esa-sceva/satcom-chunk-collection huggingface dataset:
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 4 Numeric Examples
### DFRC Waveform Design for Communication Performance  
The CM waveform obtained by WAPM is used as the radar referenced waveform which would be further fed into the DFRC system. In this section, we will design the integrated DFRC waveform by DFRC-MA where the QPSK modulation signal is selected as the communication-reference signal. The number of communication users is \(K=3\). Other parameters are the same as in the previous section. Variables \(\mathbf{Y}^{(0)}\), \(\boldsymbol{\lambda}^{(0)}\) are initialized to be \(\mathbf{0}\), and variable \(\mathbf{X}^{(0)}\) is initialized to be the random matrix. For comparison, the BnB method in [18] is considered to make the comparison. We studied the communication performance vs. SNR with different algorithms and trade-off factors. In Figure 7, when the trade-off factor is set as \(w=0\), the communication performance has been lost, which is consistent with our objective problem. When \(w=0.25\), it can be found that the larger the trade-off factor, the higher the communication rate and the bit error rate will be, while the increase in the trade-off factor will increase the communication rate at the expense of the bit error rate.  
In Table 2, we also compare the CM performance and consumption time of the DFRC-MA and BnB algorithms. The DFRC-MA algorithm can achieve strict CM constraint, owing to its Riemannian solution space. Our proposed DFRC-MA as an iterative closed optimizing manner consumes less time than the BnB algorithm.
# Model Test of Micro-Pile Group Reinforcing High Steep Landslide
###### Abstract  
High steep landslides are a major concern for infrastructure construction in the mountainous areas of Western China. The micro-pile technique has been gradually used to prevent landslides, due to convenient construction and good performance. However, the application of the micro-pile technique on landslide prevention was generally implemented on the front edge of landslides, which is not applicable for the high steep landslides due to the limited operation space. In this study, a large-scale model test on the performance of a micro steep pile group on the prevention of high steep landslides was conducted in order to implement the micro-pile on the top of landslides. The force-deformation characteristics and failure modes of the steel pipe micro-pile group reinforcing high steep landslides were investigated. The test results showed that the landslide thrusts acting on the micro-pile group showed a triangle distribution. The maximum soil earth pressure was observed near the slip surface during landslides. The resistance of the micro pole group was distributed in an inverted triangle, mainly in the upper half of the loaded section. The sliding bed resistance is unevenly distributed along the height direction, and is larger near the slip surface. Once the landslide occurred, the force distribution of each row of steel pipe micro-pile was basically the same. The bending moment of the loaded section of the steel pipe micro-pile was mostly negative, with a larger bending moment in the range of eight times the pile diameter above the slip surface. The largest bending moment value is located at two times the pile diameter on the slip surface. On the other hand, the bending moment of the embedded section of the steel pipe micro-pile is mostly positive, showing a tension state with a maximum value at four times the pile diameter under the slip surface. This implies that the role of loaded and embedded sections of the micro-pile group on the landslides is different. The failure mode of the micro-pile group was mainly attributable to the bending failure within eight times the pile diameter above and below the slip surface.  
micro-pile group; landslide; model test; force distribution; failure mode +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Applications Key IoT Technologies for Energy-Efficient 6G in Smart Cities
It also supports privacy and a high-level protection and support system for healthcare workers, professionals and patients, thus maximizing the advantages of its application [37]. 6G wireless communication can also provide haptic communication, as it is a nonverbal communication medium that employs the sense of touch. This is extremely useful for remote users, as they can experience haptic communication and interaction in the real world. The superior features of the 6G application are useful in haptic communication in smart cities among the members and individuals. Similarly, a 6G wireless communication network can be applied in industry for manufacturing and automation purposes. By providing full automation to provide secure communication and interaction mediums, 6G wireless communication can ensure high-data-quality transfer that is nearly error-free. Another significant aspect of employing a 6G system is data transfer without any data loss when transmitted and received by the end-user [42]. Hence, the application of IoT devices is revolutionizing the people's lives through the new state-of-the-art, i.e., 6G wireless communication in smart cities.
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 4 Analytical Power Consumption Model
Based on the above equations, the transmission power of digital scheme with QAM modulation can be calculated as:  
\[P_{\mathrm{TX}}^{\mathrm{QAM}}\left[dB\right]=\mathrm{SNR_{req}^{QAM}\left[dB \right]}-10\log_{10}\left(\left|h\right|^{2}\right)+N\left[dB\right]. \tag{16}\]  
Finally, substituting Equation (16) to Equation (8), the analytical power consumption model of the digital transmission with QAM modulation is obtained as:  
\[ \begin{split} p_{\mathrm{totD}}^{\mathrm{QAM}}=& P_{ \mathrm{CD}}^{\mathrm{QAM}}+\frac{4}{\pi}\sqrt{10^{\frac{p_{\mathrm{TX}}^{ \mathrm{QAM}}\left|dB\right|}{10}}\cdot PAR\cdot P_{\mathrm{T}}\left(t\right)} +15.438\cdot 10^{-6}f_{\mathrm{s}}^{0.6036}\\ &+16.437\cdot 10^{-5}f_{\mathrm{s}}^{0.447}+\xi B_{\mathrm{sig}}\log_{2} \left(1+10^{\frac{\mathrm{SNR_{req}^{QAM}}\left|dB\right|}{10}}\right).\end{split}  \tag{17}\]  
In Figure 10, the power consumption of the digital and analog scheme resulting from the simulation and analytical model vs. distance between transmitter and receiver for \(\mathrm{MSE}=10^{-5}\), \(B_{\mathrm{sig}}=150\) kHz and \(\xi_{\mathrm{FM}}=0.5\) is presented. In the case of the digital scheme, the lower and upper bound of the power consumption are plotted. It can be observed that the simulation results are between curves obtained by the upper and lower bound. Moreover, in the case of the analog scheme, the approximation fits to the simulation results very well. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Uncoded QAM**} & \multicolumn{2}{c}{\(a_{\mathrm{uncoded}}^{\mathrm{QAM}}\)} & \multicolumn{2}{c}{\(b_{\mathrm{uncoded}}^{\mathrm{QAM}}\)} & \multicolumn{2}{c}{\(c_{\mathrm{uncoded}}^{\mathrm{QAM}}\)} \\ \cline{2-7}  & **lower** & **upper** & **lower** & **upper** & **lower** & **upper** \\ \hline \(\mathrm{MSE}=10^{-4}\) & 50.42 & 90.86 & \(-1.037\) & \(-1.176\) & 8.575 & 9.43 \\ \(\mathrm{MSE}=10^{-5}\) & 49.88 & 88.68 & \(-1.01\) & \(-1.162\) & 9.144 & 10.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fitting parameters for the uncoded digital transmission scheme. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Coded QAM**} & \multicolumn{2}{c}{\(a_{\mathrm{coded}}^{\mathrm{QAM}}\)} & \multicolumn{2}{c}{\(b_{\mathrm{coded}}^{\mathrm{QAM}}\)} & \multicolumn{2}{c}{\(c_{\mathrm{coded}}^{\mathrm{QAM}}\)} & \multicolumn{2}{c}{\(d_{\mathrm{coded}}^{\mathrm{QAM}}\)} \\ \cline{2-7}  & **lower** & **upper** & **lower** & **upper** & **lower** & **upper** & **lower** & **upper** \\ \hline \(\mathrm{MSE}=10^{-4}\) & 39.04 & 45.01 & \(-0.5964\) & \(-0.3550\) & 26.43 & 22.38 & \(-0.1168\) & \(-0.07004\) \\ \(\mathrm{MSE}=10^{-5}\) & 30.85 & 46.03 & \(-0.1491\) & \(-0.4444\) & 1.931 & 28.02 & \(-0.02527\) & \(-0.07516\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The fitting parameters for the coded digital transmission scheme.
# Design and Fabrication of a Novel Window-Type Convection Device
## 4 Conclusions  
This paper presented a window-type convection device prepared in accordance with the principles underlying the operation of airflow multipliers. The air entrainment performance of the convection device was optimized by designing its wind-deflector plate in the shape of an airfoil and adjusting the outlet slit size. The analysis results revealed that the mass flow rate of the convection device increased alongside the airfoil camber and thickness. Although decreasing the slit size reduced the flow velocity, it also facilitated improving the entrainment of outdoor air, which is affected by ambient pressure; more specifically, reducing the size of the slit induced lower ambient pressure around it, thereby enhancing the entrainment of outdoor air. Moreover, the mass flow rate at the convection device outlet was 11.6 times that at the inlet, implying that the device has the potential to improve convection between indoor and outdoor airflows, and then to reduce energy consumption and carbon emissions.  
The NACA4424 airfoil (namely, 4% relative camber, maximum camber located 40% from the leading edge and maximum thickness of 24% of the chord length) with a 1.2 mm slit, which delivered the highest air entrainment performance during simulation, was applied to conduct an experiment and validate the simulated results. The mean error between the experimental and simulated mass flow rates was 6.3%, suggesting that the ANSYS Fluent simulation was highly accurate, and that the convection device was feasible.  
Conceptualization, Y.-H.G. and W.-H.H.; methodology, Y.-H.G. and W.-H.H.; software, H.-T.L.; validation, H.-T.L., [PERSON]-H.G. and W.-H.H.; formal analysis, H.-T.L. and Y.-H.G.; investigation, H.-T.L., [PERSON]-H.G. and [PERSON]-H.H.; resources, Y.-H.G.; data curation, H.-T.L. and W.-H.H.; writing--original draft preparation, W.-H.H.; writing--review and editing, W.-H.H.; visualization, H.-T.L.; supervision, [PERSON]-H.G. and [PERSON]-[PERSON]; project administration, [PERSON]-H.G. and [PERSON]-[PERSON]; funding acquisition, Y.-H.G.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 15: Mass flow rate measured under simulated and experimental conditions at different operating voltages of the centrifugal fan.
## 3 Results and Discussion
### Ternary Transition Metal Nitrides as Superconductor  
Figure 8 illustrates the impact of substituting different elements in place of niobium (Nb) within the NbN compound on the predicted Tc. The graph showcases how Tc varies with the level of substitution for several dopants, each represented by a unique color and symbol on the plot (smoothened using Spline interpolation). For each dopant, the x-axis indicates the proportion of Nb that has been replaced by the doping element, ranging from 0 (pure NbN) to 1 (completely substituted). The y-axis displays the predicted Tc values, highlighting how each element's incorporation influences the superconducting behavior of NbN.  
Figure 7: Figure illustrating the relationship between atomic radius and electronegativity for various transition metals, with a specific focus on identifying potential substitutions for Nb in the NbN. The blue scatter points represent the general population of transition metals, mapping their atomic radius against their electronegativity. In contrast, the magenta and green points highlight transition metals with lattice structures and valence electrons similar to those of Nb, respectively. The magenta points, larger and semi-transparent, denote metals with a crystal structure akin to Nb, while the green points, medium-sized and semi-transparent, indicate metals sharing Nb’s valence. Elliptical contours in green and red outline regions where elements closely adhere to Hume–Rothery rules regarding atomic size and electronegativity compatibility with Nb.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 4 System Background
### Attack Model  
The attack model for the network under study is the Dolev-Yao model [23], by which attackers are assumed to be rational, powerful, and fully controlled entities capable of intercepting, tampering with, and sending messages. They can also attempt to break the security function of the protocol by analyzing communication content. In the RAN domain of the 5G network, there are security vulnerabilities in the wireless communication between MTCDs and gNBs, making them prone to malicious attacks such as information interception and tampering. According to the specification [21], the N2 interface employs IPSec and IKEv2 certificates to protect communications, ensuring their integrity and confidentiality, and preventing replay attacks. Therefore, the connection between the 5G core network and the gNB is considered secure. However, the connection between MTCDs and gNBs is weaker and potentially insecure. It is assumed that the interior of the 5G core network and its network functions are secure, ensuring the safety of connections between network functions. In contrast, other entities in the RAN are not completely trusted.  
Given these considerations, an ideal 5G identity handover authentication protocol should support security features including device anonymity, bidirectional identity verification, secure data transmission, and perfect forward secrecy. Additionally, it should be capable of defending against active attacks, including impersonation, linkability attacks, replay attacks, man-in-the-middle attacks, and DoS attacks, as well as passive attacks like eavesdropping and location tracking.
## 2 Overall System Design  
The overall design of the collision-free HRC system is shown in Figure 1. The proposed HRC system comprises motion capture, action recognition, motion prediction, decision-making, path planning, and robot controller modules. The motion capture module relies on OptiTrack cameras to generate high-quality 3D streaming skeleton pose estimates at a sampling rate of 20 frames per second. Using recorded spatial-temporal data for a human performing task-related motions/activities as training data, a motion prediction module is pretrained to predict the future one-second evolution of human poses (20 frames) based on the motion observed over the previous one-second interval. The decision-making module is implemented using NMPC-ECBF-based motion planning and robot execution mechanisms. In our work, a robot starts from a random position in the working environment and continuously searches for a collision-free path to the estimated interaction coordinates based on the received one-second ahead pose estimation feedback provided by the predictor module.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Ranking by Support**} & \multicolumn{3}{c}{**Ranking by Confidence**} \\ \hline
**Association Rules** & **Support** & **Confidence** & **Association Rules** & **Support** & **Confidence** \\ \hline
9\(\rightarrow\)25 & 0.615 & 0.8723 & 75\(\rightarrow\)25 & 0.255 & 1.0000 \\
25\(\rightarrow\)9 & 0.615 & 0.7593 & [9,39]\(\rightarrow\)25 & 0.28 & 0.9032 \\
50\(\rightarrow\)25 & 0.57 & 0.8906 & [9,50]\(\rightarrow\)25 & 0.465 & 0.9029 \\
25\(\rightarrow\)50 & 0.57 & 0.7037 & 50\(\rightarrow\)25 & 0.57 & 0.8906 \\
50\(\rightarrow\)9 & 0.515 & 0.8047 & 9\(\rightarrow\)25 & 0.615 & 0.8723 \\
9\(\rightarrow\)50 & 0.515 & 0.7305 & [25,50]\(\rightarrow\)9 & 0.465 & 0.8158 \\
[9,50]\(\rightarrow\)25 & 0.465 & 0.9029 & 50\(\rightarrow\)9 & 0.515 & 0.8047 \\
[25,50]\(\rightarrow\)9 & 0.465 & 0.8158 & [25,39]\(\rightarrow\)9 & 0.28 & 0.7887 \\
[9,25]\(\rightarrow\)50 & 0.465 & 0.7561 & 25\(\rightarrow\)9 & 0.615 & 0.7593 \\
50\(\rightarrow\)[9,25] & 0.465 & 0.7266 & [9,25]\(\rightarrow\)50 & 0.465 & 0.7561 \\
9\(\rightarrow\)[25,50] & 0.465 & 0.6596 & 69\(\rightarrow\)50 & 0.25 & 0.7353 \\
[9,39]\(\rightarrow\)25 & 0.28 & 0.9032 & 9\(\rightarrow\)50 & 0.515 & 0.7305 \\
[25,39]\(\rightarrow\)9 & 0.28 & 0.7887 & 50\(\rightarrow\)[9,25] & 0.465 & 0.7266 \\
39\(\rightarrow\)[9,25] & 0.28 & 0.6222 & 25\(\rightarrow\)50 & 0.57 & 0.7037 \\
75\(\rightarrow\)25 & 0.255 & 1.0000 & 9\(\rightarrow\)[25,50] & 0.465 & 0.6596 \\
69\(\rightarrow\)50 & 0.25 & 0.7353 & 39\(\rightarrow\)[9,25] & 0.28 & 0.6222 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The association rules mined by frequent item set mining. **Author Contributions:** [PERSON] wrote the main parts of the paper; [PERSON] and [PERSON] designed and performed the simulations in the case study; [PERSON] and [PERSON] analyzed the result. **Funding:** The work was funded by the National Natural Science Foundation of China, Grant 71690233 and 71571186. **Acknowledgments:** We thank the C-MAPSS developers and the National Aeronautics and Space Administration (NASA) for providing the engine simulation data inputs. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 DNSGA Algorithm
### Spatial Density Ordering  
The spatial density expressions for the two individuals of \(Y_{1}=\left(x_{1}^{1},x_{1}^{2},\ldots,x_{1}^{n}\right)\) and \(Y_{2}=\left(x_{2}^{1},x_{2}^{2},\ldots,x_{2}^{n}\right)\) with n-dimensional decision variables is expressed as Formula (1).  
\[d(Y_{1},Y_{2})=\sum_{i=1}^{n}\left|x_{1}^{i}-x_{2}^{i}\right|\,(1\leq i\leq n) \tag{1}\]  
Empirically, the influence of the decision variables of different dimensions on the aerodynamic performance of the centrifugal impeller varies widely. Therefore, different weights are assigned to different decision variables. The mathematical expression of spatial density with weights is Equation (2).  
\[d_{w}(Y_{1},Y_{2})=\sum_{i=1}^{n}w_{i}\Big{|}x_{1}^{i}-x_{2}^{i}\Big{|}\,(1\leq i \leq n,w_{i}\geq 0) \tag{2}\]  
where \(w_{i}\) is the weight of the \(i\)th dimension variable.  
In this paper, a spatial density operator ranking with weights is used to remove the redundant individuals in \(M2\), and the procedure is as follows:  
1. \(M1\) and \(M2\) are combined into group \(Q\).
2. The two individuals with the lowest spatial density in \(Q\) are found, and at least one belongs to \(M2\).
3. If one individual belongs to \(M1\) and the other belongs to \(M2\). The individual belonging to \(M2\) is directly removed from \(Q\). If both individuals belong to \(M2\), the one who has a lower spatial density with other individuals in \(Q\) is deleted.
4. Return (2) and (3) in a loop until the total number of individuals in Q reaches the population size.
## 2 Methods
### Receptive Field Block  
Neuroscientific research has demonstrated that the population receptive field size in retinal mapping correlates with the eccentricity of the retinal stimulus. Specifically, as eccentricity increases, the scale of the human visual receptive field also expands, meaning that the closer the target area is to the center of the receptive field, the more important its location becomes [37]. The receptive field block (RFB) proposed by RFBNet [36] uses different scales of conventional convolutions and dilated convolutions in each branch, simulating multiple receptive fields within the population receptive field by adjusting convolutional kernel dimensions. Dilated convolution in each branch simulates the dependence of population receptive field size on eccentricity. Therefore, we attempt to incorporate the RFB into the network to improve deep feature extraction from BCHI, while avoiding a significant increase in computational cost.  
RFB consists of multi-branch convolutional layers with different kernel sizes and dilated convolution layers. According to research [38; 39; 40], the performance of multi-scale receptive field implemented by convolution kernels of different sizes is better than that of single-scale receptive field implemented by fixed-size convolution kernels. Specifically, the RFB begins by applying a \(1\times 1\) convolution to reduce the number of channels and form a bottleneck structure, followed by a standard n \(\times\) n convolution. To replace the original \(5\times 5\) convolution, two stacked \(3\times 3\) convolutions are employed, which reduce parameter count while enhancing nonlinear representation. To accommodate the limited depth of the network, we adopt the RFB-s module [36] and replace the original \(3\times 3\) convolution with a combination of \(1\times 3+3\times 1\) convolutions. The additivity of convolution is illustrated by the following equation:  
\[P(x)=W_{1}*(W_{2}*x) \tag{2}\]  
where \(W_{1}\) and \(W_{2}\) represent the convolution of dimensions \(1\times 3\) and \(3\times 1\), respectively, \(*\) denotes convolution process, and \(x\) denotes the input feature map. The RFB-s module [36] adds a dilated convolution after each normal convolution. Expansion convolution can capture more information in a larger area and generate a higher resolution feature map with constant parameters. The detailed structure of RFB can be shown in Figure 2.  
Figure 2: Structural details of the receptive field block (RFB) module. The module contains four parallel branches. The first branch uses a \(1\times 1\) convolution, while the remaining three apply convolutions with different kernel shapes and dilation rates to capture multi-scale contextual features. The outputs are concatenated and fused with a \(1\times 1\) convolution, followed by a residual connection and ReLU activation.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Applications
#### 2.3.3 Healthcare  
Federated learning has a promising application in the healthcare domain because of its data privacy-preserving properties. For individual healthcare organizations, the amount of patient data may not be sufficient to train predictive models that can be put into use [41]; therefore, the use of federated learning techniques can build federated networks for cross-regional hospitals with similar medical information. The emergence of federated learning breaks down the barriers that prevent data from being shared between different healthcare organizations for disease analysis.  
In [42], federated learning is applied to the collection and analysis of electronic health records, enabling the development of secure data harmonization and federated computational procedures to transform extensive electronic health records into meaningful phenotypic clinical concepts for analysis.  
In [43], federated learning is applied to address the problem of searching for similar patient matches. The similarity between patients is efficiently computed using their hash codes, enabling healthcare organizations to summarize the general characteristics of similar patients.  
Automatic extraction of medical information using an unstructured text format can be performed by federated learning. [PERSON] et al. [44] combined federated learning with natural language processing for the first time, which can make full use of clinical records from different hospitals to train a machine learning model specific to a medical task and improve the quality of a particular clinical task.