esa-sceva/satcom-chunk-collection huggingface dataset:
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 3 Preliminaries
### Particle Swarm Optimization (PSO)  
PSO algorithm is a tool for solving optimization problems, which was created by [PERSON] and [PERSON] [28]. Its inspiration stems from the swarming behavior of birds. The PSO algorithm is a computational way that optimizes a problem by improving a population of candidate solutions, here called particles, and moving these particles around in the search space according to simple mathematical formulae over the particle's position and velocity. Every particle's movement is influenced by its local optimal position but is also guided toward the global optimal positions in the search space, which are updatedas better positions are found by other particles. This is expected to move the particles' swarm toward the global optimal solution. At every movement, the solutions are evaluated according to an objective function, which is also called \"fitness function\". Generally, the main target is maximizing or minimizing the objective function. The standard PSO algorithm is very simple and easy to implement [29]. For a multidimensional problem of maximizing the objective function, \(f\) can be described as  
\[X^{*}=argmax\quad f(X)=\{X^{*}\in R:f(X^{*})\geq f(X),\quad\forall X\in R\} \tag{1}\]  
where \(X\) is a \(D\)-dimensional vector, \(X^{*}\) is the optimal solution after optimization. The \(i\)th particle is represented as \(X_{i}=(x_{i1},x_{i2},\cdots,x_{iD})\), the velocity \(V\) also is a \(D\)-dimensional vector, and \(i\)th particle's velocity is represented as \(V_{i}=(v_{i1},v_{i2},\cdots,v_{iD})\). The particle's position and velocity are updated by the mathematical Formulae (2) and (3). The new velocity is updated according to the following expression:  
\[v_{id}=v_{id}+c_{1}r_{1}(pbest_{id}-x_{id})+c_{2}r_{2}(gbest_{gd}-x_{id}) \tag{2}\]  
where \(v_{id}\) is the \(i\)th particle's velocity in the \(d\) dimension; \(c_{1}\) and \(c_{2}\) are the acceleration constants called cognitive coefficients and social coefficients, they are used to balance the displacements of particles toward the local or the global optimal position; \(r_{1}\) and \(r_{2}\) are independent random numbers in the interval (0, 1). The \"personal best\" of the \(i\)th particle \(pbest_{id}\) indicates the best position obtained so far by that specific particle, \(gbest_{gd}\) is the global best position of the whole swarm. Generally, the value of \(v_{id}\) is limited to the range \([v_{min},v_{max}]\) for controlling roaming of the particle outside the search space.  
The new location of \(i\)th particle is based on the following equation:  
\[x_{id}=x_{id}+v_{id} \tag{3}\]  
where \(x_{id}\) is the \(i\)th particle's position in the \(d\) dimension.  
The PSO algorithm will stop when the maximal iterations are reached or the global optimal position of particle in the swarm cannot be improved further after sufficiently many iterations. Therefore, the PSO algorithm has shown its advantages and efficacy in solving complex optimization problems. The basic process of PSO algorithm is shown in Figure 2.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 9 Conclusions  
In this paper, satellite formation reconfiguration and formation-keeping control laws are proposed by employing the passivity-based output feedback concept. For the complete nonlinear and time-dependent dynamics of the relative motion of a pair of satellites in elliptical orbits, new combined control algorithms, including a consensus protocol, are proposed and analyzed. A comparison of the control modes using passivity-based output feedback control and the proportional-differential controller with and without the consensus algorithm is given. The new result of the paper is also a rigorous mathematical proof of the use of control laws based on the passification method for the satellite system under consideration. The simplified version of the control law, which requires fewer calculations, is also considered. To increasing the accuracy of positioning satellites in the formation, in addition to the obtained algorithms, it is proposed to use multi-agent control algorithms based on the consensus protocol. Various methods of constructing satellite formations require the lowest energy costs to maintain the system. Mathematical modeling and computer simulations of the proposed algorithms for options for constructing formations are carried out in two PCO orbits of 8 satellites. The results obtained show good performance of the proposed satellites formation control laws for the problem of interest. It is demonstrated that the proposed formation-keeping control laws have robustness with respect to a variety of perturbations, including aspherical geopotential perturbation, air drag and solar radiation pressure.  
Conceptualization, A.M.P; data curation, I.K. and J.F.; formal analysis, A.M.P.; funding acquisition, A.M.P.; investigation, I.K. and J.F.; methodology, A.M.P.; project administration, A.M.P.; software, I.K. and J.F.; supervision, A.M.P.; writing--original draft, A.M.P. and B.A.; writing--review and editing, A.M.P. and B.A. All authors have read and agreed to the published version of the manuscript.  
The work was carried out in BSTU \"VOENMEH\" with financial support from the Ministry of Science and Higher Education of the Russian Federation (government contract agreement No. 075-03-2020-045/2 of 9 June 2020).  
The authors declare no conflict of interest.  
Figure 15: PBC law. Error and control time histories: (**a**) errors along axis x; (**b**) controls along axis x; (**c**) errors along axis y; (**d**) controls along axis y; (**e**) errors along axis z; (**f**) control along axis z.
## 4 Test-Bed Implementation Setup
### Assumptions  
Since the raspberry pi OS driver for WLAN only provides support for a maximum MTU (Maximum Transmission Unit) of up to 1500 bytes and increasing the limit requires Linux kernels to be configured, recompiled, and reconfigured, it is left as a maximum of 1500 bytes. It is also acceptable to set the maximum MTU to 1500 bytes as indicated in the _batman-adv_ documentation. In this case, the batman algorithm automatically fragments the frames on the data-link layer and combines the de-fragmented frames on the receiver node. Though the authors of BATMAN do not recommend this method, additional overhead for fragmentation and de-fragmentation is required and may impact the network's performance. As the topology utilized in the experimental setup is the same for all the tests  
Figure 8: Placement of nodes at various locations in the campus block with configuration.  
Figure 9: (**a**) Lab 1 with 3 nodes (2 in sight) and (**b**) Lab 4 with 3 nodes (2 in sight).  
conducted (BATMAN IV, BATMAN V, and BATMAN IV with split hop penalty) during the study, they are comparable, and therefore, any changes to the MTU would affect the results in the same manner for all protocols tested.  
Another assumption is that the availability of the RF medium on the 2.4 GHz band for all the nodes to communicate is limited in the tested scenarios, as labs (where the evaluation has been conducted) are within the wi-fi range of the campus. Channel 9 with a band of 2.542 GHz was chosen for the ad hoc network, though choosing a different channel would have the same density as choosing the 8 or 9 channel of the wi-fi. However, due to the nature of radio waves, coverage and throughput might change but will exhibit similar performance deltas as presented in the results [36]. Due to the real scenario, the footprint movement of the people involved in the experiment and a limited number of people part of the staff was minimized to the best of our abilities but could not be kept to zero.
## 4 Results and Discussion
### Experiment 3: Manipulation of Petri Dishes for Scanning the QR Code  
The robot successfully demonstrated the ability to manipulate the Petri dish to complete the task. One important consideration is the positional accuracy of each component of the mobile robot.  
The average positioning error of the mobile robotic platform used in this work was found to be 10.2 mm without applying any error correction but by using the fine positioning technique as explained in experiment 1. This error was acceptable in Experiment 1 but is not suitable for the precise manipulation of Petri dishes. The average error after using the more advanced error compensation technique was found to be 1.9 mm and the maximum error exceeded 3 mm as shown in Figure 11. This is a significant improvement but still not good enough. Solving this error is critical for successfully executing tasks, such as placing the lid on the Petri dish after environmental sampling.  
Figure 10: Live location of the robot updated on the map as the robot moves towards the storage area. L\({}_{1}\) to L\({}_{4}\) are the storage area, home point, path waypoint, and materials transfer area.  
The maximum error of the platform should be less than 1.5 mm for the correct dish placement which corresponds to the average gap between the lid and the dish. One way of solving this problem is by using advanced sensors, such as a vision system to find the error. The robotic arm mounted on the mobile platform can then use the information about the platform error calculated by the vision system and compensate for this error. In the current work, the error compensation technique using the vision system was able to reduce the average error to 0.19 mm with a maximum error <0.5 mm as shown in Figure 12. Hence, it is concluded that a vision system is essential for this task.
## 3 Results
### Comparison of the Simulated and Filter Paper Method SWRC
The variations in the parameter \(n\) value also contributed to the \(\psi_{m}\) decrease since it modifies the slope of the SWRC. Therefore, the rise in this parameter on the simulated SWRC results in smaller \(\psi_{m}\) values. Hence, according to the results provided by [PERSON] model, it would be necessary to use a stabilization method to control the volume changes (due to soil suction) of the arsenic-contaminated soil; although, it would not be necessary. The variation in the shape of the SWRC follows the soil's particle size change. According to the Laplace equation, the bigger the particle size, the smaller the \(\psi_{m}\) value. This is explained by the inverse relationship between the \(\psi_{m}\) and the radius of curvature. In other words, when the suction decreases, the radius of curvature increases. However, this increase in the estimated particle's diameter and the modification of the value of the parameters \(m\) and \(n\) do not decrease the difference between the simulated and the filter paper method curves. As an alternative, using diverse methods to predict the SWRC on contaminated soil samples might provide a fast and initial approximation. An example of such a simple method is the grain-size analysis. However, these SWRCs should be interpreted with caution since they may induce incorrect estimates of the \(\psi_{m}\) and, consequently, the hydraulic conductivity since the \(\psi_{d}\) value is required to obtain the SWRC from Equation (1). Therefore, the \(\psi_{d}\) value can be obtained using the reference values of a similar soil or by using the filter paper method at a high saturation rate.
## 5 Results  
### LSTM Models and SVR Models  
Figure 12, Tables 4 and 5 present the comparison between measured and predicted displacement of ZK2-1 and ZK2-3 using LSTM and SVR models. For ZK2-1, the values of RMSE and MAPE with the LSTM model were 13.79 mm and 0.74%, while the accuracy factors of the SVR model were 15.12 mm and 0.74%. For ZK2-3, the values of RMSE and MAPE with the LSTM model were 12.74 mm and 0.43%, while the accuracy factors of the SVR model were 15.43 mm and 0.60%  
In trend terms, the values of RMSE with the LSTM model and SVR model in ZK2-1 were 3.38 and 3.17 mm, and the values of RMSE with LSTM model and SVR model in ZK2-3 were 5.23 and 6.26 mm. In periodic terms, the values of RMSE with the LSTM model and SVR model in ZK2-1 were 12.95 and 13.63 mm, and the values of RMSE with the LSTM model and SVR model in ZK2-3 were 10.92 and 10.62 mm (Table 6).  
Figure 12: The curves of the relationship between observed and predicted displacement.  
The LSTM models and SVR models both had worse performances after May 2015. LSTM models had significantly better performance in step a, period c, step d, and period e than SVR models. The performances of the models in step a, period b, period c, step d, and period e in the periodic terms were reflected in the total displacement (Figure 12).
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 5 Description of the Experimental Set-Up  
Continuous monitoring is performed on two aluminum tensioned rods with the same geometric characteristics and subject to environmental excitation. The experimental set-up is shown in Figure 2.  
The geometrical characteristics of the rods are given below:  
* Material: aluminum 6000 series.
* Density: 2700 kg/m\({}^{3}\)
* Young modulus: 69 GPa
* Beam cross section: rectangular, with base \(b=15\) mm and depth \(h=25\) mm.
* Clear span between fixed ends \(L=4000\) mm.  
* Nominal tensile force in the beams T= 8 kN (axial force at installation). The axial force is then variable because of environmental thermal cycles. Each of the two rods is equipped with the following instrumentation:
* 4 single axis piezo accelerometers, placed in vertical direction, model 603C01 produced by _PCB PIEZOTRONICS_[21]. They are industrial accelerometers with a sensitivity of 0.1 V/g, a full scale \(\pm\)50 g, frequency range 0.5\(\div\)10,000 Hz.
* Strain gages arranged in a full-bridge configuration to measure tension. The conditioning unit is a National Instrument board NI 9236 and the system has been pre-calibrated against a certified load cell. A thermocouple measuring ambient temperature during the test is also present and connected to a conditioning device with an analog output, so that temperature data can be acquired together with acceleration data. Only ambient air temperature is measured, not the temperatures of the aluminum rods.  
All data are sampled thanks to a series of National Instruments NI 9234 boards, having a \(\pm\)5\(V\) full scale and a 24 bit ADC; data have been collected at a sampling frequency \(f_{s}=512\) Hz, considered enough to measure at least the first vibration modes of interest. A global acquisition time \(T_{acquisition}\) of 600 s has been chosen.  
Figure 3 shows the position of the accelerometers on the rods; they are named with the letter C followed by a number, indicating the column of the matrix in which the data for that accelerometer are collected. For beam 1, the sensors are C1, C2, C3 and C4 and the accelerometer data are collected in column 1, 2, 3 and 4 of the acquisition matrix. For Beam 2, the sensors are C5, C6, C7 and C8 and the accelerometer data are collected in columns 5, 6, 7 and 8 of the acquisition matrix.
## 2 Materials and Methods
### Methodology  
To predict the thermal fields on the surface of the turbine blade, the new methodology involving the creation of the geometrical model of the engine part with the corresponding CFD analysis with the defined boundary conditions, followed by the utilization of the artificial neural network utilization for the prediction of the temperature fields was developed. In Figure 1, the proposed methodology applied to the iSTC-21v jet engine can be seen. However, the proposed methodology can be analogically applied to any jet engine or its parts.  
According to the methodology presented in Figure 1, first, the 3D model of the investigated object must be created. The model must be prepared according to the geometry in accordance with the technical documentation of the product. In our case, the methodology was applied to the turbine parts of the iSTC-21v jet engine (Figure 2), which is described in the right part of Figure 1. The main goal of the methodology described in the previous figure is to create a method, which is able to predict temperature fields on the turbine blade surface much faster than the CFD method.  
The 3D model of the turbine and nozzle section is shown in Figure 2. The turbine section consists of the stator hub and stator shroud with 19 stator vanes. The pressure turbine consists of the rotor hub, rotor shroud and 27 turbine blades. According to the geometry, the finite element model was created. In our case, the model was prepared and meshed in the ANSYS software. The boundary conditions for 10 operational regimes were defined in the ANSYS CFX software. Inputs for the 10 performed CFD analyses are summarized in Table 1. The boundary conditions were defined by the temperature and  
Figure 1: Methodology for the prediction of the turbine blade surface temperature fields.  
Figure 2: 3D model of the geometry of the turbine and nozzle section of the iSTC-21v jet engine.  
pressure in front of the turbine and by the speed. All of these parameters were measured experimentally in laboratory conditions [35; 36]. These data were experimentally measured on the iSTC-21v jet engine specifically for the purpose of the application of the proposed methodology in our Laboratory of Intelligent Control Systems of Aircraft Engines.  
The third step shown in Figure 1 represents the utilization of the CFD analysis results as the inputs for the ANN. These coefficients are used for the ANN training in order to predict the thermal fields in the specific nodes with the positions defined in the chosen coordinate system. The last step shown in Figure 1 is linked up to the previous one, and it means the ANN is used during the life phase for the prediction of the temperature fields according to the measured data. In our case, as the input data, the experimental results presented in Table 1 were applied.
# Simple Approach for Evaluation of Abrasive Mixing Efficiency for Abrasive Waterjet Rock Cutting
## 4 Experimental Results and Analysis  
### Kinetic Energy and Rock Cutting Performance  
Figure 7 depicts a granite specimen after the experiment, along with the focus geometry, the water flow rate, and the AFR. The cutting surface indicates the cutting path, and the cutting side shows the cutting depth at the cutting start point according to the test conditions. Because the SOD is short, the cutting width is similar to that shown on the cutting surface. Figure 8 presents the cutting depth according to the AFR at each WFR for each focus diameter, showing that the larger the focus diameter, the deeper the cutting depth. The maximum cutting depth occurs at a large focus diameter, and the optimum AFR (\(\hat{m}_{a,o}\)), which is the AFR at the maximum cutting depth, increases with the focus diameter. With the same input energy conditions (e.g., water pressure, water, and AFR), the mixing variables affect the cutting depth.  
Figure 6: Schematic drawing of abrasive waterjet rock cutting.  
Figure 9 shows the DBP as a function of the AFR by focus diameter. This figure demonstrates that the larger the focus diameter, the farther the abrasives are from each other, resulting in a higher DBP. An increase in the AFR increases the number of abrasive particles in the focus and decreases the DBP (Figure 9a). As derived from Equations (4) and (7), the WFR does not significantly affect the DBP. This finding is explained by the fact that all the abrasive particles are simultaneously accelerated and move at the same velocity. A higher WFR results in a faster initial water velocity (Equation (3)), so the abrasive-water mixture moves faster, but \(t_{f}\) decreases. Whether the abrasive particles move quickly or slowly, the DBP remains the same. Because the WFR affects the terminal velocity of the abrasive, a larger WFR has a larger kinetic energy for cutting, even with the same AFR. The complex effect of the mixing variables can be expressed as the energy required to cut one unit depth of rock (\(\epsilon\)) from the experimental results, as shown in Figure 8.  
Figure 8: Rock cutting depth as a function of focus diameter at WFRs of (**a**) 10.67, (**b**) 29.5, and (**c**) 50.00 mL/s.  
Figure 7: Cutting results with different focus diameters and WFRs on the granite specimens.  
Figure 10 presents the kinetic energy per unit second (\(E_{a}\)) calculated using Equation (9) under the experimental conditions of this study. A high water flow rate corresponds to a large acceleration momentum, yielding a high kinetic energy because of the high terminal velocity. Increasing the AFR causes an increase in the total abrasive mass but decreases the terminal velocity (Equation (2)). As such, the kinetic energy for rock cutting and the effects of the abrasive mixing variables resulting from the experiment were evaluated using a function of the DBP.
## 3 Materials and Methods
### Digitalizing Calibration Data Exchange and Management
#### 3.3.1 Calibration Provider  
As the sizes and calibration volumes of calibration service providers vary greatly, there are large differences in the systems used for creating calibration certificates. Currently, it is still common that especially the smaller calibration service providers' laboratories may still use quite simple solutions for creating their calibration certificates, such as basic text editor tools and templates where the information is filled in manually. For the larger organizations, the investments in more advanced IT systems and automation are easier to justify and more beneficial.  
For the organizations using more advanced systems, adapting to a fully digital and machine-readable format is typically a lot less challenging as the data formats that have already been in use can, in most cases, be converted into a different with ease; for them, it is mostly simply a data-mapping task. For the organizations using more simple tools, the transition can be more challenging.  
For data integrity reasons, the calibration certificates need to be signed when data are exchanged in a digital format, even if a signature is not considered to be a mandatory part of a calibration certificate, as is the case with ISO 17025. The reason behind this is that a digital signature can be validated to prove the authenticity and integrity of a document or file, i.e., ensure that it is authentic, originally created, and sent by the correct person or organization, and that its contents are unaltered.  
The sharing of digital documents can be implemented in various ways; they can be sent via email, uploaded to a cloud server, or even delivered on a USB memory stick, to name a few possibilities. The differences in these methods are in the ways in which they can be integrated into the systems used to manage the documents.