esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Triangular Tracking Results with Variable Amplitude  
Another experiment performed is the triangular reference signal with variable amplitudes that were settled randomly in 25 V and 121 V. Figure 10 shows the repercussions of the error for the considered reference. During the first 4 s, where the amplitude was 25 V, both controllers had a similar demeanour. The PID shows a perceptible shift at the upper target point in 2 s; however, the QCSMC-ANN provided a constant development along this range with the same deviation as the PID. On the other hand, the major difference can be noticeable during 121 V, where the PID behaves similarly to previously analysed triangular signals where fast corrections occur during the slope changes of the reference signal. This effect produced a brief increment of the error amplitude in the analysed controllers, but  
Figure 8: The error generated in 2 cycles of a sine wave reference.  
Figure 9: The control signal in 2 cycles of a sine wave reference.  
the QCSMC-ANN managed to carry this without any transients, a feature produced by the PID.  
As previously, another important feature is the control signal, which is shown in Figure 11. It can be seen that any saturations or sudden corrections that can deteriorate the hardware were developed in the performed experiments. Nevertheless, effects of chattering are analysed in further details in the following section.
## 4 Analysis of Economical Structural Framing  
### Proposed PMR Framing Method  
The factor that has the greatest influence on the increase in cost during prefabrication is material cost, and economic structural framing is required to lower the production cost of PMRs [10]. Therefore, this study proposed five PMR structural framing methods, which are as follows:  
* Type A: frames are in the form of a continuous set of cubes (supporting interval: 1.25 m).
* Type B: frames are in the form of a continuous set of cubes (supporting interval: 2 m).
* Type C: frames in which the ducts are moved to the outside (supporting interval: 2 m).
* Type D: frames that can be directly fixed to the concrete (supporting interval: 2 m).
* Type E: frames with only the lower support retained (supporting interval: 1.5 m).  
Type A is the most commonly used type of frame [10]. In Type A, it is possible to support each member at the bottom and fix a member to the system channel at the top. Because it is in the form of a continuous set of cubes, workers were expected to produce it the fastest in the production stage. It has the most stable form of production, transportation, and construction at the construction site.  
In Type B, the supporting interval of the PMR frame was altered from 1.5 m (which was the supporting interval in Type A) to 2 m. By adjusting the supporting interval, the length of system channels required was expected to reduce. To counteract the deterioration of the PMR unit's overall rigidity following the adjustment of the interval, vertical and horizontal cube-shaped frames were added.  
In Type C, the duct inside the continuous cube-shaped structural frames was removed and made protruding to the outside. This could reduce the input length of system channels. The ducts occupy more space than the pipe and tray members do; hence, the efficiency of inputting the system channel member was expected to improve when the ducts are removed.  
Type D is a structural frame designed to attach PMR modules to concrete. The number of system channels is expected to increase because the members added are cubic. However, it could be applied if it was necessary to respond to the lateral load.  
Type E was completely different from the PMR cubic structural frames; only the bottom-supporting member was retained, and only the essential member was added to the top. The number of system channels was expected to be the least for this design.  
The five designs and photos (captured by the subcontractor, GS Neotek, Seoul, Republic of Korea) of the structural frames proposed in this study are shown in Figures 1-5.  
Figure 1: Type A: Frames in the form of a continuous set of cubes (supporting interval: 1.25 m).  
Figure 4: Type D: Frames that can be directly fixed to the concrete (supporting interval: 2 m).  
Figure 5: Type E: Frames with only the lower support retained (supporting interval: 1.5 m).  
Figure 3: Type C: Frames with the ducts moved to the outside (supporting interval: 2 m).  
Figure 2: Type B: Frames in the form of a continuous set of cubes (supporting interval: 2 m).
## 2 Materials and Methods
### Training Datasets Generation and Site Selection
#### 2.1.3 Sentinel-2A/B Satellite Imagery  
Sentinel-2 is one of the satellites operated by the European Space Agency (ESA) and provides 13 multispectral bands with several different resolutions (10 m, 20 m, and 60 m). The imagery of Sentinel-2 has the highest resolution of 10 m among the current freely available for the general public. Accordingly, Sentinel-2 was selected for the study because it can provide much richer information than any other free satellite images. A short revisit period of five days is another strength of the Sentinel-2 imagery. Initially, the revisit period was ten days, but two satellites named Sentinel-2A and Sentinel-2B take images alternately, which reduces the revisit period to 5 days.  
Sentinel-2 provides two types of images: (1) the L1C product, a top of atmosphere (TOA) reflectance image and (2) the L2A product, a bottom of atmosphere (BOA) reflectance image. The L2A product can overcome a significant difference in reflectivity, which varies for different acquisition times. Because aerial images are acquired at a much lower altitude than satellite images, it is better to use images with atmospheric correction. Because ESA provides the L1C product for images over the study area from 2018, all experimental images were converted to L2A through the Sen2 cor tool of the Sentinel application platform (SNAP) software [9; 13]. Twelve images (four 10 m, six 20 m, and two 60 m) with different spectral bands ranging from visible wavelength to SWIR were acquired. In some land classification studies, 60 m resolution images are not used because they are primarily for atmospheric correction [14; 15]. However, we tested our approach with and without 60 m resolution imagery to consider whether additional atmospheric information is useful for training input images.  
For matching the Sentinel-2 images acquired at the same time interval as the aerial orthoimages, data were searched through the Copernicus website, where the Sentinel series took all provided images [16]. We obtained both Sentinel-2A and 2B sensor images, which contain less cloud coverage, from the website. Searched images used in this research are listed in Table 1, and only band 2 images are depicted in Figure 3. All 10 m and 20 m images were used in training as defaults, with 60 m as optional. Because the datasets are acquired simultaneously with the aerial orthoimages, it was assumed that there were no significant topographic changes during the short period. Accordingly, listed datasets are used for all the following experiments.  
\begin{table}
\begin{tabular}{c c} \hline
**Platform** & **Sensing Start Time** \\ \hline \multirow{3}{*}{Sentinel-2A} & 2018/04/18 02:16:01 \\ \cline{2-3}  & 2018/04/28 02:16:11 \\ \cline{2-3}  & 2018/05/28 02:16:51 \\ \hline Sentinel-2B & 2018/05/23 02:16:49 \\ \hline Total & 4 images \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 sensing start times used for the research.
## 3 Results and Discussion
### Antibacterial Micromolded Scaffolds
Agar tests showed the presence of inhibition halos, which confirmed the antibacterial activity, only when TCS loaded specimens were assayed (see Figure 15b) for an _E. coli_ culture). Figure 16 compares the cell growth of MDCK cells on a non-porous PLA specimen, a PLA scaffold, and the TCS loaded PLA scaffold. Specifically, Figure 16a shows that cell viability was similar for the different samples (e.g., control well without specimens and wells containing the specimens). These results allow discarding the potential toxicity of TCS and are coherent with the potential use of TCS for sanitizing the specimens, in a similar way as used in different hygiene products (e.g., soaps, deodorants, toothpastes, and others). Figure 16b shows that the number of cells grown on the porous micro-pieces, with and without TCS, were similar but significantly higher (increase by about 40-60%) than observed for specimens without pores (\(p<0.05\)). The reduction in the size of cells in the porous specimens can be a morphological evidence of the increase in the number of cells (Figure 16c). The ability of MDCK cells to colonize tubular channels is demonstrated by different evidences related to their mobility and adherent growth [55; 56; 57]. Note that pores in the scaffolds remained open but covered by cells (see asterisks in Figure 16c). Finally, the cells proliferated similarly in all the cases, confirming the biocompatibility of processed PLA scaffolds. Cell growth percentages demonstrate the increase of cell proliferation in the porous specimens. Moreover, the presence of the TCS has not a cytotoxic effect and as antibacterial drug could prevent bacterial contamination of the micromolded specimen. Figure 15: **(a)**_S. epidermidis_ (dashed lines) and _E. coli_ (solid lines) bacterial growth profiles in presence of non-porous PLA control), PLA scaffold and PLA scaffold loaded with 6 wt-% of TCS. **(b)** Agar test for _E. coli_ bacterium using PLA scaffolds with and without the TCS antibacterial drug.
## 6 Experimental Verification
### Load Disturbance Tests  
Suppression of high-frequency harmonics can also improve the anti-disturbance performance of HPMSM. To illustrate this point, load disturbance experiments are implemented. HPMSM runs steadily at 8000 rpm under no load, and then suddenly increases the load by 0.5 N\(\cdot\)m to observe the change of the rotor speed. The comparison results are shown in Figure 22. In Figure 22a, the speed drops by 200 rpm after loading, and it takes 0.3 s for HPMSM to recover stability, which indicates the anti-interference ability of HPMSM is not strong under the traditional control mode. In contrast, when the proposed scheme is adopted, as shown in Figure 22b, the speed decreased by only 50 rpm after the sudden load change. Besides, the speed recovery time is reduced to 0.1 s. In summary, by filtering out high-frequency harmonics, the anti-disturbance performance of HPMSM is significantly improved.  
Figure 21: Experimental results of the speed mutation: (**a**) no-load start experiment without proposed scheme; (**b**) no-load start experiment with proposed scheme; (**c**) speed sudden change experiment with proposed scheme.  
Figure 20: Experiment results of three-phase current waveforms and FFT analysis at 6000 rpm with 0.5 N\(\cdot\)m load [2 A/div].
## 3 CPS Architecture, Layers and Components
### Application Layer  
The interactive and third layer of the CPS architecture is the application layer where intelligent tasks are performed in the cyber world. It performs processing on the received information from the network layer which obtains that from the physical devices such as sensors or actuators. After executing applications, the system predicts the next time step and provides intelligent functionality to the CPS user. Decision-making complex algorithms are applied to the aggregated data [38]. Information based on the automated rightly invoked actions at the physical layer is processed by this layer. Protecting and preserving private data are required against leakage. In CPS, the application layer has no power or computational constraints, and it can operate efficiently. For example, the smart grid planning system predicts the electric power consumption of the region based on the readings observed by sensors at the physical layer from the previous data. It helps in electric power generation in plants. Likewise, in smart factories, digital twin technology can predict the throughput of the production line and can improve production. The most commonly used approach is data masking, secret sharing, and privacy maintenance. Furthermore, this layer needs a strong authentication process to avoid unauthorized access. In this layer, a huge amount of generated data handling and its protection in an efficient manner is a challenging task.
# Large-Eddy Simulation of Droplet Deformation and Fragmentation Under Shock Wave Impact
## 3 Computational Model
### Case Studies  
The simulations of the water droplet aerobreakup were carried out for two distinct Weber numbers: 205 and 7000. Following previous studies [21; 34], the physical properties  
Figure 1: Computational domain, along with a schematic of the physical model at \(t=0\).  
used in the calculations were prescribed as follows: a water density of 998 kg/m\({}^{3}\), a water viscosity of \(1.003\times 10^{-3}\) kg/(m \(\cdot\) s), and a surface tension coefficient of 0.073 N/m.  
The corresponding Mach numbers of the propagating shock waves, the post-shock flow conditions, and the Weber and Reynolds numbers calculated using post-shock air properties are presented in Table 1. These quantities can be used to non-dimensionalize the various flow variables, following the usual methodology, e.g., [18], facilitating a direct comparison across different flow cases. In particular, the non-dimensional time variable is defined as follows:  
\[t^{*}=\frac{u_{\rm g}}{D_{0}\sqrt{\varepsilon}}\left(t-t_{0}\right), \tag{17}\]  
where \(\varepsilon\) stands for the ratio between liquid and gas densities, with \(t_{0}\) representing the time instant of the shock impact on the droplet.  
Previous similar research works based on no-model calculations [35; 36] employed a computational grid with a spatial resolution exceeding 1000 cells per initial diameter. This resolution was necessary to accurately resolve the viscous boundary layer and predict the occurrence of KH instabilities. However, these studies were limited by their substantial computational demands, focusing primarily on demonstrating early-stage instabilities occurring on the cohesive surface of droplets. These studies did not extend the analysis to investigate the subsequent stages involving fragmentation and mist formation, which constitute the principal focus of the present research. Here, due to the adopted LES approach, which utilizes an SGS model to approximate the effects of unresolved turbulent eddies, a spatial resolution of 100 cells per original diameter was employed in the flow region of primary interest. This resolution was also chosen based on similar works, where it was proven to be effective in accurately capturing the overall deformation of the droplet surface while providing an approximate estimation of KH hydrodynamic instabilities [37; 38]. According to previous research findings at comparable Mach numbers, e.g., [27], the current flow configurations are characterized by a Kolmogorov length scale of approximately 0.5 \(\upmu\)m, for which the DNS approach would be practically unaffordable.
## 2 Computational Framework  
### Governing Equations and Solution Details  
All simulations for this study solved Reynolds-averaged Navier-Stokes solutions. The flow equations were solved using the finite volume method, and the sliding mesh technique was applied for the interface between rotational and stationary domains. This led the computational domain to update the deformed mesh but change in time. Hence, the moving mesh integrated over a control volume was coupled with continuity and momentum equations, which can be written as follows:  
\[\frac{\partial}{\partial t}\int_{V_{\text{c}}}\rho^{\text{f}}\text{dV}_{\text {c}}+\oint_{\text{S}}\rho^{\text{f}}\big{(}\mathbf{v}-\mathbf{v_{\text{g}}} \big{)}\cdot\mathbf{n}\text{ dS}=0 \tag{1}\]  
\[\frac{\partial}{\partial t}\int_{V_{\text{c}}}\rho^{\text{f}}\text{dV}_{\text {c}}+\oint_{\text{S}}\rho^{\text{f}}\mathbf{v}\big{(}\mathbf{v}-\mathbf{v_{ \text{g}}}\big{)}\cdot\mathbf{n}\text{ dS}=-\oint_{\text{S}}\boldsymbol{\tau}\cdot \mathbf{n}\text{ dS}-\oint_{\text{S}}\text{P}\cdot\mathbf{n}\text{ dS} \tag{2}\]where, \(\rho^{\rm f}\) is the fluid density, \(\upsilon\) is the fluid velocity vector which is based on the Cartesian coordinates, \(\upsilon_{\rm g}\) is the moving grid velocity, \(\tau\) is the molecular momentum transport tensor, \(\rho\) indicates the pressure gradients term, \(\rm V_{\rm c}\) is the mesh cell control volume, \(\rm\mathbf{n}\) is the normal vector to the control volume surface, and \(\rm S\) is the control volume surface area. The \(\gamma-\rm Re_{\rm 0}\) transition model of [PERSON] et al., 2006 [19], coupled the SST model with transport equations for the intermittency and \(\rm Re_{\rm 0}\) (i.e., momentum-thickness Reynolds number). The major improvement of this transition model was that it did not rely on nonlocal parameters, hence it was more suitable for modern CFD codes and complicated transitional-flow simulations. Further, a special modification to the intermittency was included to allow for separation-induced transition prediction. The SST \(\gamma-\rm Re_{\rm 0}\) has been used widely at both low [20] and high Reynolds numbers in aerodynamic applications. This paper adopted the \(\gamma-\rm Re_{\rm 0}\) transition model, which is proportional to the maximum strain-rate Reynolds number, i.e., presents the advantage of being a local property. The vorticity Reynolds number (\(\rm Re_{\rm v}\)) is defined as:  
\[\rm Re_{\rm v}=\frac{\rho\gamma^{2}}{\mu}\frac{\partial u}{\partial y}=\frac{ \rho\gamma^{2}}{\mu}\Omega \tag{3}\]  
The \(\Omega\) and \(\rm y\) in Equation (3), are the vorticity and the wall normal distance, respectively, and the maximum value of \(\rm Re_{\rm v}\) is dependent on the \(\rm Re_{\rm 0}\).
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 4 Identity-Based Full Homomorphic Encryption Scheme
### Correctness and Parameters  
**Theorem 3**.: _When \(q=\sqrt{Nnm}^{\frac{3}{2}}\omega(\log n),\sigma=\sqrt{m}\omega(\log n),_{\mathbf{ A}}<\left(\sqrt{N}m\omega(\log^{2}n)\right)\right)^{-1}\), the IBFHE scheme constructed in Section 4.1 is successfully decrypted with great probability._  
Proof.: For the initial ciphertext \(\mathbf{C}\in\mathbb{Z}_{q}^{m^{\prime}\times N}\) and secret key \(\mathbf{s}_{id}\in\mathbb{Z}_{q}^{m^{\prime}}\) of the id, there are  
\[\begin{array}{ll}\mathbf{s}_{id}^{\mathsf{T}}\cdot\mathbf{C}&=\mathbf{s}_{id}^{\mathsf{ T}}\big{(}\mathbf{A}_{id}^{\mathsf{T}}\mathbf{X}+\mu\mathbf{M}+\mathbf{E}\big{)}\\ &=\mathbf{s}_{id}^{\mathsf{T}}\mathbf{A}_{id}^{\mathsf{T}}\mathbf{Y}+\mu\mathbf{s}_{id}^{\mathsf{ T}}\mathbf{M}+\mathbf{s}_{id}^{\mathsf{T}}\mathbf{E}\\ &=\mu\mathbf{s}_{id}^{\mathsf{T}}\mathbf{M}+\mathbf{s}_{id}^{\mathsf{T}}\mathbf{E}\\ &=\mu\mathbf{s}_{id}^{\mathsf{T}}\mathbf{M}+\mathbf{e}^{\prime}\end{array} \tag{1}\]  
It can be obtained from Equation (1) and decryption formula  
\[\begin{array}{ll}\mu^{\prime}=\mathbf{s}_{id}^{\mathsf{T}}\cdot\mathbf{C}\cdot\mathbf{M} ^{-1}(\mathbf{\omega})\\ =(\mu\mathbf{s}_{id}^{\mathsf{T}}\mathbf{M}+\mathbf{e}^{\prime})\mathbf{M}^{-1}(\mathbf{\omega})\\ =\mu\mathbf{s}_{id}^{\mathsf{T}}\mathbf{\omega}+\mathbf{e}^{\prime}\mathbf{M}^{-1}(\mathbf{\omega}) \\ =\mu\big{\lfloor}\frac{q}{2}\big{\rfloor}+\mathbf{E}^{\prime}\end{array}\]  
In order to enable the decryption effective, it is necessary to ensure the ciphertext's noise \(\parallel\mathbf{E}^{\prime}\parallel_{\infty}\leq\sqrt{N}\big{(}q\sigma\sqrt{m} \alpha\omega\big{(}\sqrt{\log n}\big{)}\big{)}<\frac{q}{5}\), where \(\alpha<\left(\sqrt{N}\sigma\sqrt{m+1}\omega\big{(}\sqrt{\log n}\big{)}\right)^ {-1}\), that is \(\frac{2\mu^{\prime}}{q}=\frac{2\mu\big{\lfloor}\frac{q}{2}\big{\rfloor}+2 \parallel\mathbf{E}^{\prime}\parallel}{q}<\mu+\frac{2}{5}\), satisfying \(\frac{2\mid\mu^{\prime}\mid}{q}=\mu\). The ciphertext can be successfully decrypted. To meet the above requirements, set scheme parameters \((m,q,\sigma,\alpha)\): \(m=2n\log q,q=\sqrt{Nnm}^{\frac{3}{2}}\omega(\log n),\sigma=\sqrt{m}\omega( \log n),\alpha<\left(\sqrt{N}m\omega\big{(}\log^{2}n\big{)}\right)^{-1}\). \(\square\)
## 1 Introduction
### Organization  
This paper is organized as follows: all the considered spectrum sharing techniques are detailed conceptually along with their associated interference management schemes in Section 2. In addition, an algorithm to execute all these techniques is presented. Then expressions for the average capacity, spectral efficiency, and energy efficiency for each technique are derived in Section 3. An optimal number of ABSs is then derived for each technique and the system parameters and assumptions to evaluate performances are given in Section 4. In Section 5, an extensive system-level performance evaluation and discuss the relative outperformance of one technique to another is performed. Finally the performance of all techniques is compared in terms of spectral and energy efficiencies and find an optimal small cell density for each technique that can satisfy both the spectral and energy efficiency performance requirements for the 5G system. The paper is concluded in Section 6. A list of acronyms (Table 1) and a list of notations (Table 2) are given in the following section.
## 4 Experiments and Results
### Quantitative Performance Evaluation of the Framework
#### 4.1.1 Trajectory Generation
This could potentially be beneficial for scenarios such as the GCS, where occlusions and low frame rate both could be challenging for solely IoU-based matching. In summary, employing YOLOv7-tiny and DeepSORT as the detector and tracker, respectively, produces the greatest quality trajectories among all tested combinations empirically on the GCS dataset. Additionally, this experimental study demonstrates the modularity of the framework that allows rapid testing and evaluation of the two models.