esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 3 An adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
### Algorithm for Implementation Process  
1. Initialize each parameter. The number of populations \(N\), the dimension of the solution \(D\), the maximum evolutionary generation \(G_{\text{max}}\), the upper and lower bounds of the individual variables \(x_{\text{max}}\), \(x_{\text{min}}\), the maximum and minimum of the scaling factor \(F_{\text{max}}\), \(F_{\text{min}}\), the maximum and minimum of the mutation probability \(CR_{\text{max}}\), \(CR_{\text{min}}\), the precocious cycle \(Q\), the precision value \(\delta\) and the variance threshold value det.
2. Initialize the population. The chaotic mapping strategy was used to generate NP initial populations, and the fitness values of the individuals were calculated and ranked in order of magnitude. From the NP populations, the top N fitness values were selected as the initial population of the algorithm.
3. Calculate \(F\), \(CR\), \(\mu\) according to Equations (9), (10) and (12).
4. Mutation operation. Calculate variant individuals \(t_{i,j}(g)\) according to Equation (11).
5. Crossover operation. Find a new variant individual. \(v_{i,j}(g)\) according to Equation (6).
6. Selection operation. The next generation \(x_{i,j}(g+1)\) is obtained from Equation (7).
7. Update the local and global optimal values.
8. Check whether the population is precocious or not; if it isprecocious, the mutation operation will be carried out again. If \(\sigma^{2}<\det\) and \(f_{best}(g)>\delta\), for \(j=1\) to \(D\), Calculate new individuals \(x_{i}(g)\) according to Equations (15) and (16), and update the optimal value.
9. Repeat Steps 4-8 for \(N\) times.
10. If \(g\) does not reach the maximum number of iterations \(G_{\text{max}}\), then go to Step 3, otherwise output \(x_{best}\), \(f_{best}\).
## 4 Example
### Telemetry Data Anomaly Detection Experiment  
#### 4.3.1 Data Matching Based on a Knowledge Graph  
When selecting CMG system anomaly data, we chose several real anomaly occurrences in the spacecraft and selected telemetry data for testing, one hour before and after the anomaly occurrence (30 min before and after). According to the anomaly report, we selected 60 data samples for testing. These data samples were not used for feature data extraction to evaluate the generalization performance of the anomaly detection algorithm. After preparing the experimental data, we conducted the telemetry data anomaly detection experiment.  
In the first step of the anomaly detection algorithm matching, to quickly match the corresponding reference data according to the static attributes of the experimental data and improve the search speed, the slicing window method is applied to the experimental data for static information matching. For example, setting the slicing window to a length of 100 data points as a detection cycle, performing static information matching for the data within each slicing window, obtaining the reference dataset, and then performing anomaly detection of the telemetry data sequence.  
In each detection cycle, we selected the 1-2 most similar feature datasets as reference data and used the DBSCAN clustering algorithm for analysis. Figure 18 shows the clustering results obtained using the DBSCAN method. Compared with the reference dataset, this method can easily identify abnormal data points. Figure 18 depicts the clustering results of normal reference data (represented by dots) and abnormal data (represented by plus signs).  
Figure 17: Knowledge graph visualization.  
According to the tests, the method proposed in this paper can identify 95% of the anomalies in the test set. Even though the current implementation is only a small-scale test demonstration, it still shows high accuracy compared to the traditional threshold-based methods. These are widely adopted in spacecraft ground control systems where technicians monitor telemetry data in near real time during the spacecraft's in-orbit operation, aided by signal thresholds to check if values exceed preset ranges. Typically, this type of monitoring targets only a portion of the telemetry sequences. Despite its common use in the industry, our proposed method demonstrates significant advantages over this approach. The accuracy comparison results between our proposed method and the traditional threshold-based method are shown in Table 3.
# Development of Multi-Cation-Doped M-Type Hexaferrite Permanent Magnets
## 3 Results and Discussion
### Crystalline Structure and Microstructure Analysis
When a small amount of Fe\({}_{2}\)O\({}_{3}\) is present in the M-type hexaferrite, grain growth is well-controlled. However, in the \(y\) = 2.0 sample, Fe\({}_{2}\)O\({}_{3}\) was exhausted, and thus the Fe content would be insufficient even in the mother phase (M-phase). It is believed that the lack of Fe content promotes grain growth of SrM [23]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Cation Composition** & \(x\) & \(\rho\) (g/cm\({}^{3}\)) & \(M_{\rm S}\) (emu/g) & \(H_{\rm C}\) (Oe) \\ \hline  & 0.0 & 2.75 & 72.08 & 3562 \\ SrFe\({}_{12-2x}\)Si\({}_{x}\)Mg\({}_{x}\) & 0.05 & 2.87 & 72.15 & 3912 \\  & 0.1 & 2.91 & 70.46 & 3402 \\  & 0.2 & 4.07 & 66.49 & 2986 \\ \hline \end{tabular}
\end{table} 
Table 1: Sintered sample densities (\(\rho\)), saturation magnetization (M\({}_{\rm S}\)), and coercivity (H\({}_{\rm C}\)) values of the SrFe\({}_{12-2x}\)Si\({}_{x}\)Mg\({}_{x}\)O\({}_{19}\) (\(x\) = 0, 0.05, 0.1, 0.2) samples sintered at 1230 \({}^{\circ}\)C. The M\({}_{\rm S}\) value is defined as the magnetization (M) value at H = 25 kOe. Figure 2: (a) Magnetic hysteresis curves of the SrFe\({}_{12-2x}\)Si\({}_{x}\)Mg\({}_{x}\)O\({}_{19}\) (\(x\) = 0.0, 0.05, 0.1, 0.2) samples sintered at 1230 \({}^{\circ}\)C and (b) plots of \(M_{\rm S}\) and \(H_{\rm C}\) vs. \(x\).
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 2 Theoretical Background
### Fast Fourier Transform  
The Fourier transform is an analysis method that decomposes a function (or signal) over time into a function over frequency components. The Fourier transform is expressed as Equation (1):  
\[X(f)=\int_{-\infty}^{\infty}x(t)e^{-\beta\pi ft}dt\ (here,\ \int_{-\infty}^{\infty} \lvert x(t)\rvert dt<\infty) \tag{1}\]  
If \(x(t)\) is the 'impulse response' of a system, its Fourier transform, \(X(f)\), becomes the frequency response of the system. As this Fourier transform takes considerable time to analyze, the supplemented method is a fast Fourier transform (FFT) [16].  
In this study, we analyzed the frequency response of the acceleration signal measured from a smart bolt fixed to the valve to assess the characteristics of the leak signal and the performance of the smart bolt [18; 19].
## 3 Results and Discussion  
### Properties of Stamp Sand and Compost  
The properties of the stamp sand and compost are shown in Table 1. The stamp sand was slightly alkaline (8.2) in nature, whereas the compost had a near neutral pH (6.9). The stamp sand had an electrical conductivity (EC) of 83 uS/cm, whereas compost had an EC of 1840 uS/cm. Stamp sands had an OM content of 0.5% whereas compost had OM content of 84%.  
The stamp sand is sandy in texture with 95% sand, and 2.5% each of silt and clay. The Cu concentrations in stamp sand and compost were 719 and 54 mg/kg, respectively. The concentrations of total iron (Fe) and aluminum (Al) in stamp sand were 6200 and 2300 mg/kg, respectively. Stamp sand had total and plant available P contents of 800 and 0.7 mg/kg, respectively. Total and plant available P in compost was 2090 and 1009 mg/kg, respectively. The N content in stamp sand and compost was 1.81 and 388 g/kg, respectively. The remediation work done by USEPA on the Torch Lake superfund site in 2005 [7] may have increased the total P content of the stamp sand. However, as evidenced from the Mehlich 3 results (Table 1), a very small fraction of that total P is plant available (less than 0.1%); the rest of the P in stamp sand is in bound forms and not available to plants.  
Our data show that stamp sand is lacking in N, P, and OM. Previous studies have reported that the application of compost can be beneficial for the improvement of soil quality in sandy soils [16]. However, an added impairment in stamp sand was the presence of high concentration of Cu. Camelina is known to be a hardy oilseed crop that can be grown on marginal land unsuitable for cultivation. Camelina is resistant to drought, cold, and pests [17] and can adapt to a variety of environments. Although not known to be metal tolerant, field pennycress is cold-tolerant and is used as a winter cover to reduce soil erosion and nutrients leaching [18; 19]. There are few studies related to the heavy metal tolerance of these two species when grown in conjunction with beneficial soil amendments such as compost. Heavy metal toxicity has been reported extensively for several oilseed crops including other Brassicaceae family plants to which both amelina and field pennycress belong. Heavy metals, such as Cu, are known to limit seed germination in Indian mustard and result in decreased growth and metabolism and oxidative damage [20]. Soil amendments such as compost, biochar, biosolids, and manure, which contain a high amount of OM can help increase nutrient content of the soil in addition to decreasing plant availability of metals and help in revegetation of contaminated sites [21]. [PERSON] et al. [22] reported a decreased uptake of Cu by Indian mustard in soils treated with compost.
# Effect of Fuel Mass Flow at the End of Injection on Cavitation and Gas Ingestion in the Nozzle
## 3 Mathematical Model  
### Governing Equations  
In this paper, based on the finite volume method (FVM), a cavitation numerical model was proposed. This model contained two phases and three components, namely fuel, fuel vapor, and air. Then, coupled with the large eddy simulation [37] (LES), a set of governing equations was shared between the liquid and vapor in the control unit. The continuity equation and momentum equation were as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho u)=0 \tag{1}\]  
\[\frac{\partial\rho u}{\partial t}+\
abla\cdot(\rho uu)=-\
abla p+\
abla\cdot \tau+\int\limits_{S(t)}\sigma kn\cdot\delta(x-xt)ds \tag{2}\]  
In Equations (1) and (2), \(p\) is pressure, \(\rho\) is density, \(u\) is velocity, \(t\) presents time, and \(\tau\) is the viscous shear stress; \(\int\limits_{S(t)}\sigma kn\cdot\delta(x-xt)ds\) represents the momentum source term of momentum Equation; \(\sigma\), \(k\), and \(n\) represent the surface tension coefficient, interface curvature, and normal unit vector pointing to surface \(S\).  
Fuel flowed through the nozzle, there were two different flows of liquid-vapor. Cavitation was a dynamic equilibrium process in which the liquid was converted into vapor. Therefore, to establish the cavitation model, it is necessary to take the phase change-transition source term into the corresponding FVM transport equation:  
\[\frac{\partial(\alpha_{t}\rho_{l})}{\partial t}+\
abla\cdot(\alpha_{t}\rho_{ l}u)=\dot{m} \tag{3}\]  
\[\frac{\partial(\alpha_{v}\rho_{v})}{\partial t}+\
abla\cdot(\alpha_{v}\rho_{ v}u)=-\dot{m} \tag{4}\]  
\[\frac{\partial(\alpha_{g}\rho_{g})}{\partial t}+\
abla\cdot(\alpha_{g}\rho_{ g}u)=0 \tag{5}\]  
\[\alpha_{l}+\alpha_{v}+\alpha_{g}=1 \tag{6}\]  
In the above equations, the subscripts \(l\), \(v\), and \(g\) are diesel, diesel vapor, and air, respectively; \(\alpha\) is the volume fraction of each component; \(\dot{m}\) is the mass exchange rate between the liquid-vapor phase. The convection term in Equation (4) is as follows:  
\[\
abla\cdot(\alpha_{v}\rho_{v}u)=\rho_{v}(\
abla\cdot(\alpha_{v}u))+\alpha_{ v}(u\cdot\
abla\rho_{v}) \tag{7}\]  
In this paper, the fluid assumed incompressible, the convection term in Equations (3)-(5) is expanded as follows:  
\[\frac{\partial\alpha_{l}}{\partial t}+\
abla\cdot(\alpha_{t}u)=-\frac{\alpha _{l}}{\rho_{l}}  
The mass conservation equation for each component is as follows:  
\[\frac{\partial\rho_{i}}{\partial t}+\
abla\cdot(\rho_{i}u)=0 \tag{11}\]  
Substituting Equation (11) into Equations (8)-(10) to simplify and add:  
\[\
abla\cdot u=\dot{m}(\frac{1}{\rho_{l}}-\frac{1}{\rho_{v}}) \tag{12}\]  
Thus, the vapor-liquid phase transport equation is as follows:  
\[\frac{\partial\alpha_{l}}{\partial t}+\
abla\cdot(\alpha_{l}u)=\alpha_{l}( \
abla\cdot u)+\dot{m}(\frac{1}{\rho_{l}}-\alpha_{l}(\frac{1}{\rho_{l}}-\frac{ 1}{\rho_{v}})) \tag{13}\]  
\[\frac{\partial\alpha_{g}}{\partial t}+\
abla\cdot(\alpha_{g}u)=\alpha_{g}\dot {m}(\frac{1}{\rho_{l}}-\frac{1}{\rho_{v}}) \tag{14}\]
## 1 Introduction
It carries the bespoke SAR (Synthetic Aperture Radar) Interferometric Radar Altimeter (SIRAL) instrument which has three operating modes: as well as operating as a conventional pulse limited radar in its Low Resolution Mode (LRM), it can employ along track SAR processing to increase along track resolution, and in full SAR Interferometric (SARIn) mode to additionally record the across track slope. While LRM mode continues the data coverage of past and future ocean surface altimetry, SAR mode gives increased information on sea-ice covered seas and SARIn model is used to investigate the elevation and movement of ice sheets and glaciers. The wave state of the ocean surface is characterised by the Significant Wave Height (SWH), described as the characteristic height of the random waves in a sea state as observed from a ship and defined as 4\(\sigma_{s}\) where \(\sigma_{s}\) is the standard deviation of the sea surface elevation [20]. The use of existing LRM equivalent algorithms to retrieve SWH from CS2 has been performed by Radar Altimetry Database System (RADS) [21] for both LRM and pseudo-LRM (pLRM) modes (a conversion from the recorded SAR retrievals to theoretically coincident pulse limited echo [22]) and incorporated into large consistent wave data sets and models [9; 17]. However CryoSat-2 operates primarily in SAR mode for the Polar seas, a region that has had significant change to the open ocean state over the period of CS2 operation [9], with reduced summer sea ice extent. The theory to retrieve SWH from SAR mode exists, and we present here an application of this theory to retrieve ocean surface information over the full CS2 period in order to improve the quality and coverage of observational SWH data in polar seas. The data processing chain presented in this paper can also be applied to other satellite missions and to cover the global oceans. The instrument specifications described in Section 2.3 can be adapted for the SAR instrument on Sentinel 3 for example, and the global LRM mode data from CS2 can be processed to give SWH data in mid-latitudes. A description of the CS2 data format accessed for processing and the final data end products are described in Section 2.1. Additional supporting data from analysis are described in Section 2.2. A description of the new semi-analytical retracker of [23] is in Section 2.3 and its adaptation for use in a CS2 LRM and SAR mode data processing chain is in Section 2.4. We present an in-depth look at the retracking process in Section 3.1. Comparisons to alternative CS2 SWH data are presented in Section 3.4, validation against in situ wave buoy observations is in Section 3.5. The description of the final validated Arctic processed data is given in Section 3.7, with additional Antarctic data in Section 3.8. Final conclusions are in Section 4. The new data are available at [[http://www.cpm.ucl.ac.uk/ocean_wave_height/](http://www.cpm.ucl.ac.uk/ocean_wave_height/)]([http://www.cpm.ucl.ac.uk/ocean_wave_height/](http://www.cpm.ucl.ac.uk/ocean_wave_height/))(accessed on 20 August 2021).
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
## 1 Introduction  
The grounding grid ensures the safety of personnel and power equipment in the substation facility. It also provides stable voltages to the equipment without disturbing the continuity of supply. The main aim of the grounding grid is to provide a low impedance path to fault currents caused by lightening strikes, short circuits, and switching surges [1; 2; 3; 4].  
The grounding grid is a lattice of horizontal bare conductors extending across the entire area of the substation. It is mainly made up of steel, galvanized steel, copper, copper clade steel, etc. Practically, the mesh size of a grounding grid varies from 3 m to 7 m with a depth from 0.7 m to 1 m [5]. As the grounding grid is hidden inside Earth, vertical conductors are the only access points from the Earth's surface. Moreover, fault currents are effectively dissipated into the Earth via vertical grounding rods that connect the grounding grid with low resistivity soil. Based on the substation configuration, the grounding grid mesh may be of equal or unequal spacing and may have a diagonal branch. The optimized configuration of the grounding grid plays a vital role in improving its efficiency. Therefore, the configuration is frequently modified, which is achieved by changing the mesh and adding vertical grounding rods [6; 7]. A typical grounding grid is shown in Figure 1.  
Grounding grid resistance (Rg), ground potential rise (GPR), maximum touch voltage, and maximum step voltage are the key parameters for measuring the performance of the grounding grid [5]. The step and touch voltages are influenced by the GPR and the configuration of the equipment in a substation. In case of a lightning strike, the step and touch voltages are the significant factors to improve the safety of the grounding grid [8; 9]. After years of operation, grounding conductors corrode and even break. Corrosion occurs due to the presence of water particles and air gaps in soil. Corrosion and breakpoints reduce the efficiency of the grounding grid, which can cause serious damage to the equipment, as well as personnel. Therefore, the stable operation of the grounding grid requires regular diagnostic tests.  
In essence, the performance analysis of unknown grounding grids is comprised of the following stages: fault diagnosis, configuration detection, and orientation detection; the former being dependent on the latter. Out of the three stages, orientation detection is the least addressed stage in spite of being very basic to the performance analysis of the unknown grounding grid. This is because the existing literature has considered the orientation of unknown grounding grids as parallel to the substation boundary, which practically may differ and leads the existing methods of configuration detection to ultimately fail [10]. Therefore, this paper proposes the transient electromagnetic method (TEM) to diagnose the orientation of the unknown grounding grid. Furthermore, the proposed method is validated for different mesh configurations of the grounding grid.
# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
## 5 Simulation and Results  
In this section, a simulation is carried out to compare the performance of the proposed IUPPM-VTL with traditional VTL and IUP-VTL.
## 3 Methods
### Bending Beam Rheometer  
Testing of the bituminous binders at low temperatures has been performed by bending beam rheometer (BBR) (Figure 6) under the standard of AASHTO T 313-08 [25] after double (short-term and long-term) aging. The samples of the bituminous binders for the tests had the shape of a beam with dimensions of 6.25 \(\times\) 12.5 \(\times\) 125 mm. Before testing the samples have been kept at the tested temperature for 60 min. In the beginning of the test the load, equal to 980 mN, is applied automatically for 1 s and it has been kept as the constant one for the following 240 s. The maximum deflection of the middle of the beam has been measured automatically.  
Figure 5: Sector compactor.  
Figure 6: Rheometer with bending beam (BBR).  
Maximum stress on the bottom surface of the bituminous beam in its middle has been calculated under the formula:  
\[\sigma=\frac{3\cdot P\cdot\ell}{2\cdot b\cdot h^{2}} \tag{1}\]  
where \(P\) is a load, mN; \(h\), \(b\), \(\ell\) are height, width, and length of the beam respectively, mm.  
Maximum strain of the bottom surface of the bituminous beam in its middle at the time moment \(t\) has been calculated under the formula:  
\[\varepsilon(t)=\frac{6\cdot h}{\ell^{2}}\delta(t), \tag{2}\]  
where \(\delta(t)\) is the maximum deflection of the middle of the bituminous beam, mm.  
The stiffness modulus of the bitumen at the load duration of \(t\) has been calculated under the formula:  
\[S(t)=\ \frac{P\cdot\ell^{3}}{4\cdot b\cdot h^{3}\cdot\delta(t)}. \tag{3}\]  
Bitumen m-value, characterizing stress relaxation rate, has been calculated under formula [26]:  
\[m(t)\ =\ \frac{d\ \text{log}\left[S(t)\right]}{d\ \text{log}\left(\text{t} \right)}. \tag{4}\]  
Testing of the bitumens has been performed at the temperatures of \(-24\ ^{\circ}\)C, \(-30\ ^{\circ}\)C, and \(-36\ ^{\circ}\)C. Such temperatures are characteristic for winter climate in many countries including Kazakhstan [27; 28; 29; 30; 31].
## 8 Security Examination  
A secure random key generation process using b bits is used to create both a user key and a group key. A user key or a group key is used as a starting point for the generation of a session key, which is then combined with another value and hashed. The user key provides a safe environment. The extra value, which is generated by feeding a random integer into a HASH function, is completely arbitrary and unrelated to anything else. Since this is the case, the session key is likewise safe.  
The user key may be automatically updated at certain intervals inside the KMS. The number of users who are leaving or joining the DR project determines whether or not the group keys need to be refreshed. In the case that a user wants to join or leave the DR project at the time of the update, the group key must be reset.  
When it comes to refreshing techniques, the HASH function is used for both the user key and the session key. The session key differs from other keys in that it can be updated using either a random additional value or metering information together with the metering date. As a direct consequence of this, the new keys function independently of the old ones.  
Only the two endpoints of the communication channel have access to the session keys. The receiver first uses a secure session key to validate the digital signature associated with the encrypted material. The communication is only decrypted by the recipient if it is successful in passing authentication. In such a case, the message is deleted without being read. After this, the authenticity of the information transfer as well as its integrity is guaranteed.  
Considering that people have the option to either join or leave the group that is working on the DR project, it is important to think about the group's forward and backward security. The group keys and other values are all generated and refreshed in accordance with our strategy if any user decides to join or leave the group before being sent to the new group's members. This process only takes place if there are users who make either of these decisions.