esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Model Validation and Optimization  
Following the development of the AI model aimed at identifying the best combination of RAP and FBC for foamed bitumen bound asphalt mixtures, the subsequent vital phase involves validating and optimizing the model. This step is essential to confirm the model's predictive accuracy and its capability to suggest mixed designs that fulfill the necessary criteria for rutting resistance (HWTT) and tensile strength (ITS). It is imperative that the recommendations generated by the AI model undergo thorough testing against actual data, allowing for ongoing adjustments and enhancements.  
Figure 4 compares the performance of various AI models that use real experimental test results as inputs. The errors shown represent the performance of each model, helping to determine the most accurate one that can best fit this dataset, considering the complex relationship between foamed bitumen parameters. This selection process allows for deeper training on the dataset, which includes experimental tests with varying RAP content (0-100%) and foamed bitumen content (1.5-4%). After training the model, boundary limits were set for RAP and FBC to refine the predictions. The goal is to identify the best-performing model for further refinement and practical application. All models achieved a high R\({}^{2}\) above 0.83 with a small mean squared error (MSE) and a mean absolute error (MAE). Based on the metrics, the Random Forest Regressor is the best-performing model. However, in this comparison, it showed a performance similar to that of SVR, but RF performs well with large, high-dimensional datasets and can effectively handle non-linearity, noise, and missing data. Unlike SVR, it is less sensitive to feature scaling.  
Figure 4: \(Cont.\)
## 2 Literature Review  
### Literature on Superplasticizers  
Researchers are currently analyzing how superplasticizers interact with various components of UHPC to address potential challenges arising from inadequate interaction. This includes studying the behavior of superplasticizers concerning cement particles, fine aggregates, and other additives. Most research on superplasticizer use in UHPC focuses on third-generation products. Second-generation products have been overshadowed by third-generation ones, leading to less attention on their potential. Lowering costs and meeting regulatory requirements with second-generation products are positive signs for incorporating UHPC as a target.  
[PERSON] et al. [38] investigated the preferential adsorption of two superplasticizers (one containing only carboxyl groups and one containing both carboxyl and phosphate groups) on cement and silica fume. The research indicated that the carboxyl-containing PCE demonstrated higher adsorption potential on the cement surface, influencing the flow spread of the cement paste. Additionally, the carboxyl- and phosphate-containing PCE showed a higher affinity for adsorption on silica fume at lower concentrations, while at higher concentrations, it exhibited a strong adsorption capacity on both cement and silica fume. [PERSON] et al. [39] reported that the dispersing efficacy of PCE-type superplasticizers was intrinsically dependent on their chemical composition and justified the significant effect of chemical structure on the rheological properties of the pastes by an exponential correlation. They attributed the retardation effect (which affects concrete's setting time) to two factors: PCE molecules adsorbed onto cement particles and those within the aqueous phase. It was reported that factors like the type and dosage of SP caused chemical shrinkage of paste only in the initial 24 h. [PERSON] et al. [40] found that enhancing the water-to-binder ratio and superplasticizer dosage increased flowability in fresh cement mortar, resulting in reduced yield stress. They noted that adjusting the superplasticizer dosage had no impact on plastic viscosity. According to [PERSON] et al. [41], combining PCE-SP with nano-clay resulted in enhanced flexural toughness (by 45%), increased flexural strength (by 30%), a slight boost in compressive strength, and decreased porosity in UHPC samples. Other studies, like [PERSON] et al. [42] and [PERSON] et al. [43], also found that rheological indices increased and free water decreased when nano or micro silica was combined with a superplasticizer [44; 45]. [PERSON] et al. [46] evaluated the efficacy of three distinct superplasticizer variants (namely SNF, SMF, and PCE) incorporated into UHPC at dosages of 0%, 0.5%, 1%, 1.5%, and 2%. The findings indicated that the mixtures containing PCE-SP outperformed those with sulfonated superplasticizers in terms of water reduction capacity, retention of workability, and mechanical strength.
# Changed Detection Based on Patch Robust Principal Component Analysis
## 3 Methodology
### Patch Low-Rank Decomposition Modeling  
In (6), the whole frame is taken as one column of the image matrix, and the great similarity of the columns makes the image sequence become a one-rank matrix. For the P-RPCA method, the parameter \(t\) is set as the length of each square window which means that the window has a size of \(t\times t\). For the image of size \(m\times n\), it is divided into many patches by the window of size \(t\times t\), and the number of patches in each frame is given by (10).  
\[T=[\frac{m}{t}]\times[\frac{\mu}{t}] \tag{10}\]  
where \(T\) is the number of the patches and \([\cdot]\) indicates a rounded-up operation.  
If the size of the image cannot be exactly divided, we should pad the boundary with a gray value of 0 so that the size can be exactly divided. The size of the image can also be adjusted by scaling the image. The segmented image patch sequences are concatenated together successively in the time dimension. Figure 2 illustrates the divisionand concatenation. Hence, the original sequence with \(N\) frames is turned into an image sequence with \(T\times N\) frames.  
Similarly, each frame of the new image sequence is vectorized as a column, and then the sequence is converted into an image matrix denoted by \(D^{\prime}\) with a size of \(t^{2}\times TN\). Then, the low-rank decomposition can be used to obtain the sparse change regions of the sequence. The augmented Lagrange multiplier (ALM) algorithm [31] can also give the solution method of the optimization problem in (11):  
\[\arg\min_{A^{\prime},E^{\prime}}\
orm{A^{\prime}}_{*}+\lambda\
orm{E^{\prime}} _{1}+\frac{\mu}{2}\
orm{D^{\prime}-A^{\prime}-E^{\prime}}_{F}^{2} \tag{11}\]  
Once the low-rank component \(A^{\prime}\) and the sparse component \(E^{\prime}\) of \(D^{\prime}\) are obtained, one can recover the background of the original image and sparse change regions according to original stacking order. On the one hand, P-RPCA turns the local illumination variations inside the image into that between the successive frames which can be filtered by the low-rank decomposition into the reconstruction error. Hence, compared with RPCA, P-RPCA is better at dealing with local illumination variations [10].  
On the other hand, the rank of the image patch matrix becomes higher than the original image matrix since the patches in the same image are strongly dissimilar, which makes increases rank after division and concatenation. As the number of the consistent background becomes at least the number of patches, the rank of the image matrix increased to \(T\) from rank one. For the optimization of (11), the kernel norm, the first term on the right side of the equation, becomes significantly bigger than before, which forces the second term of sparsity and the third term of the reconstruction error to be smaller. Hence, P-RPCA can reconstruct a more accurate background and clear change regions at the same time than RPCA.
## 5 Conclusions  
In this paper, we proposed a new formula using IRA output to calculate the relative position of the closest point. Since the attitude and velocity vector of aircraft are different due to the wind when the aircraft is actually flying, the existing relative position calculation method using attitude becomes inaccurate. We derived the \"effective look angle\" equation using IRA's look angle calculation principle and proposed a formula to calculate the relative position of the closest terrain point by using the velocity vector of the aircraft and the effective look angle. The proposed formula was verified with real data from actual flight. The flight test results show that the positions of the closest points calculated using the conventional method and the proposed method are different because of wind effect. The difference of the relative position of the closest point calculated using each equation was largest at the flight height of 5 km which had largest influence of wind blowing from the side. The accuracy of the proposed formula and conventional formula were evaluated by TRN simulation. The TRN simulation results indicate that the proposed formula calculates the closest points more accurately than the conventional formula. Using the proposed method, the performance degradation of TRN can be prevented even in high altitude and windy environments.  
Conceptualization, C.-K.S.; Data curation, J.L.; Project administration, S.W.L.; Supervision, S.J.L. and M.-J.Y.; Visualization, J.O.; Writing-original draft, J.O.; Writing-review & editing, C.-K.S. and J.L.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 10 Conclusions  
The MHC method improves the traditional IP protocol. Using the improved MHC method to replace the traditional IP protocol can ensure that the network layer transmission has a security and reliability mechanism and the traceability of the message. The message hash chain can ensure the integrity, immutability, and synchronization of the transmitted data. At the same time, the use of chain signature and authentication technology can significantly reduce the overhead of signature authentication and improve the efficiency of secure transmission of message sequences. The MHC method has higher requirements on the reliability of the transmission process, so we design packet loss retransmission, error detection and correction, and chain synchronization for the communication process. Finally, the experimental results show that the MHC method adds an endogenous authentication mechanism and a reliable mechanism compared with the traditional transmission model without an authentication mechanism in the general software implementation. The MHC method can guarantee the non-repudiation of all previous messages through one signature. The transmission efficiency of the method is higher than that of the AH protocol and the ESP protocol of IPSec. Under the condition of ensuring the confidentiality of the transmitted message, the method has higher transmission efficiency than the ESP protocol. The method can make the transmission process more reliable and provide chain synchronization services for the transmission process.  
In the future, we will further explore the impact of different hash functions and cryptographic algorithms on the efficiency and security of MHC methods. At the same time, we will also improve the network protocol stack based on the MHC method and try to implement the chip-level MHC method. Applying the MHC method to broadcast, Internet of Vehicles, aerospace, and other application scenarios is also the focus of our subsequent work.  
Conceptualization, M.H. and W.J.; methodology, W.J.; software, M.H.; validation, M.H. and W.J.; formal analysis, M.H.; investigation, W.J.; resources, W.J.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H.; visualization, M.H.; supervision, W.J.; project administration, W.J.; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China grant number 2018 YFB1800100, and Scientific and Technological Innovation Service Capacity Building-Cyberspace Security Discipline Innovation Platform Construction Fund Project grant number 77F1910917.  
Not applicable.  
The authors declare no conflict of interest.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
## 4 Design of Phased Antenna Array  
Once the principles of the phase shifter is validated by simulations as well as by measurements. A continuous beam-steerable antenna array operating at 40 GHz is designed and fabricated by inserting this phase shifter into the feeding network. The antenna array is composed of two elements fed by the phase shifter and the dimension after optimization of one element is about 3.4 \(\times\) 2.4 mm\({}^{2}\), with optimized spacing between elements of 0.47 mm. Figure 9 shows the fabricated prototype of the proposed phased array antenna. This antenna array is also fabricated on a Rogers RT5880 substrate, and the size of the board is about 20 \(\times\) 16 mm\({}^{2}\).  
Experiments are being carried out to measure the performances of the fabricated phased array and are compared to the simulation results, as can be seen from  
Figure 8: Simulated (continuous line) and measured (dotted line) \(\mid\)S21\(\mid\) versus frequency.  
Figure 9: Photograph of fabricated phased antenna array.  
Figures 10 and 11. Figure 10 illustrates the simulated and measured reflection coefficients of the phased array antenna. A good agreement between them can be clearly observed and good impedance matching is achieved over the band of 40.50-41.70 GHz.  
The total scan patterns of the phased antenna array at the two extreme voltages \(-2\) V and \(2\) V and at \(-1\) V are shown in Figure 11. By changing the DC bias, beam steering can be achieved since the change in this DC bias has a direct effect on the change in the phase difference between the two radiation elements. Furthermore, the main beam can be tuned continuously to different directions between \(0^{\circ}\) to \(25^{\circ}\) by adjusting the phase differences of each antenna element through the phase shifter. A side-lobe level on the azimuth plane around \(-8\) dB is observed for a Vg of \(-2\) V in the measurement, which is a little bit higher compared to the simulated one, which is about \(-9\) dB. This side lobe has occurred at \(-2\) V, which is an extreme DC bias, to achieve the C\({}_{\rm Egmax}\) corresponding to a beam scanned at  
Figure 11: Normalized simulated (continuous line) and measured (dotted line) beam pattern in azimuth at 41 GHz for Vg of 2 V, \(-1\) V, and \(-2\) V.  
Figure 10: Simulated and measured reflection coefficient for Vg of 2 V and \(-2\) V.  
\(25^{\circ}\) (at C\({}_{\rm Eqmax}\)). This means that as the scanning angle becomes further from \(0^{\circ}\) (at C\({}_{\rm Eqmin}\)), the side lobe appears to become higher. In addition, this can be due to the diffraction of the limited size of the ground conductor and also may be due to some perturbation caused by the PADs used to connect the two varactors using the wire bonding technique.  
Thanks to the already developed GaN HEMT varactor small-signal model, the proposed phased antenna array successfully works as was expected in the EM simulations.
## 3 Results and Discussion
### Photovoltaic Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Deposition Time** & \multicolumn{3}{c}{**3 min**} & \multicolumn{3}{c}{**5 min**} \\ \hline
**Voltages (V)** & **60** & **70** & **80** & **60** & **70** & **80** \\ \hline Jsc (mA/cm\({}^{2}\)) & 0.65 & 1.30 & 0.40 & 1.80 & 1.94 & 2.27 \\ \hline FF & 0.29 & 0.48 & 0.47 & 0.19 & 0.51 & 0.40 \\ \hline Efficiency (\%) & 0.12 & 0.40 & 0.12 & 0.20 & 0.64 & 0.55 \\ \hline Voc (V) & 0.64 & 0.66 & 0.63 & 0.66 & 0.65 & 0.61 \\ \hline IPCE (\%) & 1.28 & 2.57 & 0.80 & 3.52 & 3.80 & 4.48 \\ \hline Rsh (\(\Omega/\mathrm{cm}^{2}\)) & 1222 & 194.7 & 6333.3 & 1360 & 1400 & 750 \\ \hline Rs (\(\Omega/\mathrm{cm}^{2}\)) & 750 & 168 & 476.2 & 250 & 108.4 & 163.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the photovoltaic tests. Figure 6: J-V data of the EPD films at (**a**) 3 min (**b**) 5 min deposition time. The fill factor (FF) was calculated according to Equation (6) and efficiency \(\eta\) according to Equation (7) [43]:  
\[\text{FF}=(\text{J}_{\text{m}}\,\text{V}_{\text{m}})/(\text{J}_{\text{sc}}\,\text{V }_{\text{oc}}), \tag{6}\]  
\[\eta\,(\%)=((\text{FF}\,\text{J}_{\text{sc}}\,\text{V}_{\text{on}})/\text{Pin}) \times 100, \tag{7}\]  
where \(\text{J}_{\text{m}}\) (mA/cm\({}^{2}\)) and \(\text{V}_{\text{m}}\) (V) are the maximum current and voltage, respectively. \(\text{J}_{\text{sc}}\) is the short current (mA/cm\({}^{2}\)), \(\text{V}_{\text{oc}}\) (V) is the open circuit voltage and Pin is the input power of 100 mW/cm\({}^{2}\). The series resistance (Rs) and shunt resistance (Rsh) were calculated according to [44]. Photovoltaic values for the ZnO films are comparable to those obtained by [PERSON] et al. [30], who used a multistep EPD method for TiO\({}_{2}\), achieving an efficiency of 1.28%, a fill Factor (FF) of 0.67, a short current density (Jsc) of 2.69 mA/cm\({}^{2}\), and an open-circuit voltage (Voc) of 0.67 V. [PERSON] et al. [32] used EPD of graphene on TiO\({}_{2}\), achieving a 2% efficiency after 20 min of deposition and a 6% efficiency for 5 min of EPD. However, the photovoltaic parameters decreased with a longer deposition time. [PERSON] et al. [42] achieved an IPCE of 0.6% using ZnO nanowires in DSSCs. [PERSON] et al. [45] obtained efficiencies ranging from 0.4 to 0.74% by employing ZnO nanorods in DSSCs, indicating that the formation of complexes of Zn\({}^{2+}\)/dyes contributed to a reduction in the overall electron injection efficiency.
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 3 Mathematical Formulation  
### Governing Equation  
The RANS equation was used for the numerical simulations. For the three-dimensional incompressible flows, the continuity and momentum equations are written as follows:  
\[\frac{\partial\left(\rho\overline{u}_{i}\right)}{\partial x}=0 \tag{2}\]  
\[\rho\frac{\partial\overline{u}_{i}}{\partial t}+\rho\overline{u}_{j}\frac{ \partial\overline{u}_{i}}{\partial x_{j}}=\rho\overline{f}_{i}-\frac{\partial \overline{p}}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}(\mu\frac{ \partial\overline{u}_{i}}{\partial x_{j}}-\rho\overline{u_{i}^{\prime}u_{i}^{ \prime}}) \tag{3}\]  
where \(\overline{p}\) is the mean pressure, and \(\rho\) and \(\mu\) define the density and dynamic viscosity of fluid, respectively. \(\overline{u}_{i}\) is the averaged velocity vector and \(\overline{u^{\prime}_{i}u^{\prime}_{i}}\) denotes the Reynolds stresses.  
The CFD simulations were carried out in the commercial software STAR-CCM+. The finite volume method (FVM) was used to discretize the RANS equations. The Realizable k-\(\epsilon\) model was applied as the turbulence model. The convective terms were discretized using a second-order upwind scheme, the diffusion terms were discretized by a central differences scheme, and the temporal terms were discretized by adopting a second-order backward Euler scheme. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm was applied to deal with pressure-velocity coupling. The volume of fluid (VOF) method was used to capture the evolution of free surface for the two-phase flow. The all-Y+ treatment was employed for near wall modeling, which applies wall function for coarse meshes and resolves viscous sub-layer for fine meshes. The dynamic fluid body interaction (DFBI) module combined with overset mesh was applied to consider ship motions in pitch and heave degree of freedom.
## 2 Theoretical Considerations about Frequency Mixing
The segments rendered without color correspond to those detuned to provide a proper longitudinal dispersion. The lower part of the plot shows the actual configuration in terms of the undulator K parameter, including corrections. The corresponding undulator gaps and phase shifter gaps and offsets are also reported. The chicane was set to a delay of 0.7 fs. where we got rid of minus signs in the arguments and in the order of the Bessel functions since we consider modulus of the product. This bunching is then amplified in the output radiator. A thorough mathematical description of our system should actually include finite SASE bandwidths around the initial frequencies \(\omega_{1}\) and \(\omega_{2}\). However, such a description, as well as considerations on the statistics of the mixing process go beyond the scope of this paper, which is to report about experimental results instead. They will be developed in a separate, forthcoming work. Here we only limit ourselves to a few additional remarks. First, we remind that in the linear regime, the SASE process can be modelled as a Gaussian process. Therefore, the arguments of the Bessel functions in Equation (2) must follow a Rayleigh distribution because they are proportional to the field amplitude through the energy modulations \(A_{1}\) and \(A_{2}\). As a result, \(b_{FM}\) should be considered as a random variable too. Second, we note that in the simple case of a cold beam, the exponential function in Equation (2) becomes unity. The probability density function for \(b_{FM}\) can be easily obtained numerically by calculating the product of the Bessel functions in Equation (2). As their arguments, we use two large sets of independent random variates obtained from the Rayleigh distribution. The probability density function calculated in this way depends on the mean value (which we choose equal for both Rayleigh distributions). It is then straightforward to find the average value for \(b_{FM}\) and to optimize it. We found that the maximum bunching amounts to about 20% and is obtained for a mean value of \((K-1)A_{1,2}B\simeq 1.5\) slightly below the maximum of \(J_{1}\), which is for \((K-1)A_{1,2}B\simeq 1.8\). Finally, we note that while the SASE process in the linear regime follows Gaussian statistics, the frequency mixed signal obviously deviates from it, being the result of a non-linear operation.