esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of the Performance of a Novel Vertical-Axis Wind Turbine
## 1 Introduction
Most of the VAWT's power output is produced on the upwind pass, whereas flow momentum is considerably reduced on the downwind pass, hence resulting in a reduced power output [13]. Darrieus-type VAWTs with straight blades are less efficient than those with helically twisted blades [14]. Various numerical and analytical schemes have been implemented in a bid to investigate performance characteristics of VAWTs. [PERSON] et al. [15] solved potential flow equations in conjunction with integral boundary layer equations formulated for VAWT rotors, using a semi-inverse iterative algorithm. From their simulations, they inferred that VAWTs can be designed to avoid the occurrence of dynamic stall resulting from blade-vortex interaction in the downward part of rotor rotation during gusts and normal operation, and even at low tip-speed ratios [16]. [PERSON] et al. [14] implemented a CFD scheme based on the vortex transport model (VTM). The VTM is based on solving Navier-Stokes (N-S) equations in terms of the vorticity and velocity. The governing momentum equation is expressed in terms of the vorticity and velocity and is the result of finding the curl of the velocity and pressure-based N-S momentum equation. In their work on the steady-state and dynamic simulations of Savonius rotors, [PERSON] et al. [16] found that the best approximations of the static torque coefficient, the dynamic torque coefficient and the power coefficient were obtained using the shear stress transport-k-\(\omega\) model rather than the k-\(\varepsilon\) turbulence model. At startup, dynamic torque coefficient curves of a Savonius rotor oscillate around fixed values in polar coordinates [16]. There is a significant difference between simulated and experimental values of the power coefficient of a DVAWT at high tip-speed ratios, even though simulation and experimental power coefficient values follow the same trend as the tip-speed ratio varies [17]. The power coefficient of a Darrieus-type VAWT peaks at tip-speed ratios between 3 and 4 and drops for tip-speed ratios greater than 4 for both CFD simulation and experimental results, with values of the former being slightly higher than those of the latter (see Figure 18 in [18]). This study uses an experimental approach to investigate the performance of the novel AVAWT [1]. Unlike CFD simulations performed by some of the above cited researchers, which were based on two or three tip-speed ratios, this study investigates performance characteristics of the novel AVAWT over a broad range of tip-speed ratios.
# Stabilization Methodology in Foundation Soils by ERT-3D Application in Estepona, South Spain
## 1 Introduction
Fortunately, important advances have been made in 3D resistive imaging and its inversion processing through applying the powerful resistance inversion software, RES3 DINV [43].This is possibly the most widespread, accurate, simple, and affordable software for data inversion in electrical tomography, and, thus, it has been used in this work. Several factors have been reported to impact the variation of electrical resistivity in the subsurface. These include variation in rock type, rock fabrics, rock deformation, water saturation, different degrees of weathering, etc. [44; 45]. These factors, which are known to impact spatial variation on ground electrical resistivity, are also capable of impacting variation in other physical properties of rocks [46; 47].With different electrode distances, ERT-3D offers the possibility of locating these gaps/cavities and possible holes under the foundation of a building, which can cause severe deformation and settlement. Both consolidating and stabilizing soil through injections have been proven effective [48; 49; 50; 51] and are used to mitigate or even solve differential settling. The injection material generally depends on the lithological morphology of the soil in question. Synthetic resin is frequently used as an injection fluid [52], but this paper proposes injections of cement grout as a more economical and versatile solution. For a soil consolidation project to be successful, electrical resistivity data must be available in advance to provide initial information on the subsurface structure. In our methodology for consolidating and stabilizing buildings with shallow foundations, we will use electrical tomography as a tool that detects empty cavities, which are typical examples of anthropic (resistive) fillings with low bearing capacity in subsoils [3]. Thus, this work proposes ERT -3D to monitor the subsoil in real time, from the beginning to the final consolidation through the different injection passes into the subsoil, until its stabilization. As an example of a case of application of our combined methodology, we have chosen a historic building with serious stability problems in its foundations [5], located in Estepona, on the Spanish south coast. The subsoil was consolidated with injections of cement grout applying the electrical methodology, resulting in the preservation of the building, and avoiding its possible collapse.
## 2 Model and formulations
### Wavenumber Domain Approach for the Total Radiation Efficiency of the Finite LR Plate
The inverse Fourier transforms of \(p(x,y)\) and \(v(x,y)\) are represented as:  
\[p(x,y)~{}=~{}\frac{1}{4\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty} P\Big{(}k_{x},k_{y}\Big{)}e^{jk_{x}x+jk_{y}y}dk_{x}dk_{y}, \tag{35}\]  
\[v(x,y)~{}=~{}\frac{1}{4\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty} V\Big{(}k_{x},k_{y}\Big{)}e^{jk_{x}x+jk_{y}y}dk_{x}dk_{y}, \tag{36}\]  
respectively, and substituting them into Equation (34) gives:  
\[W~{}=~{}\frac{1}{32\pi^{4}}\text{Re}\bigg{(}\int_{-\infty}^{\infty}\int_{- \infty}^{\infty}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}p\Big{(}k_{x},k _{y}\Big{)}e^{jk_{x}x+jk_{y}y}dk_{x}dk_{y}\times\int_{-\infty}^{\infty}V^{*} \Big{(}k_{x}^{*},k_{y}^{*}\Big{)}e^{-jk_{x}^{*}x-jk_{y}^{*}y}dk_{x}dk_{y}dxdy \bigg{)} \tag{37}\]By changing the order of integration and firstly performing the integration over \(x\) and \(y\), the Dirac delta function is obtained, namely:  
\[\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{j(k_{x}-k_{x^{\prime}})x}e^{j(k _{y}-k_{y^{\prime}})y}dxdy\ =\ 4\pi^{2}\delta(k_{x}-k_{x^{\prime}})\delta\Big{(}k_{y}-k_{y^{\prime}}\Big{)}. \tag{38}\]  
The substitution of Equation (32) into Equation (37) derives:  
\[W\ =\ \frac{1}{8\pi^{2}}\text{Re}\Bigg{\{}\int_{-\infty}^{\infty}\int_{- \infty}^{\infty}Z_{a}\Big{(}k_{x},k_{y}\Big{)}\times\Big{|}V\Big{(}k_{x},k_{y} \Big{)}\Big{|}^{2}dk_{x}dk_{y}\Bigg{\}}. \tag{39}\]  
It should be noted that only the wavenumber components \(k_{x}\) and \(k_{y}\) satisfying \(k_{x}^{2}+k_{y}^{2}<k^{2}\) contribute to an effective acoustic radiation. The range of integration can be limited, and Equation (39) can be expressed as:  
\[W\ =\ \frac{1}{8\pi^{2}}\text{Re}\Bigg{\{}\int_{-k}^{k}\int_{-\sqrt{k^{2}-k_{y} ^{2}}}^{\sqrt{k^{2}-k_{y}^{2}}}Z_{a}\Big{(}k_{x},k_{y}\Big{)}\times\Big{|}V \Big{(}k_{x},k_{y}\Big{)}\Big{|}^{2}dk_{x}dk_{y}\Bigg{\}}. \tag{40}\]  
The substitution of \(V\Big{(}k_{x},k_{y}\Big{)}\) calculated by Equation (31) into Equation (40) can acquire \(W\). Therefore, the radiation efficiency of the LR plate can be derived by:  
\[\sigma_{rad}\ =\ \frac{W}{\frac{1}{2}\rho_{0}c_{0}ab\Big{\langle}|v|^{2} \Big{\rangle}}, \tag{41}\]  
where \(\Big{\langle}|v|^{2}\Big{\rangle}\) is the mean square velocity averaged over the plate surface.
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
## 3 Proposed Predistortion System
### Identification Process of the Proposed Predistortion System
Hence, a memory polynomial function seems adequate when compared to more complex functions such as the Volterra series, which would result in comparable performance at a much higher computational cost. However, Volterra series can be considered for cases where the nonlinear distortions of the cascade made of the APD and DUT are too complex to be adequately compensated for using a memory polynomial function.
## 4 Results and Discussion
Based on Table 2, the first observational dataset required 58 iterations to yield the best estimate of hydrocarbon depth, which was 349.92 m. The second observational dataset needed 34 iterations to get its best estimate, which was 650.20 m. In contrast, only 21 iterations were needed by the third observational dataset to produce the estimate of 950.22 m. Moreover, only 114.047 s, 73.760 s, and 60.556 s were taken by the GP-based inversion methodology to reach the estimates of 349.92 m, 650.20 m, and 950.22 m, respectively. From the table, the calculated MSE values were also very small. This means that the deviations between its true EM values and the predicted EM profiles were very low. All the final RPCs were also lesser than 0.05 as compared to what was defined in the methodology. In addition, in the context of this work, the reliability of the GP regression in evaluating the EM profiles was investigated as well by considering data modeled by the FE method as the true EM values. Thus, MADs, MAPEs, and RMSEs between the EM responses modeled by FE and EM evaluated by the GP (at depths 200-1000 m with a gap of 1000 m). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Index** & **Iteration Number** & **Estimate (m)** & **MSE** & **RPC (\%)** \\ \hline
1 & \(h_{58}\) & 58 & 349.92 & 0.00000033 & 0.04977145 \\
2 & \(h_{34}\) & 34 & 650.20 & 0.0000039 & 0.04978711 \\
3 & \(h_{21}\) & 21 & 950.22 & 0.0000051 & 0.04991896 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The main Gaussian process (GP)-based inverse modeling results (all three observational datasets). Figure 11: Convergence test (3 rd observational dataset). Figure 10: Convergence test (2 nd observational dataset). of 100 m each) were calculated. The results are presented in Table 3. Furthermore, to find out the accuracy and performance of the GP-based inverse modeling in predicting the hydrocarbon depth, percentage errors between the true depths (i.e., computer input) and the estimates were calculated as well. The percentage errors are indicated in Table 4.  
Based on Table 3, all the MADs, MAPEs, and RMSEs for depths 200 m to 1000 m with 100 m gap each were very small, which were 1.49 \(\times\) 10\({}^{-9}\) (on average), 0.114% (on average), and 3.49 \(\times\) 10\({}^{-9}\) (on average), respectively. As seen in Table 4, the percentage errors were very low as well, which was 0.0260% on average. All the results revealed that GP regression was capable of evaluating the EM profiles at various depths of hydrocarbon accurately and with low time consumption. The main goal was also achieved in that the GP-based inversion methodology can predict the depth of hydrocarbon in the SBL application efficiently.
## 4 Simulation Modeling Results  
During the simulation, it was required to determine the PAPR value for a signal packet, consisting of 1000 binary bits of transmitted messages, as well as the BER performance when using optimal FTN signals with a reduced PAPR for channels with AWGN and channels with Rayleigh fading. The simulation model is used to calculate the probability of erroneous detection of channel symbols depending on the signal-to-noise ratio \(E_{b}/N_{0}\), where \(E_{b}\) denotes the average bit energy. For each value of \(E_{b}/N_{0}\), the error probability was calculated and averaged over \(10^{6}\) bits.
# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 3 Methodology
### Feature-Selection Methods  
Feature selection (FS) is the process of extracting a subset of features from the whole dataset, such that the new feature space is reduced within specific criteria [43]. FS considers a critical step in the feature engineering process as it allows the model to concentrate on the important features and remove the less important features. FS has several advantages in terms of model performance, including the following. (i) FS contributes to increasing the prediction accuracy, (ii) it reduces the model complexity, and (iii) it makes the developed model easier to understand and interpret. FS has three main types, including the filter approach, wrapper approach, and embedded approach [44]. Figure 2 show the main categories of FS. Our work explores three feature selection methods: the chi-square test (Chi2), recursive feature elimination (RFE), and mutual information.  
* The filter approach tries to rank features based on descriptive and statistical measures. The optimum feature subset selection is based on the ranking of features according to the correlation of each feature with the desired output [45]. The types of filter methods are person correlation, information gain, and mutual information. The chi-squared test compute chi-squared stats between each nonnegative feature and class and calculates the score for each feature. This score is used to select the important features that have the highest score. We used Chi2 [46], a library built in Python. Mutual information (MI) [47] is a nonnegative value that expresses how dependent two random variables are on one another. Higher values indicate greater dependence, and it equals 0 only when two random variables are independent. We used mutual_info_classif() [47], a library built in Python.
* The wrapper approach mainly depends on the performance of the developed model. The core part of the wrapper approach is the utilized algorithm, which tries to find the optimum feature subset that gives the best performance. Initially, the process starts with a few features, and the performance is evaluated [48; 49]. This process iterated with the different number of features until reaching the optimal feature subset. Types of wrapper methods include the forward feature selection, backward feature selection, and recursive feature elimination (RFE). By using RFE feature selection, each feature is ranked according to its score in order to help the model select the best features.  
* The embedded approach considers part of the training process, and the feature-selection process is an integral part of the classification model [50]. This approach lies between the filter and wrapper approach, as the feature-selection process is made during the model tuning process. Lasso, ridge, and tree-based algorithms (i.e., decision tree, random forest, etc.) are the common ways of embedding feature selection [51]. We used RF, which uses a mean decrease impurity (Gini index) to estimate a feature's importance.
# Efficient MIMO Signal Predistortion for Secrecy-Enhancing
###### Abstract  
Massive machine type communications or internet of things (IoT) over wireless systems have invoked attention in terms of security problems, especially for multi-input multi-output (MIMO) schemes. In this paper, we propose efficient physical layer security (PLS) enhancing methods for space-time block coding (STBC), as well as spatial multiplexing (SM) schemes. The proposed methods pre-distort the transmit signals by using the phase information of the legitimate MIMO channel, and, as a result, the illegal eavesdropper cannot extract any information. The proposed predistortion for the STBC schemes is done so that the channel matrix at the receiver is a real-valued one, which results in full-rate and full-diversity gain for more than two transmit antennae. Therefore, compared to the conventional schemes, the proposed scheme eventually leads to the higher performance gain and lower detection complexity in addition to the high security protection. By extending the principle of the proposed method for STBC, a predistortion scheme is also proposed for SM by using the phase information of the column space of the channel matrix. The simulation results investigated in this paper reveal that the proposed methods can achieve enhanced error rate performances, as well as high security protection.  
physical layer secrecy; MIMO; STBC; wiretap channel +
Footnote †: journal: Electronics  
Article
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Case Study Summary  
This case study presented a risk analysis of a gas power station located in the southern coastal plain of Israel, near Ashkelon city. The seismic hazard evaluation was determined for a return period of 2% in 50 years, and the predicted PGA for the primary earthquake  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Power Station Component**} & \multicolumn{2}{c}{**Damage Probability**} \\ \cline{2-3}  & **Unanchored** & **Anchored** \\ \hline Gas Turbines & 7.68 \(\times\) 10\({}^{-5}\) & 7.68 \(\times\) 10\({}^{-5}\) \\ Steam Turbines & 0.0416 & 0.0416 \\ Steam Boiler & 0.0109 & 0.0109 \\ GIS (Gas-Insulated Switchgear) & 0.1020 & 0.0360 \\ PRMS (Pressure Regulating and Metering Station) & 0.0824 & 0.0130 \\ Water Treatment Facility & 0.2312 & 0.0893 \\ Water storage Tank & 0.1570 & 0.0620 \\ Oil storage Tank & 0.1570 & 0.0620 \\ Average & 0.0978 & 0.0394 \\ Standard Deviation & 0.0753 & 0.0360 \\ Power Station Total Probability of Damage & 0.5732 & 0.2733 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Damage probabilities for power station components in unanchored and anchored conditions.  
event at the site is expected to be 0.2 g. Further investigation reveals that the power station is located in an area with a high degree of susceptibility to liquefaction for foundations shallower than 20 m. However, the design of the power station considered this, and the construction included deeper foundations. The estimated likelihood of a tsunami occurring at the site is an event with a return period of 200 years, with a maximum predicted inundation height between 2.5 m and 5.0 m. In order to mitigate this risk, a perimeter tsunami wall was constructed, with a total cost of 0.5% of the total construction cost. A possible mitigation strategy was proposed by anchoring critical subcomponents in the power station. The analysis illustrated that anchoring of critical subcomponents can significantly reduce the probability of damage during a seismic event. The strategy will be considered cost-effective if the implementation cost is lower than USD 2.7 million in the case of a 120-year lifespan consideration and lower than USD 1.12 million for a 50-year lifespan.
###### Abstract  
Zinc (Zn) is an essential nutrient for the human body and is prone to deficiency. Supplementing Zn through zinc-enriched cereals is of great significance in addressing the widespread issue of zinc deficiency. However, there is no simple linear correlation between the soil zinc content and rice grain zinc content, which poses challenges for zoning zinc-enriched rice cultivation based on the soil Zn content. Therefore, accurately predicting the zinc content in rice grains is of great importance. To verify the robustness of the prediction model and expand its applicability, this study established a prediction model using 371 sets of previously collected and tested rice grain and root zone soil samples from the Pearl River Delta and Heyuan regions in Guangdong. The model was validated using the data from 65 sets of rice and root zone soil samples collected and analyzed in Zijin and Dongyuan counties, Heyuan, in 2023. The results show that zinc absorption by rice grains is controlled by multiple factors, primarily related to the soil S, P, CaO, Mn, TFe\({}_{2}\)O\({}_{3}\), TOC, and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio. Both the artificial neural network model and random forest model demonstrated a good predictive performance across large regions. However, in the Heyuan region, the random forest model outperformed the artificial neural network model, with an R\({}^{2}\) of 0.79 and an RMSE of 0.05 when the predicted data were compared against the measured BAF\({}_{\rm Zn}\) of the rice. This suggests that predicting the zinc content in rice grains based on the soil macro-elements (including oxides) and TOC is feasible, and, within certain regional boundaries, the prediction model is robust and widely applicable. This study provides valuable insights into the rational development of zinc-enriched rice in the Heyuan region and offers a useful reference for establishing prediction models of the beneficial element content in rice grains in areas with limited data.  
Zinc; soil; artificial neural network model; random forest model; Guangdong +
Footnote †: journal: Applied sciences  
Article
## 3 Background  
There have been substantial investments in V2V and V2I technologies and research, as the state-of-the-art survey in [14] demonstrates. Active research aims to enhance the encryption performance for these safety-critical technologies, for example, in [15], the authors provide a certificate-based algorithm to reduce cryptography delays. However, the use of quantum-based technologies to bolster communications and security performance within the V2V and V2I realms is relatively nascent and requires a transition to practical applications. Most of the current research in this domain remains theoretical [16; 17; 18; 19], with the advantages and disadvantages not being articulated. Our research indicates that the use of FSO communications may be limited to backhaul links in V2I systems due to line-of-sight issues [20]. However, exploring practical applications of quantum technology in vehicular systems will persist as a valuable research avenue, building on existing theoretical work.  
The three-year AirQKD project ran from July 2020 to September 2023. The multi-million-pound AirQKD project was an investment to help grow the UK's quantum technology capability. It did this by building a free-space optical (FSO) QKD system and related applications and technologies. AirQKD's investigations ranged from new semiconductors to deployed applications, e.g., the V2I testing described here.
## 3 Mathematical Model of Radar Operation Under Interference Conditions
This model takes into account the main steps of the target trajectory tracking algorithm based on data from a single radar and provides a comprehensive approach to accounting for probabilistic and time characteristics, which distinguishes it from existing models. This model can be used to study information processes in the systems where radars interact with tracking objects. The use of exponential stochastic GERT models allows for the analytical treatment of results (e.g., functions and distribution densities) for comparative analysis and research. It also enables the study of complex, critical information and control systems using mathematical methods. Despite the effectiveness of the GERT model for analyzing processes at the macro level, its capabilities in detailing local operations, such as precise target tracking under noise and interference conditions, are limited. To achieve higher accuracy at the trajectory level, methods capable of effectively accounting for the dynamic nature of systems and performing recursive state estimations are required. In this context, the Kalman filter serves as a suitable complement to the GERT model.