esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 3 Secure Transmission Strategies
### Eavesdropper Is Detected in the Sector Secrecy Guard Zone  
If there exist eavesdroppers within the sector secrecy guard zone, the transmitter emits AN with power \(P_{A}\) while transmitting the signal with power \(P_{S}\). The total transmit power is denoted as \(P\), \(P_{S}=\mu P,P_{A}=\left(1-\mu\right)P\), where \(\mu\) is the power allocation factor of the confidential information power to the total transmit power \(P\) with \(0\leq\mu\leq 1\)[36]. Then, the SNR at the receiver is given by  
\[\gamma_{D_{B}}=\frac{P_{S}M_{S}|h_{D}|^{2}L\left(r_{D}\right)}{\sigma_{D}^{2}}, \tag{8}\]  
and the instantaneous SNR at the most detrimental eavesdropper is written as  
\[\gamma_{E_{B}}=\max\limits_{E\in\Phi_{3}}\left\{\frac{\left(P_{S}M_{S}|h_{E}|^ {2}L\left(r_{E_{B}}\right)\right)}{P_{A}M_{s}|h_{E}|^{2}L\left(r_{E_{B}}\right) +\sigma_{E}^{2}}\right\}, \tag{9}\]  
or  
\[\gamma_{E_{B1}}=\frac{\max\limits_{E\in\Phi_{2}}\left(Pm_{S}|h_{E}|^{2}L\left( r_{E_{B1}}\right)\right)}{\sigma_{E}^{2}}, \tag{10}\]  
where \(E\in\Phi_{3}\) denotes that eavesdroppers may reside in the sector secrecy guard zone, and then the SNR at the eavesdropper is \(\gamma_{E_{B}}\). \(\gamma_{E_{B1}}\) is the SNR of the eavesdropper at \(E\in\Phi_{2}\). The distance \(r_{E_{B}}\) from the eavesdropper to the transmitter is smaller than the radius \(r\) of the sector secrecy guard zone. \(r_{E_{B1}}\) is the distance from the eavesdropper to the transmitter in the side lobes. Note that, in our paper, to prevent eavesdroppers from eavesdropping, the transmitter adds AN to the transmit signals. AN is generated so as to be canceled out at the legitimate receiver; thus, only eavesdroppers are affected by AN. Similar methods are presented in [39; 40; 41]. However, it is beyond the scope of our paper and could be our future research.
## 1 Introduction
[26] have introduced a novel T-Splines-oriented Isogeometric topology optimization (T-ITO) method to resolve the challenges of designing shell and plate structures with arbitrary geometries. Traditional NURBS-based approaches face limitations in handling complex structures due to their tensor product nature. This research study establishes the T-ITO method as a superior approach for optimizing the topology of shell and plate structures with complex geometries in technical applications. According to the literature review, it appears that the study of LPG cylinders by the IGA approach using the T-Splines function has not been carried out yet. In this work, we will integrate and adapt IGA with the T-Splines function for geometry modeling and numerical analysis in the linear elasticity case to study the LPG cylinder under static loading conditions. This work will make a significant contribution and enrich current research on the understanding of IGA with T-Splines, implemented for the numerical modeling of curved structures and also to evaluate the mechanical structure efficiency of LPG cylinders in comparison with other numerical methods like the FEM and IGA NURBS. This works is structured into three sections. The primary section focuses on the mathematical background, including the T-Splines function basis, the Bezier extraction of the T-Splines function. The following section explains the steps to prepare, draw and extract geometry via the Rhino V5 and T-Splines plugin for finite element analysis. Additionally, we model an LPG cylinder subjected to a static load applying the FEM in Abaqus/Standard,and the IGA with the T-Splines method is implemented in the Matlab 2021 software. In the last section, the focus will be on the displacement and stress numerical results of a liquefied petroleum gas (LPG) cylinder subjected to a static mechanics loading issue. The numerical results obtained are examined and verified with the FEM in Abaqus/Standard.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Simulation Results
#### 4.2.4 Packet Delivery Ratio  
In the network domain, the ratio of data packets that the destination (sink) receives to the total packets generated by sources (nodes) is called the packet delivery ratio (PDR). The improvement of this factor leads to an increase in the number of active nodes. Furthermore, the greater the number of simulation rounds, the more PDR will be reduced. The PDR for each round of simulation is calculated with Equation (26):  
\[\text{PDR}=\frac{\text{Numberofpackets received by thesink}}{\text{Totalpackets generated bynodes}} \tag{26}\]  
As shown in Figure 13, considering that the worst link quality is removed by the fitness function (Equation (8)) and also that its routing tree is based on modified priority encoding, the proposed method has a higher average packet delivery ratio than other methods in 40 rounds of simulation. Furthermore, the numerical result demonstrates that PDR is increased in our method by about 1.07 to 1.38 times compared to PSO-HSA and by about 1.27 to 1.39 times compared to TPSO-CR for the different sink locations. It is clearly exhibited that LEACH has the lowest average PDR.  
Figure 12: Network coverage for sink locations (**a**) (100, 100), (**b**) (0, 100), (**c**) (50, 150), and (**d**) (50, 200).
# A Review of Deep Learning Applications for Railway Safety
## 6 Operation Safety
### Train Running Safety  
There are many studies on monitoring the current state of train operation and quantifying train running safety [84, 85, 86, 87]. However, these studies have mainly considered limited situations that can be monitored relatively simply, such as train bridges and tunnel passes. [PERSON] et al. [88] presented a model that combines deep neural networks and recurrent neural networks for efficient train-running safety prediction. Their proposed model processed train vibration data, which was measured by an accelerometer, and predicted the wheel detail coefficient, wheel rate of 1 ad reduction, and wheel lateral pressure. Numerical analyses were conducted using the transit simulation and the actual train-railway model, and these analysis results revealed that the proposed method has better prediction performance.
## 3 Analysis of Test Data
### Dynamic Performance Analysis
#### 3.2.4 Peak Strain Analysis  
The peak strain can also reflect the degree of failure and breakage to a certain extent.  
The peak strain first decreased and then increased with the increase in temperature, and reached the inflection point at 45 \({}^{\circ}\)C, while the correlation coefficient reached 0.9713 and 0.9720, respectively, indicating an obvious correlation. Consistent with the dynamic tensile strength and average strain rate, the water bonding force and Stefan effect increased the specimens' strength, so the peak strain decreased. As the water temperature increased, damage was the main effect, making the specimens easier to damage and increasing the peak strain. According to the figure, the integrity, peak strength, and peak strain of intact sandstone specimens are higher than those of annular sandstone specimens. In the impact test, the annular hole played a buffer role, and the inner ring was squeezed first when it was impacted by the pressure bar, so the peak strain of the annular sandstone specimen was slightly lower than that of the intact sandstone specimen.  
The variation of peak strain with water temperature is shown in Figure 11.  
\[\left.\begin{aligned} \varepsilon_{T1}&=4.101-0.076t+9.554 \times 10^{-5}t^{2}\,\left(\mathrm{R}^{2}=0.9713\right)\\ \varepsilon_{T2}&=4.343-0.051t+7.021\times 10^{-5}t^{2} \,\left(\mathrm{R}^{2}=0.9720\right)\end{aligned}\right\} \tag{7}\]  
where \(\varepsilon_{T1}\) is the annular sandstone specimen's peak strain; \(\varepsilon_{T2}\) is the intact sandstone specimen's peak strain.
## 3 Result and Discussion  
### Accuracy Test of the Impedance Measurement System  
The DFT-based impedance measurement system showed an accuracy for the impedance magnitude of 1 k\(\Omega\) to 20 k\(\Omega\) in the frequency range of 200 Hz to 50 kHz. Figure 2 shows the real and imaginary part of the impedance spectra of the RRC circuit measured with respect to different R (1% tolerance) and C value by the DFT-based impedance measurement system (line) in comparison to the commercialized product (SP-200, Biologic Science Instruments, France). The impedance data measured with the  
Figure 1: (**a**) Experimental setup for impedance characterization of pork tissue including ADuCM350 discrete Fourier transform (DFT) based impedance measurement device (1), C program window (2), LabVIEW GUI (3), ultrasound transducer (4), ultrasound image display (5), pork tissue (6), monopolar needle injection working electrode (7), and Ag/AgCl counter electrode (8), (**b**) Schematic of the electrode configuration of the impedance measurement circuit.  
DFT-based impedance measurement system showed a good agreement with the commercialized product, indicating the normal working of the system.
## 2 Materials and Methods
### System Description
#### 2.2.2 IEEE Manager  
In order to receive and visualize the processed data, a smartphone application was developed. The MORFEO OpenHealth Project developed the HDP/MCAP implementation on the official Linux Bluetooth protocol stack, BlueZ 4.77 [27]. This implementation was integrated by Google in Android API 27 and newer versions to support HDP through the Bluetooth Health class.  
We used this class to implement an IEEE manager that is able to understand our SpO2 and ECG data through HDP.  
When the HDP connection is established, the manager will wait for the agent application layer (Continua) association request. In this association, the agent and the manager establish the configuration and the manager starts receiving data.  
The application consists of a main menu that leads to two different modes, one for SpO2 and the other for ECG. In the SpO2 mode, the application provides a visualization of the connection status, the SpO2 level, and the date and hour of the sent packet. In the same way, in the ECG mode the application provides the visualization of the connection status, the HR, the date and hour of the acquisition and the ECG waveform. Figure 6 shows an example of the ECG application where the waveform from an ECG waveform generator is displayed. Although the evaluation versions included two screens with ECG and Sp02 data, the clinicians' recommendation was to simplify with the ECG reading only.  
Figure 5: Representation of the different object classes in the ISO/IEEE 11073-10406 Basic ECG device specialization.
## 4 Sensitivity Analysis of Bearing Capacity Parameters of a Layered Foundation
### The Shear Strength of Soil  
Figure 9a shows the variation of the ultimate bearing capacity of the foundation under different ratios of cohesive forces \(c_{1}/c_{2}\) of the double-layer soil. It can be seen from the figure that there is a weak linear increase between the ultimate bearing capacity of the foundation and the ratio of cohesive forces. The larger the ratio of \(c_{1}/c_{2}\), the higher the growth rate of the bearing capacity. The reasoning is as follows: the cohesion of soil characterizes the attraction between soil particles. As the cohesion of soil increases, the interaction force between soil particles also increases. As a result, the internal structural stability and shear strength of the soil are enhanced, thereby improving the ultimate bearing capacity of the foundation.  
Figure 8: The influence of the width to depth ratio of the foundation and the thickness of the overlying soft clay layer. (**a**) Different width to depth ratios; (**b**) Different thicknesses of overlying soft clay layer.  
When the cohesive force of the underlying soil is 8 kPa and a is 1, 1.5, 2, and 3 respectively, the ultimate bearing capacity of the foundation is 1263.9 kPa, 1312 kPa, 1360.1 kPa, and 1456.4 kPa, with the highest increase of 7%. As the cohesion of the lower soil increases, although the growth rate shows an upward trend, the maximum growth rate is only 13.8%. It can be seen that although the ratio of cohesive forces of double-layer soil has an improvement effect on the ultimate bearing capacity of the foundation, the degree of influence is relatively small compared to the impact of changes in the size of the foundation.  
Figure 9b shows the variation of the ultimate bearing capacity of the foundation under different tangent ratios \(\tan\!\varphi_{1}/\tan\!\varphi_{2}\) of the internal friction angle of the double-layer soil. It can be seen from the figure that the ultimate bearing capacity of the foundation shows a strong nonlinear upward trend with the increase of the internal friction angle of the double-layer soil. When the internal friction angle of the underlying soil is less than 25\({}^{\circ}\), the increase in the ultimate bearing capacity is relatively small. However, when the internal friction angle of the underlying soil is greater than 25\({}^{\circ}\), the increase in the ultimate bearing capacity begins to increase. Moreover, the larger the tangent ratio of the internal friction angle of the double-layer soil, the more significant the increase in ultimate bearing capacity. The reasoning is as follows: when the friction angle inside the soil increases, the mutual friction force between the soil increases, thereby improving the shear strength of the soil. When subjected to compression from the upper load, the soil on the side of the foundation will prevent the transfer of shear force, achieving the effect of improving the bearing capacity of the foundation.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
###### Abstract  
Currently, indoor locations based on the received signal strength (\(RSS\)) of Wi-Fi are attracting more and more attention thanks to the technology's low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy \(RSS\) to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.  
Keywords: indoor location recognition; received signal strength (\(RSS\)); Wi-Fi fingerprint positioning; deep neural network (DNN); optimization methods; adaptive filter; hidden Markov models (HMM) +
Footnote †: journal: Applied Sciences
# Role of Restored Underwater Images in Underwater Imaging Applications
## 1 Introduction
Instead of estimating the transmission map directly, [PERSON] et al. [17] use a depth-estimation strategy to restore the underwater images. The proposed method involves the use of depth estimation and transmission map estimation with attenuation coefficient priors, by considering the backscattering effect, and has been proven to show superior results [18]. Generally, the efficiency of an image-processing algorithm is calculated by comparing its output processed underwater images to other similar algorithms, and by using quality metrics, such as Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), and dedicated underwater performance metrics, including Underwater Color Image Quality Evaluation (UCIQE) [19] and Underwater Image Quality Measure (UIQM) [20]. These are more common measures in evaluating image processing methods. However, there is no approach to evaluate an image processing algorithm based on its applicability in real applications. This is very important since the motive of developing an image-processing method is not only to restore or enhance the underwater images, but ultimately, to help  
Figure 1: Light absorption in water and penetrating levels of different colors. improve the efficiency of the real applications. In this paper, the first ever approach to evaluating the efficiency of the proposed algorithm based on its usefulness for underwater applications, has been shown. The contributions of the paper are: (1) proposing an underwater image restoration method, which estimates depth maps using combinations of a blurriness map, background light neutralization, and red-light intensity. The background light neutralization is estimated using the four-quadrant method, which demands lower computation compared with other methods [18], and (2) demonstrations of the restored underwater image using the proposed method on different underwater applications to evaluate the efficiency of the algorithm. This represents the first ever demonstration of a developed algorithm on real underwater applications. The structure of the rest of the paper is as follows: Section 2 describes the proposed underwater image restoration method that may be used to effectively recover original images from acquired underwater images. Consequently, the usages of the recovered underwater images on different underwater applications are explored in Section 3. Section 4 discusses results from the proposed restoration method, as well as its implementation on the selected underwater applications. The last section concludes the paper.
## 2 Related Work
### Security Computing Methods Based on Noise or Frameworks
#### 2.2.2 Federated Learning  
Federated learning (FL) [27] is an innovative distributed machine learning method that builds shared machine learning models among multiple data holders without centralizing or directly exchanging data. This method's greatest advantage is its ability to protect data privacy and security, making it particularly suitable for fields with stringent privacy requirements such as healthcare, finance, and personal services [28; 29; 30]. The core principle of federated learning is to distribute the model training process across multiple data nodes with each node responsible for training the model on local data without directly exchanging data. The model parameters or updates obtained from each node are aggregated on a central server, which integrates and updates the global model. This method's computation can be expressed as  
\[\theta_{\text{global}}\leftarrow\theta_{\text{global}}+\eta\sum_{k=1}^{K}\frac{ n_{k}}{n}\Delta\theta_{k} \tag{4}\]  
where \(\theta_{\text{global}}\) is the global model parameter, \(K\) is the total number of participating nodes, \(n_{k}\) is the data volume of the \(k\)-th node, \(n\) is the total data volume, \(\Delta\theta_{k}\) is the model parameter update uploaded by the \(k\)-th node, and \(\eta\) is the learning rate. In this way, each participant only needs to upload their model updates rather than the original data, ensuring data privacy while facilitating collaborative model training.  
Although federated learning provides robust privacy protection, this technology still faces several challenges. The primary challenges include effectively managing and synchronizing model updates in distributed networks and ensuring the efficiency and effectiveness of model training [31]. In multi-node environments, network latency, data inconsistency, and heterogeneity among participating nodes can all impact the model training performance.  
To overcome these challenges, an efficient synchronization mechanism and a robust model aggregation strategy have been designed to ensure effective model training and updating even under unstable network conditions or uneven data distribution among nodes. Furthermore, advanced optimization algorithms have been employed to enhance the convergence speed of model training, improving the overall efficiency and usability of the system. In summary, federated learning provides a new method for protecting data privacy through distributed learning mechanisms. The integration of this method with large language models and user attention mechanisms further enhances the privacy protection capabilities of the framework. It is anticipated that continuous technological innovation and optimization will provide safer and more effective privacy protection solutions for future data processing and machine learning applications.
###### Abstract  
It has been reported that fulvic acid (FA) application improves soil structure and nutrient availability. However, the effects of combined application of urea (U) and FA solution on the photosynthesis and nitrogen metabolism in maize (_Zaa mays_ L.) have rarely been reported. In this study, pot experiments were conducted in 2017 and 2018, and the effects of combined application of urea and FA solution (U+FA) on soil available nutrient contents, maize endogenous hormone concentrations, carbon and nitrogen metabolism-related enzyme concentrations, maize yield, and nitrogen use efficiency (NUE) were researched. Compared with the U treatment, the maize yield and NUE in the U+FA treatment were significantly increased by 8.31% and 17.09 percentage points in 2017 and by 16.90% and 24.31 percentage points in 2018. At the jointing and 12-leaf (V12) stages of maize, soil NH\({}_{4}\)+ content increased by 139.32% and 12.08%, separately, in the U+FA treatment. At the V12 stage, the auxin, nitrate reductase, nitrite reductase, and glutamine synthetase concentrations in maize root were increased by 42.31%, 74.17%, 16.61%, and 45.60%, respectively, and the concentrations of pyruvate phosphate dikinase and phosphoenolpyruvate carboxylase in maize leave were increased by 29.40% and 42.96%, respectively, in the U+FA treatment. The combined application of urea and FA solution significantly improved soil nutrient availability, increased the concentrations of endogenous hormones in maize, stimulated the activities of enzymes related to nitrogen metabolism, promoted the photosynthetic carbon assimilation efficiency, and ultimately improved crop yield and NUE.  
f 14 April 2022  
1 2022