esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Cr-Al-Ti-B-N and Cr-Al-Ti-B-O-N coatings were produced for the first time by closed field unbalanced magnetron sputtering using planar TiB SHS targets (STIM-4). The increase of current applied to the TiB target from 0 to 5 A results in an increase of B content in the coating from 0 to 10 at.%, inhibition of columnar growth, densification of coating's microstructure, and surface smoothening. The highest combination of mechanical (hardness 15 GPa; Young modulus 197 GPa; elastic recovery 0.5; elastic deformation of destruction \(H\)/E = 0.076; resistance against plastic deformation \(H^{3}\)/\(E^{2}\) = 0.085 GPa) and tribological properties (friction coefficient 0.65, specific wear \(7.5\times 10^{-6}\) mm\({}^{3}\)/(N\(\cdot\)m) was attained for the coatings containing 2.3 at.%. The relations between deposition parameters, composition, and coloration of coatings were established.  
The developed coatings might find a use for the protection of mechanical components from corrosion, abrasion, and wear. The ability to regulate the coloration of the upper layer by altering the oxygen content is particularly useful for the protection or finishing of various works of art since the materials employed in art usually do not allow for any substantial heating.  
P.V.K.-K.--supervision, deposition, investigation, and writing the manuscript. Z.S.A.--deposition and testing, writing of the manuscript, A.N.S.--corrosion study, S.V.--translation, correction, and revision of the manuscript. E.A.L.--material support (SHS-target).  
This research was funded by the Russian Foundation for Basic Research (RFBR) grant No 19-08-00187.  
Authors declare no conflict on interests.
## 4 Simulation Model and Results
### Simulation Results  
The simulation results are at first obtained with an \(R\)-\(L\) load. Figure 6a shows the output voltage waveform. It can be seen that output voltage consists of a nine-level and the peak value of the output voltage is 200 V, which is the sum of the DC-supplied voltage and the capacitor voltage of the other H-bridge.  
All the voltage levels observed in the output are 200 V, 150 V, 100 V, 50 V, 0 V, \(-\)50 V, \(-\)100 V, \(-\)150 V, and \(-\)200 V, respectively, at 50 Hz. In Figure 6b, the output current and reference current are depicted. From this figure, it can be clearly observed that load current is precisely tracking the reference current. The peak-peak current is 12 A, where the blue line shows the reference current and the red line shows the output current. This demonstrates the quick adaptiveness of the modified MPC algorithm. From these above figures, we can conclude that the system-output voltage and current are perfect according to the theoretical analysis.  
The capacitor voltage waveform is balanced at 50 V, as shown in Figure 6c. The capacitor voltage value has a maximum variation of less than 0.25 V (approximately). It is between 50 V and 52 V for a current range of 1 A to 6 A, which shows the effectiveness of capacitor voltage balancing. In Figure 6d, the magnified capacitor voltage is shown for a small interval. In this figure, the capacitor charging and discharging cycles are clearly visible. Whenever capacitor voltage is increased from the reference value, the controller applies the required voltage vector, which discharges the capacitor, and tries to bring it to the reference value. A similar control method is applied to increase capacitor voltage. This process is repeated and the controller balances the capacitor voltage at the desired value. Thus, we can conclude that the simulation is working properly and in accordance with the theoretical analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Inductive load & 1.2 mH \\ Resistive load & 20 \(\Omega\) \\ DC-Link capacitor & 1000 \(\upmu\)F \\ Sampling frequency & 10 KHz \\ DC voltage & 150 V \\ Capacitor voltage & 50 V \\ Reference load current (peak) & 6 A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters of HMLI.  
To show the variation of leakage current in HMLI, the waveform for leakage current and CMV are shown for the transient circuit (TC) in disabled and enabled conditions. Figure 7a,b show CMV and leakage current in the disabled TC and leakage current occurs around 30 mA. Figure 8a,b show CMV and leakage current when the TC is enabled and leakage current occurs around 19 mA.  
Figure 6: Simulation results of HMLI: (**a**) output voltage waveform, (**b**) load current tracking (– output current & – reference current), (**c**) capacitor voltage with voltage balancing, and (**d**) capacitor voltage charging and discharging.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Infinite Repeated Game in the Unlicensed Spectrum  
First, we define an operator's utility for playing an infinitely repeated game as follows.  
**Definition 1**: _For operator \(i\), given an infinite stage of payoffs \(U_{i1},U_{i2},U_{i3},\ldots,U_{ik},\ldots\), and discount factor \(\delta_{i}\) with \(0<\delta_{i}<1\), \(i\)'s future discounted reward is_  
\[\sum_{k=1}^{\infty}\delta_{i}^{k-1}U_{ik}. \tag{1}\]  
Assuming a common discount factor for all operators, e.g., \(\delta_{i}=\delta\) for all \(i\), the discount factor \(\delta\) has two equivalent interpretations. First, \(\delta\) means that the operator \(i\) tends to care about its payoffs in the near term more than that in the long term. The other interpretation of \(\delta\) is such that the operator cares about the future just as much as the present, but with probability \(1-\delta\), the game will end in any given round, e.g., there is always some chance that operators will not meet again. For example, if each sequence is only half as important as the previous one, then \(\delta=1/2\). Suppose that operators harmoniously coexist with a whole stage of mutual cooperation, by getting \(U_{sh}\) at each sequence. Then, the cumulative value of every sequence is \(U_{sh}+U_{sh}/2+U_{sh}/4+\cdots=2U_{sh}\). To compare the payoff with the one-shot game, one might multiply future discounted reward in Equation (1) by \(1-\delta\) to normalize it as \(U_{sh}\). Now we investigate two operators' interaction in the unlicensed spectrum in an infinite number of repeated games.
## 3 Network Architecture and System Model
### Complexity Analysis  
In Algorithm 1, a solver is used to solve the optimization problem. Here, the time complexity for solving the optimization problem is set to \(T\), the number of unloading tasks is set to \(N\), and the branch and bound method is used to traverse all unloading tasks. In the unloading target section, the process is simplified to selecting only the optimal two node locations using a cycle number. The time complexity of the proposed algorithm is \(O(TN)\).
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 2 System Model
### The Feedback Process of Precoding Matrices  
The full feedback of the precoding matrix imposes particularly high feedback overhead and storage requirements. We propose a CNN-based PCQNet scheme illustrated in Figure 2 to substantially decrease the feedback overhead. Specifically, the BS compresses the precoding matrices and then quantizes the compressed matrices to bitstreams for each UE. The UEs recover the precoding matrices utilizing an error-free transmission of the feedback bitstreams.
# A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review
## 2 Software
### Numerical-Based Approaches  
These solutions employ numerical methods like finite element analysis (FEA), the extended finite element method (XFEM), boundary element methods (BEMs), the meshless method, and the weight function method to simulate and analyze crack growth behavior. They provide detailed insights into crack behavior, stress distribution, and fatigue life prediction by discretizing the crack geometry and using iterative algorithms. This article primarily focuses on three-dimensional FEM and XFEM software due to its relevance in the context being discussed. In the subsequent subsections, detailed explanations of the widely used software in this domain will be provided.
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer 2: Recurrent Neural Network Model (RNN)  
After preprocessing the dataset, it is given as input to the first SimpleRNN model. The normalization layer first normalizes feedback, and the normalized output is provided as input to the RNN layer. A dense layer is applied to the outcome of the RNN layer to produce the outcome. Layer normalization gives all neurons in a layer the same feature distribution for a given input. The need for batches is removed when normalization is performed across all features and for each piece of input to a given layer [25]. That is why layer normalization works so well with sequence models like RNNs. Finally, the dense layer works on the RNN layer to produce the output.
# Aerodynamic Characteristics of a Square Cylinder with Vertical-Axis Wind Turbines at Corners
## 3 Results and Discussions
### Flow Pattern around the Cylinders
#### 3.3.2 Rotating Wind Turbines at Cylinder Corner  
Time-averaged pressure coefficient distributions of the three rotating cases are shown in Figure 12d-f. (\(R1\) and \(R6\) have excellent effects of drag reduction while \(R8\) has the worst result). The pressure distributions on the windward face of \(R1\) and \(R6\) are consistent with the plain cylinder, but \(R1\) and \(R6\) have lower negative pressure coefficients (0.7 and 0.65) on the leeward face, leading to about a 35% decrease in drag coefficient, as presented in Table 6.  
Time-averaged streamlines around the cylinder of the three rotating cases and the plain cylinder are shown in Figure 13d-f. Apparently, compared with the plain cylinder, the wake recirculation zones are dramatically elongated when the rotation speeds are 0.2/s(\(R1\)) and 2/s(\(R6\)). Thus, 86.0% and 71.3% decrease in \(\mathrm{C}_{1}^{\mathrm{RMS}}\) of \(R1\) and \(R6\) can be observed. On the contrary, the wake recirculation zone of \(R8\) is shorter than that of \(R6\), which results in a worse effect on drag reduction.  
Figure 14d-f shows the normalized time-averaged vorticity distributions of \(R1\), \(R6\), and \(R8\). When the rotating speed is 2/s(\(R6\)), the contour of normalized time-averaged vorticity distributions becomes more elongated than the plain cylinder, and the separated shear layer has smaller curvature due to the lower suction, as shown in Figure 6. As for \(R1\), the situation is similar. However, the curvature of the separated shear layer of \(R8\) is distinctly larger, so the suction of \(R8\) is greater than \(R1\) and \(R6\).  
In Figure 15d, the symmetrical vortex shedding is observed in the normalized instantaneous vorticity distributions during one period of \(R6\). The typical Karman-type vortex shedding is replaced by the symmetric shedding modes, which can lead to the large suppression of lift fluctuations shown in Figure 9. That is why the \(RMS\) lift coefficient (\(\mathrm{C}_{l}^{\mathrm{RMS}}\)) of \(R6\) is significantly lower than that of the plain cylinder. As for \(R1\), when the rotation speed is 0.2/s, the normalized instantaneous vorticity distribution during one period, shown in Figure 15c, is similar to \(S2\), which means that \(R1\) has a similar mechanism of drag reduction as \(S2\).
## 6 Application Example
### Emergency Response Process
There is no report on the use of mobile antennas and only VHF radio systems seem to be used to support communication in the field. This situation is aligned with many other large incidents affecting urban areas [15,18,20]. The use of sensor networks, for example focused on traffic control, could help by changing the frequency of the traffic lights when an emergency vehicle is approaching, or route the regular vehicles toward safe areas where they do not interfere with the response process. Moreover, oppnet-based applications could be used to coordinate the response activities in the field and share valuable information for local decision making. In this accident, as with many other urban emergencies, there was no predefined response plan, leading to a collapse in the first response process. A similar situation happened in the same place on 20 October 2013, and the effectiveness of the response process was similar to that previously described. However, these situations could have been addressed during a preparedness phase, using a HWSN. For instance, the emergency detection and diagnosis could be done in a few minutes using the technology already deployed in the area. This information and also the predefined emergency plan could be disseminated before the communication system collapses. The communication in the field could be addressed using oppnets, and the role of each node and information flow could be predefined. The amount and type of resources required could also be pre-calculated improving thus the time and effectiveness of the response process. Although the technology to implement IoT-based solutions that support urban emergency responses is already available, there are no clear guidelines about the role played by each component type, how to integrate them, and how to design the behavior of an integral solution. In that sense, we hope that this proposal contributes to modeling solutions for the described and also other work scenarios. The definitions presented in Section 4 allow determining the behavior of the different types of nodes, and the interaction capabilities among them. We can therefore model the whole response system or a part of it (for instance, the search-and-rescue or the medical assistance process). Knowing what we can expect of an emergency response process or an IoT-based solution will always be beneficial, although we decide to not improve them. In that sense, the use of HWSN can make an important difference when we try to understand the potential impact of an implementable solution.
# Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
## 3 Materials and Methods
### Dataset Construction  
Supervised learning in machine learning is an approach that learns from given labeled data to predict the outcome of new, unseen data. This method was employed in our work to enable the algorithm to learn from the data that were provided. The system was developed to perform a visual classification of date fruit; therefore, the system needs to learn from visual data. Consequently, we built a new image dataset of date fruit that contained two main classes of dates fruit, where each of these classes had 55 images and each of the images contained 10 date fruit. This means that each class represented 550 samples of dates fruit. The first class represented the date fruit with excellent quality and the second class represented the date fruit with poor quality. In addition, we had a third subset that had only five images containing a mix of date fruit with excellent and poor quality.  
The steps that were followed to construct this dataset began by collecting and separating dates with excellent surface quality from dates with poor surface quality. After that, we organized a way of capturing the images, such as setting the background and lights to capture all images with the same settings. In the following, each step is explained in detail.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(t\) & \(a_{11}\) & \(a_{12}\) & \(b_{11}\) & \(b_{12}\) & \(a_{21}\) & \(a_{22}\) & \(b_{21}\) & \(b_{22}\) & \(c_{1}\) & \(\delta\) (\%) \\ \hline
100 & \(0.31044\) & \(0.27323\) & \(0.47823\) & \(0.79593\) & \(-0.16988\) & \(0.31233\) & \(0.34733\) & \(0.79082\) & \(-0.35990\) & \(11.48781\) \\
200 & \(0.35712\) & \(0.26107\) & \(0.47534\) & \(0.80554\) & \(-0.20188\) & \(0.32807\) & \(0.38468\) & \(0.77922\) & \(-0.30469\) & \(7.07660\) \\
500 & \(0.37558\) & \(0.30333\) & \(0.48485\) & \(0.83653\) & \(-0.22143\) & \(0.30058\) & \(0.41246\) & \(0.78396\) & \(-0.28669\) & \(4.56000\) \\
1000 & \(0.36022\) & \(0.29164\) & \(0.49690\) & \(0.84216\) & \(-0.23072\) & \(0.29199\) & \(0.40519\) & \(0.76406\) & \(-0.27138\) & \(2.49100\) \\
2000 & \(0.36115\) & \(0.29540\) & \(0.49771\) & \(0.84667\) & \(-0.24094\) & \(0.30037\) & \(0.39938\) & \(0.74612\) & \(-0.27434\) & \(2.05076\) \\
3000 & \(0.36636\) & \(0.30139\) & \(0.50263\) & \(0.84719\) & \(-0.24389\) & \(0.30650\) & \(0.39658\) & \(0.74012\) & \(-0.26554\) & \(1.89797\) \\ \hline True values & \(0.35000\) & \(0.30000\) & \(0.50000\) & \(0.84000\) & \(-0.25000\) & \(0.30000\) & \(0.40000\) & \(0.75000\) & \(-0.25000\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The F-RGLS parameter estimates and errors for Example 2. Figure 2: The diagram of a multi-input OEAR system.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.6. Resisting Man-in-the-Middle Attacks (Threat Model 4)  
An attacker \(A\) intercepts patient transmission \(M_{1}=\{NID_{PD},NID_{P},V_{1},V_{2},C_{P},T_{a}(x),TS_{1}\}\) and \(M_{4}=\{QID_{P},V_{5},T_{c}(x)\}\) between \(Pat\) and \(Doc\). \(A\) cannot successfully modify \(M_{1}\) without \(PW_{p}\), \(r_{p}\) or \(r_{D}\) because of the one-way hash property and ECM-DLP, where \(W_{p}=h(ID_{p}\|PW_{p})\), \(P_{p}=h(W_{p}\|ID_{p}\|ID_{D}\|TA_{x}(x))\), \(Cer_{p}=h(T_{r_{p}}(T_{r_{MCS}}(x)\,mod\,\,p)\|TS_{1})\), \(V_{1}=h(X_{p}\|P_{p}\|Cer_{p}\|R_{p})\) and \(V_{2}=h(ID_{p}\|Cer_{p})\). \(A\) cannot successfully modify \(M_{4}\) without \(r_{p}\) and \(r_{MCS}\) because of the one-way hash property and ECM-DLP, where \(QID_{P}=E_{SK_{MCS}}(NID_{P}^{new}\|X_{P}^{new})\), \(NID_{P}^{new}=E_{r_{MCS}}(ID_{p}\|W_{p}\|R_{new}\|w)\), \(X_{P}^{new}=h(ID_{p}\|R_{new})\), \(QID_{P}=E_{SK_{MCS}}(NID_{P}^{new}\|X_{P}^{new})\), and \(V_{5}=h(NID_{P}^{new}\|ID_{p}\|SK_{MCS}\|R_{p}\|X_{P}^{new})\). An attacker \(A\) intercepts patient transmission \(M_{2}=\{NID_{P},NID_{D},V_{1},V_{3},C_{P},D_{PHI},T_{a}(x),T_{b}(x),\)\(TS_{1},TS_{2}\}\) and \(M_{3}=\{QID_{P},V_{4},V_{5},T_{c}(x)\}\) between \(Doc\) and \(MCS\). \(A\) cannot successfully modify \((M_{2}-M_{1})\) and \((M_{3}-M_{4})\) without \(r_{MCS}\) or \(r_{D}\) because of the one-way hash property and ECM-DLP, where \(SK_{PD}=h(T_{r_{D}\cdot a}(x)\,mod\,\,p)\), \(ID_{P}=NID_{PD}\oplus SK_{PD}\), \(Cer_{PD}=h(T_{r_{D}\cdot r_{D}}(x)\,mod\,\,p\|TS_{1})\), \(SK_{DMCS}=h(T_{r_{MCS}}(x)\,mod\,\,p)\), \(NID_{D}=E_{SK_{DMCS}}(ID_{D}\|R_{D})\), \(Cer_{D}=h(T_{r_{D}\cdot r_{MCS}}(x)\,mod\,\,p\|TS_{2})\), \(D_{PHI}=Cer_{D}\oplus PHI\) and \(V_{3}=h(ID_{D}\|Cer_{D}\|PHI\|R_{D})\). Hence, the proposed scheme can avoid the attacker \(A\)'s trick for \(Pat\), \(Doc\) and \(MCS\), and resists man-in-the-middle attacks.
## 3 Results
### Application of the Modified Tier 3 Process for Various Lead Characteristics  
The introduced modified Tier 3 procedure was repeated for different geometrical characteristics and material properties of the leads, as well as material properties of the surrounding tissues, and the absorbed power at their tips was assessed. More specifically, the insulation radius, the lead length and the dielectric characteristics of the medium were varied by \(\pm\)10% from their initial values, as described in Section 2.1. Table 3 presents the deviation between the calculated deposited power obtained with electromagnetic simulations and the corresponding deposited power evaluated with our proposed numerical method. This deviation was well within the confidence interval and less than 0.5 dB.  
Our process was also applied for a pair of realistic multi-electrode implants, different from the generic leads of uncapped wires studied above. Figure 10a illustrates the SAR distribution at their electrodes. Figure 10b presents the deposited power for each electrode as assessed by the full-wave simulations, the standard Tier 3 approach and the here proposed modified Tier 3 procedure. The deviation between the numerically calculated power from the Sim4 Life and the estimated one by the proposed method is in the range of 0.1 to 0.5 dB for the different electrodes. On the contrary, the respective deviation between the simulation results and the values obtained by the standard Tier 3 process ranges from 2.9 to 3.8 dB.  
Regarding the placement of leads along trajectories inside a human head, Figure 11 shows the SAR distribution at the tips of the generic implants. The deviation between the numerically calculated induced power at the tips of the pair of leads and that estimated by the proposed approach was 0.2 dB, whereas the difference from the power estimated with the standard Tier 3 procedure was 3.4 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Properties of Configuration**} & **Value** & **Deviation [dB]** \\ \hline \multirow{6}{*}{Properties of Lead} & \multirow{2}{*}{Insulation Permittivity} & 2 & 0.43 \\  & & 4 & 0.47 \\ \cline{2-4}  & & 5 & 0.41 \\ \cline{2-4}  & & 15 & 0.32 \\ \cline{2-4}  & & 0.99 & 0.34 \\ \cline{2-4}  & & 0.81 & 0.37 \\ \cline{2-4}  & & 44 & 0.12 \\ \cline{2-4}  & & 36 & 0.10 \\ \hline \multirow{6}{*}{Properties of Tissue} & \multirow{2}{*}{Dielectric Permittivity} & 85.8 & 0.33 \\  & & 70.2 & 0.29 \\ \cline{1-1} \cline{2-4}  & & 0.517 & 0.40 \\ \cline{1-1} \cline{2-4}  & & 0.423 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The deviation in dB of the deposited power at leads with different properties and surrounding tissue as assessed by the full-wave simulations and the proposed approach.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 4 Experimental Results and Simulation Analysis
### BJDST Working Conditions  
The BJDST condition is a lithium-ion battery performance test method based on DST (dynamic stress test) conditions, but the test current of the BJDST condition changes more drastically and is more complex than the DST condition. In this condition, a test cycle of 916 s is used to cycle the test until the end of the test. The current variation in the BJDST condition is shown in Figure 10.  
The BJDST case simulates the changes in driving speed and acceleration on a typical urban road in Beijing. These modes include various driving maneuvers such as starting, accelerating, driving at a constant speed, decelerating, and stopping to reflect the diverse behaviors of real-world road driving. The scenario also takes into account the traffic conditions in Beijing, including the distribution of traffic congestion, parking waiting times, and travel distances. These factors have a direct impact on the energy consumption and range performance of electric vehicles in urban environments.  
In this experiment, the initial SoC value of the battery starts from 0.8 and ends when the battery is discharged to SoC 0. After loading the battery with different simulated charging and discharging currents, the SoC and open circuit voltage of the battery change continuously, and the open circuit voltage decreases from 3.9207 V at the initial time to 3.1786 V when the SoC is 0. The initial value of the Kalman filter algorithm under this condition is set, and the initial value of the parameter in the LM algorithm is still set to 0.15. The SoC results predicted by the LM-IEKF algorithm and the error of the algorithm are given below.  
Figures 11 and 12 show the SoC prediction results and the SoC estimation error of the BJDST condition. The LM-IEKF algorithm still maintains the characteristics of a rapid convergence from the initial value to the true value. In the face of the BJDST condition with obvious current fluctuations, the EKF algorithm and IKEF algorithm face the challenge of large changes in the covariance matrix, resulting in large fluctuations in the error of their estimation results. As shown in Table 3, the maximum error of SoC estimated by the LM-IEKF algorithm is only 1.9032%, and the average error is no more than 0.562%, which is well adapted to the working condition, with an obvious current fluctuation range.  
Figure 10: Current variations for the BJDST operating condition.