCY10, TYK, Tokyo, Japan), deposited on both faces of BCY10 electrolyte disks. The electrolyte discs were made by peltetising BCY10 by uniaxial pressing (200 MPa for 1 min), followed by sintering at 1400 \({}^{\circ}\)C for 6 h to achieve relative densities >95% that of the theoretical. The composite electrodes were prepared by spin-coating of a pre-made slurry of the LNO and BCY10 phases, in a 60%/40% volumetric ratio, using preparation details that are reported elsewhere [5], and calcined in air at 1050 \({}^{\circ}\)C for 4 h with heating and cooling rates of 1.5 \({}^{\circ}\)C min\({}^{-1}\).  
The scanning electron microscope (SEM, Hitachi Tabletop microscope TM4000 Plus, Tokyo, Japan) was used to assess the microstructure of the top-view and cross-section features of the composite cathode. The cross-sectional image was taken on samples cut with a circular blade saw. The energy dispersive X-ray analysis (EDS, Brucker SCU, Brockton, MA, USA) was performed on the top-view sample to provide elemental mapping.  
Electrochemical impedance spectroscopy (EIS) was used to study the electrical properties of the composite electrode, in the frequency range 0.01 Hz-1 MHz with a signal amplitude 50 mV (Electrochemie-Autolab PGSTAT302N frequency response analyser,Utrecht, Netherlands). Humidification was controlled by passing wetted O2 (50 mL min\({}^{-1}\)) that had been bubbled through the KCl-saturated H2O solution at 30 \({}^{\circ}\)C (\(\sim\)84% RH, JUMO humidity meter) to attain a \(p_{\text{H2O}}\sim 10^{-2}\) atm, herein defined as wet conditions. In contrast, low humidity conditions were obtained directly from the high purity O2 gas bottle, where a \(p_{\text{H2O}}\sim 10^{-5}\) atm was recorded (JUMO humidity meter). Impedance data were acquired on decreasing the temperature from 550 to 400 \({}^{\circ}\)C, being recorded twice, with dwell times of 1 h and after a further 1 h, to confirm stability. Impedance data were analysed using the _ZView(r)_ software (Scribner Associates, Southern Pines, NC, USA) and the MATLAB tool packages _DFRTtoEIS_[35] and _DRTtools_[36].
# A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan
## 4 Data-Driven Analysis
### Determining Factors Affecting Land Prices
Common with intuition, unemployment and interest rates (\(IRrf\)) exhibit negative correlations with land price, as higher rates lead to lower housing affordability. More interestingly, the degree of internet penetration and size of the general population are found to exhibit negative relationships with land prices. The negative association with \"internet penetration\" can be explained in two ways. First, \"internet penetration\" supports commerce that alleviates the need of physical space for retail and office. Second, the vast availability of information found on the Internet might make it easier for people to recognize the market rates of land prices, hence increasing competition in the market and driving down the \(ULPI\). This is especially true in the case of a larger population, where there will be more houses on the market. If the market behaves as a monopolistic competition, price competition of similar types of property will cause property prices to fall. Also, when a high-speed rail station is present in a densely populated city, it helps to bring the population away to other neighboring areas. This helps to decrease the demand pressure and reduce the prices for housing. Figure 7: Regression analysis outcome for the reduced multivariate regression model (2).
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 3 Identification of Cavitation in Torque Converter
### Test Bench Setup and Test Procedure
The \(M_{bg0}\) tends to increase initially and then decrease rapidly with the increase in pump speed, as shown in comparison with the simulation value. This is due to the fact that at low pump speeds, the hydraulic converter's oil viscous friction loss accounts for a significant portion of the total loss, resulting in a relatively low \(M_{bg0}\) of the pump wheel. As the speed of the pump wheel increases, the degree of cavitation also increases. This causes the medium acting on the impeller to evolve from pure hydraulic oil to a gas-liquid two-phase mixture. As a result, the force on the impeller weakens, leading to a sharp decline in the \(M_{bg0}\) of the torque converter. The \(\Delta L\) between the inlet and outlet of the torque converter and the changing trend of \(M_{bg0}\) are similar. According to the analysis above, the hydrodynamic torque converter experiences cavitation in the large load region with high speed and low speed ratio, resulting in a decrease in \(M_{bg}\). The \(\Delta L\) of the hydrodynamic torque converter can be used to determine if cavitation is occurring. It can be determined that cavitation has occurred in this torque converter when \(\Delta L\leq 0\) L/min.
## 3 Results
### Prediction Performance of the Developed NIRS Calibration Models  
The performance of the model developed from the blended yam samples was tested using a new set of yam samples (\(N=50\)) by comparing their wet laboratory results with the predicted values from NIRS. Figure 5a,c describes the determination coefficient (R\({}^{2}\)) for dry matter (\(0.95\)), protein (\(0.26\)), and starch (\(0.83\)) respectively. The SEP of \(1.4\) for dry matter (DM) with R\({}^{2}\) of \(0.95\) indicates the excellent performance of the model for dry matter prediction. R\({}^{2}\) of \(0.83\) for starch is also adequate for screening selection. However, the R\({}^{2}\) of \(0.26\) for protein might be considered too low, but the SEC (\(0.26\)), SECV (\(0.27\)), and SEP (\(0.36\)) values were closed (Figure 5b). It implies that though a model may have a poor R\({}^{2}\) value, with closed SEC, SECV, and SEP, the model could be used and improved. The prediction of starch content also showed that the equation is suitable for screening purposes with R\({}^{2}\) of \(0.80\) and SEP of \(3.38\). However, the SEC (\(2.2\)) and SECV (\(2.10\)) for starch for the blended samples are similar, indicating the robustness of the model.  
In general, models with R\({}^{2}\) of \(0.66\) to \(0.81\) can be used for screening purposes. In contrast, models with R\({}^{2}\) of \(0.83\) to \(0.90\) are suitable for many applications, and R\({}^{2}\) of \(0.92\) to \(0.96\) is ideal for most applications, including quality control [32]. Therefore, R\({}^{2}\) of \(0.95\) for dry matter and \(0.83\) for starch in blended samples (as shown in Figure 5a,c) make this form of sample preparation most suitable for the rapid prediction of these traits, though not good for protein prediction. Screening stages of breeding can also adopt chopped samples for the determination of starch with R\({}^{2}\)cal of \(0.84\) and dry matter with R\({}^{2}\)cal of \(0.64\) (Figure 2), though there was a poor R\({}^{2}\) for protein (R\({}^{2}\) = \(0.04\)). Grated samples had an equally good R\({}^{2}\) of \(0.88\) for dry matter and R\({}^{2}\) of \(0.62\) for starch content, but low R\({}^{2}\) of \(0.43\) for protein content (Figure 4). Figure 4 shows the SEC (\(1.94\)) and SECV (\(1.88\)) for the dry matter of the blended samples. Blended yam samples had a high RPD value of \(2.93\) for dry matter, \(2.16\) for starch, and a low RPD of \(1.03\) for protein (Figure 3). RPD values below \(1.5\) are considered not usable, although those between \(2.0\) and \(3.0\) are suitable for screening purposes, and values above \(2.5\) and \(3.0\) are excellent for quantitative predictions [33; 34].  
Figure 5: (**a**) Scatter plot of predicted dry matter vs. actual dry matter; (**b**) predicted protein vs. actual protein content; (**c**) predicted starch content vs. actual starch content for blended yarn samples. **SEP**: standard error of prediction.
## 2 Problem Formulation
### System Description  
Currently, most mobile platforms use different wheels as driving and supporting mechanisms. However, mobile platforms with many driving wheels require more complicated synchronization control, are more expensive, and have fewer applications in daily life. Therefore, a typical mobile robot only uses the front wheels or the rear wheels as the driving wheels. The mobile robot is controlled by the rotation speed of the driving wheels. In general, the mobile platform's rotational movement and forward movement can be precisely controlled by the driving wheels' rotation speed. However, the basic movement of the mobile robot needs to avoid forward sliding and lateral slipping during movement.  
Some coordinate frames need to be established in the working environment and on the mobile platform for positioning and trajectory tracking. These frames are as follows:  
1. The world frame \(O_{\text{W}}\): Establish the system's global coordinate system at a fixed position in the inertial coordinate system; it is used to describe the position and posture of the mobile robot.
2. The driving-fixed frame \(O_{D}\): Select the midpoint of the driving wheels as the origin of this local coordinate system, the \(x\)-axis direction is the same as the forward movement direction, the \(z\)-axis direction is the vertical direction of the movement plane,and the corresponding \(y\)-axis direction is established according to the right-hand rule direction.
3. The gravity-fixed frame \(O_{C}\): The origin of the gravity-fixed frame is established at the gravity center of the mobile platform, the \(x\)-axis direction is the same as the forward direction, the \(z\)-axis direction is the vertical direction of the movement plane, and the corresponding \(y\)-axis direction is established according to the right-hand rule direction.  
During movement, some structural parameters must be determined by measurement instead of using the values provided by the manufacturer to solve the positioning problem and achieve trajectory tracking with a mobile platform including the following:  
1. Basic structure parameters: The length of the mobile platform is \(L_{c}\), the width of the mobile platform is \(B_{c}\), the radius of the wheels of the mobile platform is \(R_{c}\), and the distance from the midpoint of the driving wheels to the gravity center of the mobile platform is \((D_{cx},D_{cy},D_{cz})\).
2. Vehicle positioning parameters: The initial position and initial posture of the midpoint of the driving wheels are \((P_{dx0},P_{dy0},Q_{dz0})\).  
In the trajectory tracking task, the structural parameters are fixed values, and the position and posture of the mobile platform are constantly changing. However, the distance from the midpoint of the driving wheels to the gravity center of the mobile platform is also a fixed value. The specific description of the structural parameters is shown in Figure 1.
## 5 Conclusions  
For the application of 3D line laser sensor for rail abrasion detection, a new point cloud alignment combination algorithm is proposed based on the use of voxel filtering downsampling, i.e., the combination algorithm of ISS-4 PCS + KD-ICP. The experiment first downsampled the initial collected rail point cloud contour, and finally selected voxel filtering as the downsampling method for the rail point cloud contour by comprehensively considering the running time of the algorithm and the influence on the later point cloud alignment. Next, we quantitatively analyzed the extracted ISS feature points, discussed the influence of the number of extracted feature points on the coarse alignment, and determined the optimal case of the number of feature points. Considering that the proposed coarse alignment algorithm works best when the number of ISS feature points is 4496, the number of feature points of 4496 points was chosen as the number of feature points for the proposed 4 PCS coarse alignment algorithm based on ISS feature points. Further, the proposed algorithm was compared and analyzed with other algorithms to verify the superiority of the proposed algorithm in terms of alignment time and accuracy. Finally, the rail vertical wear \(\Delta\)H and horizontal wear \(\Delta\)M were derived from the rail wear calculation experiment and then combined with the wear calculation formula to derive the total rail wear W and analyze it. The results show that the wear values measured by the ISS-4 PCS + KD-ICP method used in this paper are within the permissible error range and meet the testing requirements. In the coarse alignment stage, the ISS-4 PCS algorithm proposed in this paper not only improves the accuracy compared with other algorithms but also shortens the alignment time; in the fine alignment stage, although the KD-ICP algorithm used is similar to the other algorithms in terms of accuracy, it greatly shortens the alignment time and improves the efficiency of the algorithm. In general, the new method adopted in this paper shows great progress compared with the original method both in the alignment accuracy and the alignment time, which can greatly improve the detection efficiency of rail wear and provide a reference for non-contact rail wear detection methods. Although the ISS-4 PCS + KD-ICP combination algorithm proposed in this paper presents better results on rail wear detection, it has not been applied on other models. Therefore, the next step is to extend the
# Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater
## 1 Introduction, Motivation of the Experiment  
In the last decade, Central Europe, including the Czech Republic, has increasingly been faced with the negative effects of drought [1; 2; 3]. In some regions, long-term drought has caused in extreme cases the drying up of rivers and a long-term drop in groundwater levels [4; 5]. The related water management problems are manifested mainly in agriculture, but also in the supply of drinking water to the population [6].  
One of the partial solutions is an approach used in Greece, Malta, Portugal, Italy, and Spain, where the share of reused urban effluent ranges between 1 and 12% [7]. Analysis carried out in preparation for the Water Reuse Regulation expected a provision cost for the reclaimed water of less than EUR 0.5 per m\({}^{3}\)[8]. The current, relatively restrained attitude towards the reuse of treated waste water in Europe might be gradually mitigated [9]. The European Commission included wastewater in the circular economy action plan and set itself the goal of facilitating water reuse, especially in the field of irrigation [10]. Cleaned wastewater used for irrigation of crops that are not intended for direct human consumption can thus become an important tool for reducing demands on water resources in European agriculture.  
The annual production of all WWTPs in the Czech Republic is 23 m\({}^{3}\)/s, i.e., 738 Million m\({}^{3}\)/year [11]. These water currently leave the territory of the state without any use in a short period of time. A more meaningful use of this water, for example by its infiltration and subsequent reuse, is prevented by current legislation. Primarily, waste water treatment plants must discharge their production into the receiving water in the river in the CzechRepublic. Only in exceptional cases treated wastewater can be infiltrated into an aquifer, never directly into the saturated zone, but always through the soil layer.  
One of the main reasons why today's European public is so cautious about treated wastewater is the fear of discharging PPCPs into the groundwater. Today's waste water cleaning technologies mostly remove these substances only with problematic efficiency [12; 13; 14]. The published results suggest that many PPCPs are released into the rivers in nanograms to micrograms per liter concentrations [15; 16; 17].  
The main goal of the project was to provide expert documents that would confirm or, on the contrary, remove objections against the infiltration of treated wastewater into the fluvial quaternary and its possible reuse. The work focused primarily on clarifying the behavior of different types of PPCP substances, the speed of their spread and the possibility of natural attenuation. The tool for solving these questions was an experiment consisting in the infiltration of 2366 m\({}^{3}\) of treated wastewater over into a fluvial sand-gravel and monitoring the behavior of 113 monitored PPCP substances at the test site Kojetin in South Moravia (Figure 1).
## 5 Validation and Results
### Method Validation
#### 5.3.3 Verification Results
In summary, the results from both the correlation and regression analyses demonstrate a high correlation between the complexity evaluation of this method and the outcomes of risk and efficiency assessments for autonomous driving at intersections. These findings confirm the effectiveness and reliability of this evaluation method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **R** & \(\mathbf{R^{2}}\) & **Adjusted \(\mathbf{R^{2}}\)** & **B** & **SE** & **t** & \(\mathbf{p}\)** \\ \hline (Constant) & - & - & - & 2.258 & 0.027 & 84.905 & 0.000 \\ Complexity & 0.912 & 0.832 & 0.828 & \(-1.255\) & 0.082 & \(-15.249\) & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation of regression model and parameter estimates between complexity and efficiency. Figure 12: Keyframe screenshots of the two Apollo test cases. Figure 13: Regression analysis results for complexity and efficiency.