esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 2 Video Acquisition and Rectification
### Image Acquisition and Rectification
Figure 4 shows a snapshot of the original video and its orthogonal image of the target area (1000 \(\times\) 600 m) for each case. The orthogonal image captured swells and showed that their wavefronts were shadowed from the sunlight, as the sun was located on the sea side during each observation (Table 1). Wave patterns appear differently over the four cases, reflecting different wave, lightning and camera conditions. We extracted the swell parameters by analysing the spatial and temporal variations of pixel intensity over the sea surface. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Area** & **Date/Time (JST)** & **SWH** & **SWP** & **TWL** & **Solar Angles 1** \\  & & & & & & **(Azimuth, Elevation)** \\ \hline Case 1 & A1 & 27 November 2018/8:41 & 0.60 m & 7.8 s & +0.67 m & (136.8\({}^{\circ}\), 20.6\({}^{\circ}\)) \\ Case 2 & A2 & 27 November 2018/10:51 & 0.61 m & 8.6 s & +0.59 m & (168.0\({}^{\circ}\), 33.2\({}^{\circ}\)) \\ Case 3 & A3 & 7 September 2019/6:08 & 0.89 m & 7.1 s & \(-\)0.31 m & (88.0\({}^{\circ}\), 8.3\({}^{\circ}\)) \\ Case 4 & A3 & 20 September 2019/9:31 & 1.06 m & 5.5 s & +0.49 m & (130.9\({}^{\circ}\), 44.9\({}^{\circ}\)) \\ \hline \hline \end{tabular}  
* \({}^{1}\) Azimuth angle clockwise from the north and elevation angle positive upward of a horizontal plane.  
\end{table} 
Table 1: Field test conditions: significant wave height (SWL), significant wave period (SWP), tidal water level (TWL), and solar angle. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Area** & **Video Length** & **Mean Camera Position** \\  & & **(longitude, Latitude, Altitude 1)** & **(Azimuth, Elevation)** \\ \hline Case 1 & A1 & 422 s & (138.2789\({}^{\circ}\), 34.7609\({}^{\circ}\), 153.6 m) & (184.8\({}^{\circ}\), 34.8\({}^{\circ}\)) \\ Case 2 & A2 & 741 s & (138.2479\({}^{\circ}\), 34.7431\({}^{\circ}\), 100.9 m) & (100.9\({}^{\circ}\), 61.5\({}^{\circ}\)) \\ Case 3 & A3 & 278 s & (138.2970\({}^{\circ}\), 34.7721\({}^{\circ}\), 142.4 m) & (202.6\({}^{\circ}\), 76.6\({}^{\circ}\)) \\ Case 4 & A3 & 500 s & (138.2964\({}^{\circ}\), 34.7701\({}^{\circ}\), 145.0 m) & (210.3\({}^{\circ}\), 66.9\({}^{\circ}\)) \\ \hline \hline \end{tabular}  
* \({}^{1}\) Altitude above mean sea level. Altitude above ground level is 4-5 m lower. \({}^{2}\) Azimuth angle clockwise from the north and elevation angle positive upward of a horizontal plane.  
\end{table} 
Table 2: Video length and estimated camera parameters.
## 4 Numerical Results and Discussions
### Comparison between the Proposed D2D-CRS-SM and VBLAST MIMO with ZF Detection
Figure 22: A comparative study between the proposed D2D-CRS-SM and VBLAST-ZF in terms of BER of NU in S\({}_{2}\).
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 5 Conclusions  
We designed efficient implementations of PRESENT and GIFT block ciphers on quantum computers. Our quantum circuits for PRESENT and GIFT block ciphers achieved an optimal number by minimizing qubits, quantum gates, and circuit depth. Based on the proposed method, we estimated quantum resources to apply the Grover search algorithm.  
Future work is to implement another block cipher as a quantum circuit to evaluate quantum resources for the Grover search algorithm. It seems very meaningful to estimate quantum resources for implementing candidate block ciphers in lightweight cryptography competition hosted by NIST ([[https://csrc.nist.gov/projects/lightweight-cryptography](https://csrc.nist.gov/projects/lightweight-cryptography)]([https://csrc.nist.gov/projects/lightweight-cryptography](https://csrc.nist.gov/projects/lightweight-cryptography)), accessed on 10 May 2021). Many block ciphers have been proposed, and estimating the quantum resources for these block ciphers would make for interesting research. The result of FELICS competition ([[https://www.cryptolux.org/index.php/FELICS](https://www.cryptolux.org/index.php/FELICS)]([https://www.cryptolux.org/index.php/FELICS](https://www.cryptolux.org/index.php/FELICS)), accessed on 10 May 2021) [20] is another candidate. In this competition, a lot of lightweight symmetric key cryptography algorithms were evaluated on embedded processors. Comparing the performance of whether there is a relationship between quantum computers and embedded processors is an attractive research opportunity.  
Data curation, K.J.; Investigation, G.S., H.K. ([PERSON]) and H.K. ([PERSON]); Software, K.J. and G.S.; Supervision, H.S.; Writing--original draft, K.J.; Writing--review and editing, K.J., H.K. ([PERSON]) and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (<Q|Crypton>, No.2019-0-00033, Study on Quantum Security Evaluation of Cryptography based on Computational Quantum Complexity, 90%) and this work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2018-0-00264, Research on Blockchain Security Technology for IoT Services, 10%).  
The authors declare no conflict of interest.
## 3 Methodology
### Adjustments to the Critical Value of \(t\) Testing  
In the FGWR framework, a \(t\)-test can be performed for each parameter at each calibration location. This problem has become more complicated due to the potential dependence on local estimates [23; 36]. An effective correction to the significance level \(\alpha\) for the \(t\)-tests has been proposed [37]. The adjusted \(\alpha\) value can be tried multiple times during the hypothesis test, including 90% (\(\alpha\) = 0.1), 95% (\(\alpha\) = 0.05) and 99% (\(\alpha\) = 0.001) confidence. The adjusted value of \(\alpha\) for the local \(t\)-tests is given by:  
\[\alpha=\frac{\xi}{\frac{\mathrm{ENP}}{\mathrm{m}}} \tag{19}\]  
where \(\xi\) is the desired error rate (i.e., 0.05), \(\mathrm{m}\) is the number of parameters and \(\mathrm{ENP}\) is the effective number of parameters. \(\mathrm{ENP}=\mathrm{trace}(\mathrm{S})\) and \(\mathrm{S}\) is the hat matrix. If \(\mathrm{ENP}=\mathrm{m}\) then \(\alpha=\xi\); in all other situations \(\alpha<\xi\).  
In the MFGWR framework, each covariate produces an \(\mathrm{ENP_{j}}\) where \(\mathrm{ENP_{j}}=\mathrm{trace}\Big{(}\mathrm{R_{j}}\Big{)}\). \(\mathrm{R_{j}}\) is the effective number of parameters for each covariate. Each covariate has its independent critical value \(\alpha_{j}\) derived from the aforementioned \(t\)-tests.  
\[\alpha_{j}=\frac{\xi}{\mathrm{ENP_{j}}} \tag{20}\]If \(\text{ENP}_{\text{j}}=1\) then \(\alpha_{\text{j}}=\upxi\); in all other situations, \(\alpha_{\text{j}}<\upxi\). This paper provides the first application of both MFGWR and its associated hypothesis testing framework for modeling spatial flows.
## 3 Results
### Model Evaluation
#### 3.1.2 Seasonal GPP Simulation Results  
The model performances across different seasons (spring [March-May], summer [June-August], autumn [September-November], and winter [December-February]) are shown in Figure 7. Overall, the ML models exhibited the highest accuracy in winter, with all models showing higher \(R^{2}\) values ranging from 0.36 to 0.79, lower \(RMSE\) (0.83-1.45 g Cm\({}^{-2}\) d\({}^{-1}\)), and lower \(MAE\) (0.62-1.09 g Cm\({}^{-2}\) d\({}^{-1}\)) than other seasons. Spring follows, with autumn showing slightly lower performance than spring, while summer shows significantly poorer performance compared to other seasons (\(R^{2}\) = 0.54-0.68, \(RMSE\) = 1.63-1.95 g Cm\({}^{-2}\) d\({}^{-1}\), \(MAE\) = 1.23-1.56 g Cm\({}^{-2}\) d\({}^{-1}\)). Additionally, the ML models consistently outperformed linear models across all seasons, and the XGB model performed the best in all seasons.
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 4 System Model  
We primarily focuses on modeling graph data as undirected graphs for research purposes. An undirected graph can be denoted as \(G=(A,X)\), where \(A\in\{0,1\}^{N\times N}\) represents the adjacency matrix, with \(N\) being the number of nodes. The node set of graph \(G\) can be expressed as \(\{v_{1},v_{2},\ldots,v_{N}\}\). Additionally, \(X\in\mathbb{R}^{N\times D}\) signifies the node feature matrix, and \(D\) denotes the feature dimension of the nodes. Following the setup similar to that in literature, the feature vector of a node \(v_{i}\) can be characterized as a triplet \((x_{i},p_{i},y_{i})\), where \(x_{i}\in\mathbb{R}^{D-k}\) signifies the non-private portion of the node vector, \(p_{i}\in\mathbb{R}^{k}\) representsthe private part of the node vector, and \(y_{i}\in\mathbb{R}\) indicates the label of the node. It is assumed that during training, the partial privacy of the users can be accessed, and the process of learning node embeddings involves decoupling to protect user privacy.  
As illustrated in Figure 1, the model architecture is based on a dual-channel privacy-disentangled graph autoencoder with an attention mechanism. The model comprises two graph autoencoders: the privacy graph autoencoder and the utility graph autoencoder. Firstly, a privacy graph autoencoder is trained to estimate the privacy distribution of the graph data. An attention layer is inserted between the privacy latent representation and the privacy decoder. This attention layer measures the global importance of nodes for privacy protection through an attention mechanism and ranks them. The nodes with higher importance and their latent representations are selected to constitute the main privacy information distribution. Secondly, during the training of the utility autoencoder, the selected node corresponds to the utility latent representations to form the distribution of utility information. The HSIC is used to measure the dependence between privacy and non-privacy features. HSIC is also incorporated as a penalty term in the optimization process of the utility graph autoencoder. Finally, by alternately training the privacy and utility graph autoencoders, the privacy importance of each node is dynamically calculated, enhancing the robustness of the model across different complex datasets.
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.1 PRR and RSSI Values  
As mentioned in Section 4, we considered the RSSI and PRR values to characterize the radio communication links. These metrics can be influenced not only by hardware properties, but also by deployment area characteristics. The values of PRR and RSSI presented in Figure 5 show that, in our experiments, the links were most affected when nodes were deployed at ground level. Indeed, the values of measured RSSI or PRR at ground level were always smaller compared to the values measured for a deployment at height. This is particularly true in the case of RSSI, where the difference could reach 10-20 dBm. Since the same hardware was used for both deployments, this significant difference in the value of RSSI and PRR measured in the two experiments was mainly due to the environment properties. These properties were time and link-position dependent. In the following, we compare, for the two deployments, the temporal and spatial properties.  
An important observation can be made in Figure 5: the two metrics, RSSI and PRR, have different behaviors. While the difference in terms of RSSI between ground-level and aboveground scenarios is always significant, this is not the case for PRR, meaning that a similar PRR can be obtained at highly different RSSI levels. Moreover, the opposite is true as well and, at similar RSSI levels, the PRR can be quite different, as shown for the two directions of link 3-4. The results presented in this section show that the PRR is the best metric to use at network layer to select the link which guarantees the relevant metric transmission reliability.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 4 The Application of Machine Learning in Nano-Modified Concrete
### Gene Expression Programming (GEP) Algorithm  
GEP is a symbolic regression method based on evolutionary algorithms. It is flexible and adaptive and applicable to various optimization problems. GEP has wide applications in the fields of function approximation, symbolic regression, feature selection, and image processing.  
An empirical GEP model was developed by the Gene Expression Programming technique. The model was validated using statistical evaluation. The trend of the GEP model was consistent with the general trend of experimental results in the literature. The GEP model predicted that the compressive strength of concrete increased with the addition of carbon nanotubes (CNTs), NS, and NC, while the compressive strength decreased with the increase in NC [24]. [PERSON] et al. [26] study experimentally investigated the effect of freeze-thaw cycles on the compressive and flexural strengths of cement mortars containingNS and MS and predicted and calculated the porosity, compressive and flexural strengths of cement mortars using the new GEP model, which showed a high correlation between the target values and the predicted results. [PERSON] et al. [60] first used Response Surface Methodology (RSM) to maximize the mechanical properties and durability of concrete containing recycled coarse aggregate and nano-silica, and then predicted the compressive capacity of concrete with recycled coarse aggregate and nano-silica volcanic ash powder by using Gene Expression Programming (GEP) technique. An integrated approach to ANN, GEP, and RF was combined and formed a powerful learner. A new expression for predicting the 28-day compressive strength of nanocomposites doped with silicon content was also developed based on a database [61].
###### Abstract
This assumption is reasonable for the RFI source in SAIR, but there is no research showing that the RFI source in SAR also obeys similar distribution characteristics. In order to calculate the exact geographic location of multiple RFI sources of SAR with taking into account the two-dimensional DOA estimation requirements and the complexity of the system calculation, this paper presents a new method for the location of multiple independent RFI sources combining two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) and the particle swarm optimization (PSO) algorithm. The main idea of this method is to employ array signal processing technology to estimate the spatial geometric relationship between the SAR system and the RFI sources at each azimuth time, and then use the particle swarm optimization method to solve the optimization problem in the geographic location estimation of multiple independent interference sources. This method is different from other methods in three main aspects. First of all, the proposed method makes use of an L-shaped channel array to realize the two-dimensional estimation of RFI DOA, its structure is simple and effective. The reason for choosing an L-shaped array is given in Section 2.1. Moreover, compared with the MUSIC algorithms, the two-dimensional ESPRIT used in this method does not need to construct and search the spatial spectrum of the signal, thereby reducing the computational burden and improving the computational efficiency of the DOA estimation method, which is very essential when processing large-scale SAR data. Last but not the least, the method of estimating the geographic location of RFI sources proposed in this paper can accurately indicate the specific geographic coordinates of RFI sources, instead of just getting the direction of RFI sources, which can provide location information for shutting down illegal RFI sources and help the SAR system avoid interference areas. The rest of this paper is organized as follows. Section 2 describes the geometry model of the SAR system and RFI sources, the multi-channel array model, and the signal model. In Section 3, we present the multiple RFI sources geolocation method based on the 2D-ESPRIT and PSO algorithm under the condition of an L-shaped channel array. The simulation experiment given in Section 4 proves the feasibility of the proposed method, followed by the discussion and conclusion in Sections 5 and 6.
## 3 Research Results
### Result of Seismic Liquefaction Assessment  
As a result of classifying 899 boreholes by depth, 9615 3D point data were created and these were used to assess FL in 2.2. Seven seismic waves, including the Gyeongiu and Pohang earthquakes in South Korea, the Hokkaido earthquakes in Japan, and some earthquakes in the US, Greece, and Italy, were used to perform the subsurface response analysis and liquefaction assessment, and the mean value was used as the representative value. Additionally, the spectrum matching was performed for compliance with the Standard Design Response Spectrum corresponding to the return period of 2400 years, stipulated in the Common Applications of Seismic Design Code. Later, each borehole was divided into a 1 m gap by the geological information and groundwater level data for the detailed liquefaction assessment by depth, and over 20 m, it was divided into 3 m to conduct the liquefaction assessment.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 5 Results
### Impact of the RTA STAs Number  
First, we consider the dependence of the metrics introduced above on the number \(M\) of RTA STAs. Figure 4 shows the simulation results for \(T_{period}=50\,\mathrm{ms}\), \(\sigma=10\,\mathrm{ms}\) and TXOP Limit \(=2\,\mathrm{ms}\). With default EDCA, RTA STAs do not use any special method for RTA traffic service. In this case, RTA STAs and non-RTA STAs use EDCA parameters corresponding to AC Voice and AC Best Effort (Table 1). The obtained results show that with the default EDCA parameters, 0.99999-quantile of delay exceeds 20 ms, so RTA QoS requirements are violated. The multi-link method [44] described in Section 3 almost halves the delay quantile. However, RTA service methods described in Section 4 guarantee even lower delays and do not require devices to have multiple radio interfaces, so it only makes sense to compare these methods with each other. Note that the multi-link method can be combined with Tuned EDCA, PCA, or Smart PCA methods to further reduce the delay quantile, and it can be a direction of future research.  
For \(M=1\), the delay quantile for PCA and Smart PCA does not differ because condition (1) is met, so the channel access in both methods is the same. With increasing \(M\), PCA loses not only to the Smart PCA but also to the Tuned EDCA. The reason is that in PCA, one RTA STA can occupy the channel for time \(T_{\mathrm{PCA}}+T_{s}\), which causes large delays for another RTA STA that generate data during this channel occupancy. In addition, before a successful transmission, an STA can lose contention to several other RTA STAs, which leads to waiting for several \(T_{\mathrm{PCA}}+T_{s}\) intervals. At the same time, when using Tuned EDCA, the main contribution to the delay is waiting only for TXOP Limit and maybe a few short \(T_{s}\) transmissions in case of losing to several RTA STAs. The Smart PCA retains the delay advantage of the Tuned EDCA with increasing \(M\) because instead of waiting for long channel occupancy by other RTA STAs, the RTA STA can transmit its data frame as soon as it arrives.  
Figure 4: Delay quantile and efficiency vs. the RTA STAs number \(M\) in case of \(T_{period}=50\,\mathrm{ms}\), \(\sigma=10\,\mathrm{\SIUnitSymbolMicro s}\), TXOP Limit \(=2\,\mathrm{ms}\).
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 2 Materials and Methods
### Reynolds Number  
The Reynolds number of a certain flow condition is defined as the ratio of inertial forces to viscous forces, and it is a dimensionless parameter that can help predict flow patterns. The Reynolds number is defined in Equation (13), where \(L\) is the characteristic length.  
\[Re=\frac{\rho\cdot V\cdot L}{\mu} \tag{13}\]  
As can be seen in Equation (13), if all other parameters are held constant, the Reynolds number will decrease if the characteristic length decreases. When analysing propeller flow, the characteristic length is the chord length of the blades, and so smaller blades will result in smaller Reynolds numbers. As can be seen in Figure 6, at a Reynolds number of 100,000 for smooth airfoils, there is a sudden transition from a low maximum section \(C_{L}/C_{D}\) ratio to a high maximum section \(C_{L}/C_{D}\) ratio. Propellers used on small drones often operate in Reynolds numbers less than 100,000, and the maximum lift/drag ratio that can be achieved is therefore limited by their small diameters.  
When operating below the 100,000 Reynolds number threshold, the lower lift/drag ratios associated with these flow regimes, are the biggest contributor to the lower efficiencies associated with scale propellers. The velocity component in Equation (13) means that the angular velocity of the propeller affects the Reynolds number. The Reynolds number will vary along the length of the blade, due to varying chord lengths and rotational velocities, and at low angular velocities sometimes no part of the blade will exceed the 100,000 Reynolds number threshold. It is therefore critical for a propellers efficiency to operate at the highest RPM value possible (assuming tip speeds do not approach Mach 0.8).