esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Phenomenological Model for RC Circular Columns
## 3 The ModIMK Model and Parameters' Calibration
### The Model Parameters' Calibration  
The backbone curve reflects the component's monotonic loading process, so the model parameters cannot be obtained directly by analyzing the test hysteresis curve. The component's deterioration behavior is determined by the energy dissipation throughout the loading process, and the deterioration coefficients cannot be obtained through simple calculations. Only by the numerical simulation of the test process and by analyzing the fitting situation between the test curve and the simulation curve can the model parameters of the component be finally determined. Therefore, a finite element model needs to be created for each component for the numerical analysis.  
The peak-orientated ModIMK model is suitable for RC circular columns where flexural deformation is dominant. According to the analysis of reference [26], six model parameters, i.e., \(M_{y},\theta_{y},M_{c}/M_{y},\theta_{p},\theta_{pc},\Lambda\), can determine the hysteretic model of a component.  
According to reference [26], the analytical model was developed, and the model parameters were calibrated using the OpenSees platform. Due to the fact that the plastic hinge can only appear after a component yields, the analytical model differs from the real test, and in order to simulate this more accurately, the analytical model has to be stiffness-corrected [27; 28; 29].  
The model parameters were calibrated in the positive and negative loading directions, and then their average values were obtained, which are listed in Table 1. The empirical prediction equations were created on the basis of the statistical regression of the average values.  
Figure 1: Backbone curve of ModIMK model.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Description** & **Parameter** & **Description** \\ \hline \(K_{0}\) & elastic stiffness & \(M_{y}\) & yield moment \\ \(\theta_{p}\) & chord rotation of strain-hardening stage in & \(K_{s}\) & hardening stiffness \\ \(M_{r}\) & residual moment in backbone curve & \(K_{c}\) & post-capping stiffness \\ \(\theta_{y}\) & chord rotation of elastic stage & \(\theta_{u}\) & ultimate rotation capacity \\ \(M_{c}\) & capping moment & \(\theta_{pc}\) & chord rotation of softening stage in backbone curve \\ \(c\) & the deterioration exponent & \(\Lambda\) & cyclic deterioration parameter \\ \hline \hline \end{tabular}
\end{table}
Table 3: Meaning of model parameters.
## 4 Case Study and Results
### Energy Consumption Results
#### 4.2.3 Scenario 3--Aerodynamic Disturbance  
In Scenario 3, the model includes thrust corrections from CFD (Section 3.4). This results in slightly different profiles, particularly in vertical phases and hovering (Figure 5 and Table 7).
## 3 Results and Discussions
### Thrust Forces  
Figure 8 shows the variation of thrust force with ultrasonic amplitude through the two materials during the reaming of CFRP/Ti stacks by UVR. In the experiment, the tool speed was 600 rpm, and the feed speed was 0.41 mm/s (CFRP) and Ti (0.16 mm/s). During the reaming of the titanium alloy, the thrust force decreases with the increase of ultrasonic amplitude and reaches its minimum value when the amplitude reaches 6 \(\upmu\)m, which is 19 N less than the thrust forces of CR. In the reaming of CFRP, the thrust forces also decrease with the increase of ultrasonic amplitude and reach their minimum value when the amplitude reaches 6 \(\upmu\)m, which is 4 N less than thrust forces of CR. The average thrust forces in UVR decreased by 57% (Ti) and 40% (CFRP), respectively, compared to CR. Therefore, the larger the amplitude, the smaller the axial force. This is consistent with the analysis in Section 3.1.  
According to [23],  
\[F_{\text{mcr}}=k_{\text{cr}}v_{\text{a}}a_{\text{p}} \tag{7}\]  
\[F_{\text{m}ar}=\frac{t_{\text{e}}-t_{\text{b}}}{T}k_{\text{ar}}v_{\text{a}}a_ {\text{p}} \tag{8}\]  
where \(k_{\text{cr}}\) and \(k_{\text{ar}}\) are the specific resistances in the axial thrust direction for CR and UVR, respectively. \(a_{\text{p}}\) is the depth of cut, the thrust force in CR is \(F_{\text{mcr}}\), the thrust force in UVR is \(F_{\text{m}ar}\), and the vibration period of UVR is \(T\). Frictional direction between the rake face of the tool and the chip is reversed at \(t_{\text{i}}\). The UVR starts at \(t_{\text{b}}\) and ends at \(t_{\text{e}}\). Therefore, \((t_{\text{e}}-t_{\text{b}})/T<1\), \(F_{\text{m}ar}<F_{\text{mcr}}\), yielding smaller thrust forces for UVR.  
Figure 9 depicts the variation in thrust force with respect to tool speed. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. The thrust forces when machining CFRP and titanium alloys by UVR are lower than CR. When reaming the titanium alloy, the trend of thrust force reduction with the increase of tool speed increases and then stabilizes, which means that when the speed is low, the thrust forces are larger and the effect of ultrasonic in reducing thrust force is not obvious. As the speed increases, the ultrasonic effect becomes more and more obvious, and when the speed reaches 1400 rpm, the thrust forces of CR no longer decrease and become stable. At that speed, the effect of ultrasonic thrust force reduction is also stable. The higher the rotation speed, the smaller the reamer feed per tooth, the weaker the suppression of ultrasound by sidewall extrusion, and the smaller the thrust force, indicating that the ultrasound effect is better.
# Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations
## 4 Performance Optimization and Testing
### Computational Time Comparison Tests
#### 4.5.1 CPU Single-Core and Multi-Core Comparison  
The first is to test the acceleration performance of the CPU using Pthread compared to the single-core serial mode. In our program, Pthread is used to launch 28 threads to be responsible for some calculations, and its acceleration performance is shown in the Figure 14. The horizontal axis is the calculation scale. We first fix the length to expand the width, and then fix the width to expand the length. It can be seen that no matter what the expansion is, the speedup is stable at around 3. Therefore, using Pthread can not only achieve more complex thread operations, but also obtain stable acceleration effects.  
Figure 13: Optimized heterogeneous parallel strong scaling test. In the first section, we mentioned that [PERSON] et al. also proposed a computing framework for solving PDE using GPU. However, according to our calculation method of parallel efficiency, the parallel efficiency calculated according to the speedup results provided by them is not ideal. For example, the speedup ratio calculated based on the grid size of 1000 (10 \(\times\) 10 \(\times\) 10) in their article, the speedup ratio also increases with the increase of the grid size, but the parallel efficiency is only below 30%.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 4 Efficient Communication Model for an SPS with Multiple Data Consumers
### Positioning Data Collators for Reliable and Faster Data Collection  
The BR itself can act as a data collecting point as RPL has a reliable DAG path to the root node. This introduces the funneling effect where forwarders close to the BR experience huge traffic. To reduce this effect, many researchers propose using multiple root nodes and collating the data outside the RPL network [42, 43]. However, this deprives the on-site data consumers from directly accessing the occupancy data within the network and adds a dependency to the Internet connection besides increasing the delay in acquiring the data. The multi-sink approaches are complex with additional systems and modifications to the RPL control messages. As an alternate, multiple data servers are proposed in this work. It is essential that the data servers are stationed close to a BR so that a data server can reach another through the BR. This reduces the funneling effect and requires no complex improvisations to the RPL protocol. As the data servers are en route to BR, all the data-producing nodes already have an optimal path to reach the data collator. To illustrate this point, the third model has multiple data collators away from the BR.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 2 Image Sampling Methods
### Insights  
Optical sampling methods should be selected in terms of micromanipulation tasks. Table 1 summarizes the characters of each method. Generally, resolution is key to obtaining high-quality information. For intracellular organelle micromanipulation, imaging resolution should reach at least the micrometer level, which is achievable with most methods. Micromanipulation is a multistep task through which  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Lateral** \\ **Resolution** \\ **Level** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Axial** \\ **Resolution** \\ **Level** \\ \end{tabular} } & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Limitations**} & \multirow{2}{*}{**References**} \\  & & & & & \\ \hline \multirow{2}{*}{CFM} & \(\sim\)200 nm & \(\sim\)500 nm & out-of-focus blurs & high light intensity; slow & \multirow{2}{*}{\begin{tabular}{c} [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55] \\ \end{tabular} } \\  & & & & & \\ \hline \multirow{2}{*}{TPFM} & \(\sim\)500 nm & \(\sim\)1 μm & out-of-focus blurs & low sampling rate; & \multirow{2}{*}{\begin{tabular}{c} [56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69] \\ \end{tabular} } \\  & & & & penetration depth; & \multirow{2}{*}{\begin{tabular}{c} [56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69] \\ \end{tabular} } \\  & & & & long-term measurement & purchase expenses & \\ \hline \multirow{2}{*}{LSFM} & \(\sim\)300 nm & \(>\)1 μm & out-of-focus blurs & low axial resolution; & \multirow{2}{*}{\
\begin{tabular}{c} [70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83] \\ \end{tabular} } \\  & & & & & \\ \hline \multirow{2}{*}{PAM} & \(\sim\)300 nm & \(\sim\)2 μm & out-of-focus blurs & slow sampling speed; & \multirow{2}{*}{
\begin{tabular}{c} [84, 85, 86, 87, 88, 89, 80, 82, 83, 84, 85, 86, 87, 88, 88, 89, 80, 82, 84, 85, 87, 88, 86, 89, 80, 82, 87, 88, 82, 83, 85, 84, 86, 87, 88, 89, 81, 82, 85, 86, 87, 88, 82, 89, 83, 86, 87, 88, 89, 82, 85, 8 sampling is a single operation procedure, so the adopted methods should also be evaluated in terms of integration with robot-aided manipulation tools and automation control and programming. Furthermore, the possible photobleaching and phototoxic effects should be considered, and correct instruments with appropriate measurement parameters should be chosen. Accessibility and purchase costs should also be taken into consideration.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Homomorphic Encryption  
This big integer is then encrypted using homomorphic encryption, generating the ciphertext E(111011001000011001001110).
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
## 5 Conclusions  
This paper presented a semi-supervised learning approach for fault detection and state prediction in gearboxes. The experimental setup, measurements, and data preparation were presented. Moreover, the architecture of the proposed models was shown along with semi-supervised training and validation. Based on the performed experiment, the following conclusions can be drawn:  
* The proposed methods based on autoencoders were efficient and could detect even the initial state of pitting formation, which may be difficult with the aid of signal analysis in the time and frequency domain;
* The best model (AE+CH) showed very high effectiveness (100%) in failure detection (true positive) and 98.9% in normal state prediction (true negative), which resulted in a very high F1-measure (0.99);
* Vibration excitation due to pitting was observed in a higher order of harmonics;
* The latent space analysis revealed a generalized perspective on the gear wear--the measurements drifted in a specific direction in the latent space with the progress of the gearbox damage;
* Autoencoder outperformed the generative adversarial network in terms of generalization on wear prediction.  
Future work could focus on the use of some interesting autoencoder abilities to extract the features of vibrational signals that may be beneficial in detecting failure based on the frequency spectrum. This can therefore lead to a better understanding of the influence of surface damage on gearbox vibration and noise excitation.  
Conceptualization, M.B.; Methodology, M.B. and B.K.; Software, M.B.; Validation, M.B.; Formal analysis, M.B. and B.K.; Investigation, M.B.; Resources, M.B.; Data curation, B.K.; Writing--original draft preparation, M.B. and B.K.; Writing--review and editing, M.B. and B.K.; Visualization, M.B.; Supervision, B.K.; Project administration, M.B.; Funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding and the APC was funded by the Dean of the Faculty of Mechanical Engineering and Aeronautics of Rzeszow University of Technology.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 5 Conclusions  
The effects of CP geometric parameters and jet \(Re\) number on the cooling performance and \(\dot{W}\) requirement of a novel CMJICS were analyzed by 3D RANS simulations for respectively seven and five values of CP height and diameter at jet \(Re\) numbers ranging between 5000 and 20,000. \(PEC\) values of the novel CMJICS evaluated by CFD simulations clearly indicate its excellent thermo-economic performance. Accordingly,  
* \(\dot{Q}\) increases with both \(H_{p}/D\) and jet \(Re\) number. The maximum heat transfer enhancement with respect to the flat target surface of 65% is obtained for \(D_{p}/D\) and \(H_{p}/D\) of 3.5 and 4.5, respectively. The \(\dot{Q}/\dot{Q}_{o}\) increases with \(H_{p}/D\) while decreasing with a jet \(Re\) number.
* \(\dot{Q}\) increases with \(D_{p}/D\) and jet \(Re\) number. For \(H_{p}/D\) of 4, and a jet \(Re\) number of 20,000, \(\dot{Q}\) increases by 28.3%, 30.9%, 38.1%, 48.8%, and 54.3%, respectively, for \(D_{p}/D\) of 2, 2.5, 3, 3.5, and 4. The \(\dot{Q}/\dot{Q}_{o}\) increases with \(D_{p}/D\) while decreasing with a jet \(Re\) number.
* The \(\overline{Nu}\) number decreases with increasing \(H_{p}/D\), while increasing with a jet \(Re\) number. The \(\overline{Nu}/\overline{Nu}_{o}\) decreases with increasing \(H_{p}/D\) and jet \(Re\) number. For high \(Re\) number jets impinging on CPs with \(H_{p}/D\) above 4, minor changes in \(\overline{Nu}/\overline{Nu}_{o}\) occur.
* The \(\overline{Nu}\) number decreases with increasing \(D_{p}/D\).
* For \(D_{p}/D\) of 3.5, at any jet \(Re\) number studied, the \(PEC\) increases considerably with \(H_{p}/D\), reaching its peak values of 1.47, 1.45, 1.41, and 1.39 for jet \(Re\) numbers of 5000, 10.000, 15,000, and 20,000 at \(H_{p}/D\) of 4. Greater \(H_{p}/D\) values rapidly reduce the \(PEC\) due to the significantly increased \(\Delta P\) caused by an impingement distance shorter than \(D\).  
\(PEC\) decreases with the increasing jet \(Re\) number for any studied \(H_{p}/D\), except for a jet \(Re\) number of 5000 with \(H_{p}/D\) smaller than 2 due to transitional effects.
* The \(PEC\) of the novel cooling system increases with \(D_{p}/D\), while decreasing with the increasing jet \(Re\) number. For \(H_{p}/D\) of 4, the highest \(PEC\) value of 1.519 is obtained at \(D_{p}/D\) of 4, and a jet \(Re\) number of 10,000.  
Conceptualization, A.H.U. and S.O.U.; methodology, A.H.U. and S.O.U.; formal analysis, A.H.U. and S.O.U.; investigation, A.H.U. and S.O.U.; resources, A.H.U. and S.O.U.; data curation, A.H.U. and S.O.U.; writing-original draft preparation, A.H.U. and S.O.U.; writing-review and editing, A.H.U. and S.O.U.; visualization, A.H.U. and S.O.U.; supervision, S.O.U.; project administration, S.O.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 4 Simulation Analysis
### Simulation Results of Lever Arm Estimation
To evaluate lever arm estimation accuracy and positioning accuracy, we carried out 100 Monte-Carlo simulations, 50 times for each trajectory. The final lever arm estimation results at the end of every test are shown in Figure 5, and Figure 6 shows the final positioning results. In these Figures, the terms \"Trj1\" is the abbreviation for Trajectory 1, \"Trj2\" is the abbreviation for Trajectory 2, and \"RMS\" are the envelopes of root mean square error statistics. As shown in Figures 5 and 6, the final estimation accuracy for the equivalent dynamic lever arm \(\mathbf{L}_{IG}^{b}\) is [0.0104; 0.0110; 0.0178] m (RMS), and the positioning accuracy is [0.0123; 0.0128; 0.0181] m (RMS), which is nearly the same as the positioning accuracy of CDGPS listed in Table 2.  
In manual measurements, the lever arms can be measured with an accuracy of 1 mm level by a laser total station instrument [17], if the phase center of the GPS antenna, the sensing center of the IMU, and the rotating center of the ISP are all marked out without error. It is true that the  
Figure 5: The estimation results of dynamic lever arm \(\mathbf{L}_{IG}^{b}\). Figure 6: The positioning results of SINS/GPS navigation with lever arm automatic estimation. automatic estimation method presented here cannot accomplish such accuracy, which is restricted by the accuracy of CDGPS. However, in many practical situations, it is difficult to accurately locate these three centers, especially for the IMU. As the sensitive centers of the three-axis accelerometers do not coincide, the IMU actually have a virtual sensing center determined by the calibration algorithm of the inner arms [36], and the calibration accuracy is greatly influenced by the accuracy of inertial sensors. If the sensing centers cannot be marked out physically, the accuracy of manual measuring cannot be guaranteed. The purpose of the lever arm compensation is to obtain a higher positioning accuracy of SINS/GPS integration. Assuming that the lever arm has been manually measured with an accuracy of 1 mm, under the same simulation conditions, we obtain the positioning results of the SINS/GPS integration as shown in Figure 7.  
The positioning accuracy of Figure 7 is [0.0071; 0.0068; 0.0078] m (RMS), which is slightly higher than that of the automatic estimation method. Even so, compared with the state-of-art product, POS AV610 [4], which can accomplish typical positioning accuracy of 0.02 m (horizontal)/0.05 m (vertical) by post-processing, the accuracy of the proposed automatic estimation method is still acceptable. Thus, the automatic estimation method is an effective alternative approach for certain situations, in which the measuring conditions are not available or the dynamic lever arm cannot be measured accurately and efficiently.
## 2 Flight Parameter Preprocessing  
### Composition of the Flight Data  
Flight data includes information collected by the flight recording system during the take-off and landing of the aircraft, generally including:  
1. Flight landing and take-off labeling information, such as flight time, and flight subject, etc.;
2. Aircraft attitude parameters, such as altitude, speed, Mach number, latitude and longitude, wind speed, acceleration in all directions, pitch angle, and roll angle;
3. Information on the status of aircraft maneuver parameters, such as the deflection of the rudder and flaps as well as the position of the horizontal stabilization plane;
4. Condition parameters of the power unit, such as high and low-pressure engine speeds, and full engine oil level.  
Figure 2: The flowchart of strain prediction.  
The main focus of this paper is to study the mapping relationship among flight parameters, strains, and loads, with an emphasis on aircraft attitude parameters and navigation position information. To meet the requirements for load identification and strain prediction, 30 common flight attitude parameters are selected for data recording and analysis, including three-axis load factors, three-axis accelerations, aircraft angle of attack, Euler angles, altitude, and speed. The selected flight attitude parameters are listed in Table 1.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 3 Preliminaries
### Mathematical Problems  
ECC is based on the difficulty of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which involves computing the inverse of point multiplication on an elliptic curve (i.e., the discrete logarithm). Compared to traditional RSA encryption, ECC can achieve the same level of security with shorter keys, making it widely adopted in modern cryptography. It is used in digital signatures (e.g., ECDSA), key exchange (e.g., ECDH), and data encryption, forming a crucial part of SSL/TLS, blockchain technology, and various encryption communication protocols. The proposed V2V mutual authentication protocol, EIA, mainly utilizes the ECDLP and ECDH.  
1. Elliptic Curve Discrete Logarithm Problem (ECDLP) The ECDLP is the foundation of elliptic curve cryptography. Given an elliptic curve \(E\) and two points \(P\) and \(Q\) on it, if there exists an integer \(k\) such that \(Q=kP\), then \(k\) is the discrete logarithm of \(Q\) with respect to \(P\). Computing this \(\mathbf{k}\) is known as solving the ECDLP. This problem is considered very difficult, especially over large prime fields, providing the security basis for elliptic curve cryptography.
2. Elliptic-Curve Diffie-Hellman (ECDH) ECDH is a key-exchange protocol based on elliptic curves. It allows two participants, who do not share any prior secret information, to agree on a shared key over an insecure communication channel. The steps are as follows:  
1. Key Generation: each participant chooses a private key \(a\) and \(b\) and computes the corresponding public keys \(A=aP\) and \(B=bP\), where \(P\) is a generator on the elliptic curve.
2. Key exchange: participants exchange their public keys.  
Step 3 Shared Key Computation: Each participant uses the other's public key and their own private key to compute the shared key. The first participant computes \(S=aB\), and the second participant computes \(S^{\prime}=bA\). Since \(S=S^{\prime}=abP\), both participants arrive at the same shared key.