esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 4 Improved DQN Algorithm  
The DQN algorithm is one of the core algorithms in deep reinforcement learning, aiming to approximate the value function through representation learning. Unlike traditional Q-learning, the DQN leverages the powerful fitting and approximation capabilities of DNNs by taking the current state as input and outputting the corresponding Q-values for each possible action. Through training the DNN, the DQN algorithm gradually optimizes the Q-value predictions by minimizing the mean squared error between the predicted Q-values and the target Q-values. The core idea of the DQN is to enhance learning stability through experience replay and target networks. The experience replay mechanism stores the transitions encountered by the Agent while exploring the environment in a buffer, and during training, small batches of experience samples are randomly drawn for learning. This approach effectively reduces the correlation between samples, preventing drastic fluctuations in network parameter updates.  
Therefore, deep reinforcement learning can be applied to solve decision-making problems with complex state and action spaces. In this paper, we design a scenario in HWSNs where super nodes with a high initial energy and computational capabilities act as Agents. These Agents learn optimal action strategies by interacting with the environment to maximize cumulative rewards. In practical applications, the DQN algorithm utilizes the fitting and approximation capabilities of DNNs to find optimal strategies, thereby handling high-dimensional state spaces and complex action spaces while exhibiting strong robustness.
# A Clutter Loss Model for Satellite Communication Systems
## 1 Introduction
The approach consists of launching a single ray for each generated random urban scenario, described by its statistics of buildings' distances and heights. Each ray is subject to reflection and diffraction induced by buildings. The reflection loss is calculated as a function of the reflecting material, angle of incidence, frequency and wave polarization; the ground reflection, not considered in [11], is added to the model as it is relevant for U6G systems whose steerable antennas also point towards the users in the streets. The diffraction loss is calculated according to the knife-edge method [12]. Different combinations of reflections and diffractions are considered depending on the scenario. The clutter loss statistics are finally gathered from a number of simulated scenarios (a single ray is used for each scenario), which are sufficiently high to guarantee statistical reliability. A fitting procedure on the statistics obtained for London and Melbourne is finally proposed to derive an analytical model of more practical use and wider applicability in an urban environment. The remainder of this paper is organized as follows. The rationale of the model is presented in Section 2. The simulated urban environment and its associated statistics (building distance and height) are illustrated in Section 3, while Section 4 describes how the reflection loss and diffraction loss are calculated. The proposed method for clutter loss calculation is summarized in Section 5, and the results obtained at 6 GHz for London and Melbourne are presented in Section 6, together with the derivation of an analytical model. The model validation is described in Section 7. Finally, Section 8 draws some conclusions.
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 2 Integrated Architecture of QNSC and BER-LM  
### Principle of PSK/QNSC  
The principle of QNSC was first proposed in an article published in 2004, which indicated that information-theoretic security against a known-plaintext attack is possible with QNSC [19]. Figure 1 illustrates the process of transforming a 2 PSK signal into an encrypted 8 PSK signal through PSK/QNSC. The 2 PSK signal has only two angles with a difference of 180 degrees, corresponding to 0 and 1, respectively, which can represent 1-bit data. A 2-bit key (k1 and k2) can have four forms (00, 01, 10, and 11) and can represent four directions. With PSK/QNSC, a 2 PSK signal is rotated to four directions to obtain an encrypted 8 PSK signal. For example, by using the 1-bit data D = 0 and the 2-bit key K = (k0, k1) = (0, 1), the 2-bit encrypted data E = (k0, k1 \(\oplus\) b) = (0, 1) can be generated. Then, the 2-bit encrypted data can be mapped to the 8 PSK constellation. By increasing the bit number of the key, you can further obtain encrypted data of 16 PSK, 64 PSK,..., 2 nPSK. With the increase in the bit number of encrypted data, constellation points become more and more dense, and adjacent constellation points are more easily masked by optical noise generated by amplifiers and lasers. This can prevent Eve from identifying accurate signals without knowing the key. The legal receiver can use the key to recover the original 2 PSK signal and obtain accurate original data.
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index
### Construction of Urban Rail Transit Network Model  
In order to study node importance of urban rail transit network, the topology of urban rail transit network should be described and defined. According to different research purposes or different travel behaviors, the abstract way of network is often different. In the literature [18], a set of space syntax analysis method was proposed, which combines urban infrastructure and geographic network. The literature [19] systematically describes the emergence of complex urban area networks, their structure, and possible representations. According to the different network abstraction methods, there are three kinds of abstraction methods: Space L method, dual method, and Space P method.  
The abstract way of Space L is convenient to study the basic topological characteristics of a traffic network. For an urban rail network, the subway station is abstracted as a node, and the two stations with direct rail lines between them are abstracted as an edge. When the dual method is used to model, the result of Space L is taken as the basis, the station interval is taken as the node, and the station is taken as the edge. This modeling method ignores the geographical characteristics of urban rail network and highlights the connection relationship between urban rail networks. The modeling method of Space P mainly reflects the transfer property of an urban rail network. In the abstract method of Space P, the subway station still represents the nodes in the complex network, but for the edge, all stations on the same subway line are directly connected. This abstract way emphasizes the direct relationship between two stations and ignores the number of stations on the same route. The urban rail network model constructed by the Space P method mainly reflects the connectivity between stations. At this time, the connectivity refers to the number of transfers between stations, not the geometric distance between stations.  
Figure 1: Simplified network structure and passenger flow relationship.  
By using the Space L model of complex network, all stations in an urban rail transit network are abstracted as the set of nodes, and the connection between stations is abstracted as the set of connection edges between nodes. The rail transit network graph \(G(V,E)\) is constructed. Among them, \(V=\{i|1,2,\cdots,i,\cdots,N\}\) is the set of nodes, \(E=\left\{e_{ij}|i,j=1,2,\cdots N,i\
eq j\right\}\) is the set of connecting edges, based on which the importance of nodes in urban rail transit network is analyzed.
## 5 Conclusions  
The [PERSON] optimization method uses four parameters, namely, pole arc coefficient, permanent magnet thickness, permanent magnet arc radius, and air-gap length, as the optimization variables to optimize the flux density, no-load back EMF, and output torque of the AFPMBLDC, with unequal thickness poles as the target. The Maxwell 3D finite element method is used to compare the 3D transient electromagnetic field simulation analysis of the generator before and after optimization, and the following conclusions are drawn:  
1. Using [PERSON]'s optimization method to find the optimal four parameters, the optimized motor air-gap flux density was reduced by 31.8%, the total harmonic distortion rate was reduced by 7.5%, and the torque ripple was reduced by 4.3%.
2. The feasibility of the proposed method for the optimization of the axial flux PMG is verified by Maxwell 3D finite element simulation.  
In summary, [PERSON]'s optimization method can be effectively applied to the parameter optimization of an AFPMBLDC with unequal thickness poles, and it can improve its performance. This scheme provides some reference for the optimization design of an AFPMBLDC.  
Conceptualization, S.W., Q.G.; writing--review and editing, H.X., T.Z. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 3 Results
### Effect of Variables on the Response
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Sum of Squares** & **DF a** & **Mean Square** & **F-Value** & **_p-Value Prob \textgreater{} F_** \\ \hline Model & 113,7000 & 9 & 126,300 & 968 & \textless{}0.0001 \\ x\({}_{1}\):t (min) & 18,229 & 1 & 18,229 & 139 & \textless{}0.0001 \\ x\({}_{2}\):T (\({}^{\circ}\)C) & 281,900 & 1 & 281,900 & 2161 & \textless{}0.0001 \\ x\({}_{3}\):m (\%) & 424.82 & 1 & 424 & 3.26 & 0.1046 \({}^{\text{b}}\) \\ x\({}_{1}\)x\({}_{2}\) & 22,716 & 1 & 22,716 & 174 & \textless{}0.0001 \\ x\({}_{1}\)x\({}_{3}\) & 17.22 & 1 & 17.22 & 0.13 & 0.7247 \({}^{\text{b}}\) \\ x\({}_{2}\)x\({}_{3}\) & 0.40 & 1 & 0.40 & 3036 & 0.9573 \({}^{\text{b}}\) \\ x\({}_{1}\)\({}^{2}\) & 56,475 & 1 & 56,475 & 432 & \textless{}0.0001 \\ x\({}_{2}\)\({}^{2}\) & 282,900 & 1 & 282,900 & 2168 & \textless{}0.0001 \\ x\({}_{3}\)\({}^{2}\) & 633,000 & 1 & 633,000 & 4852 & \textless{}0.0001 \\ Residual & 1173 & 9 & 130 & & \\ Lack of Fit & 762.26 & 5 & 152 & 1.48 & 0.3624 \({}^{\text{b}}\) \\  & \multicolumn{2}{c}{R\({}^{2}\) = 0.999; \(\text{R}_{\text{adj}}^{\text{a}}\) = 0.997; Adequate precision = 79.8} \\ \hline \end{tabular}  
* Degree of freedom. b\(p\) value more than 0.05 indicates that the model terms are not significant.  
\end{table} 
Table 3: Analysis of variance of the model regression for adhesion strength (Y). quickly. Therefore, within the appropriate range, increasing the treatment temperature can shorten the curing time. The response surface of Y as a function of curing agent dosage (m) and treatment temperature (T) is presented in Figure 4. As shown in Figure 4, the increase of treatment temperature led to the increase of adhesion strength at any curing agent dosage. When the treatment temperature reached above 200 \({}^{\circ}\)C, the adhesion strength value tended to be constant. So, the increase of curing time and treatment temperature both had a positive effect on adhesion strength. According to the experimental results, and considering energy cost and the structural characteristics of the HJT solar cell, 195-200 \({}^{\circ}\)C and 35-45 min were the suitable treatment temperature and curing time of the HJT silver paste. 40 wt% was the suitable curing agent dosage according to the experimental results of Figure 2.  
Figure 3: Response surface showing Y as a function of treatment temperature (T) and curing time (t). Figure 2: Response surface showing Y as a function of curing agent dosage (m) and curing time (t).
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
## 2 Method
### Subjects  
Six subjects participated in the experiment. They were postgraduate and final year undergraduate students of the Music Technology courses at the University of Huddersfield, aged between 21 and 27. All subjects reported to have normal hearing and were native English speakers. They all had previous experience with psychoacoustic listening tests.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 1 Introduction
Furthermore, it saves the energy to build a model for the related hardware, which is usually very hard to build accurately. Comparing with the real UAV platform, the HITL simulation system can repeat the experiments as many times as needed without worrying about UAV costs. The contributions of this paper are summarized as follows. 1. An interesting new problem called the enemy avoidance problem is defined, which can be a good adding up scenario to the collision avoidance problem. The newly defined problem is different from most of the existing collision avoidance problems, for it is to avoid the collision with the enemy UAV rather than static obstacles or moving teammates. 2. A novel framework is proposed to learn the policy for the decision making UAV. The proposed framework formulates the enemy avoidance problem as a Markov Decision Process (MDP) problem, and solves the MDP problem by a temporal-difference reinforcement learning method called Sarsa. The Cerebellar Model Arithmetic Computer (CMAC, [19]) technique is also embodied in the proposed framework for the generalization of the continuous state space. With this framework, such a decision making problem is transformed from the usual computational problem to a learning problem. Besides, it can learn the policy with an unknown environment model, and can make decisions based on continuous state space rather than discrete ones like most existing works do. 3. A hardware-in-the-loop (HITL) simulation environment for the enemy avoidance problem is constructed, which is used for the policy learning and policy testing experiments. Different from the simulation environment in most of the existing works, this HITL simulation system saves a lot of model designing trouble, and has better consistency to the real environment, such as the environment noise. When comparing with real environment platforms, the HITL simulation system has the advantage of saving experimental cost. The remainder of this paper is outlined as follows. Section 2 gives some reviews on the related literature. The enemy avoidance problems are presented in Section 3. The proposed framework for the enemy avoidance problem is elaborated in Section 4. Section 5 details the construction of the hardware-in-the-loop simulation environment. Simulation experiments and results are illustrated in Section 6. Finally, Section 7 concludes the whole work and discusses future works.
## 2 Materials and Methods
### Test Materials
#### 2.1.2 Uranium Tailings  
The tailings used in the test were sourced from the dry bank of a tailing pond in Hunan Province. The physical parameters of the tailings sand, including specific gravity, natural dry density, natural pore ratio, and particle gradation, were determined in accordance with the \"Standard for Geotechnical Testing Method\" (GB/T 50123-2019) [19]. The relative density \(G_{\text{s}}\) is 2.499, with a natural dry density of \(\rho=1.539\) g/cm\({}^{3}\) and a pore ratio of \(e=0.78\). The tailings were screened for particle size analysis, and the particle size distribution diagram of the tailings is as shown in Figure 1. The grading parameters were calculated as: \(d_{10}=0.078\) mm, median particle size \(d_{30}=0.162\) mm, \(d_{60}=0.2\) mm, coefficient of non-uniformity \(C_{u}=2.532\), coefficient of curvature \(C_{c}=1.661\), coefficient of non-uniformity \(C_{u}<5\), and coefficient of curvature \(C_{c}>1\), indicating that the tailing particles were uniform and belong to poor gradation sand.  
The particle size of the uranium tailings is relatively uniform, mainly composed of semi-transparent mineral particles with a metallic luster, less debris, and visible impurities, such as quartz, as shown in Figure 2a. The micromorphology of the uranium tailings was analyzed using scanning electron microscopy (SEM). Figure 2b,c show the scanning results for the uranium tailings. The surface of the sample was relatively smooth, with a small amount of debris, and the distribution of fine pores was evident. At high magnification, the uranium tailings exhibited microfine grains attached to the surface.
# The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures
## 2 Gaussian Processes and Extreme Function Theory
### The Extreme Function Theory and the Complete Procedure  
Consider the posterior distribution as defined in Equation (7), set accordingly to the hyperparameters optimised as described in the previous Section. By definition, a GPdistribution over functions is a multivariate normal (Gaussian) distribution, thus the probability distribution \(z\,=p(\mathbf{f}^{*}|X^{*},X,\boldsymbol{\varphi})\) will follow the form  
\[z=\frac{1}{\sqrt[3]{2\pi^{D}|K^{*}|}}\exp\biggl{[}-\frac{1}{2}(\boldsymbol{f}^ {*}-m^{*})^{\top}K^{*-1}(\boldsymbol{f}^{*}-m^{*})\biggr{]} \tag{12}\]  
where \(m^{*}=m(\mathbf{f}^{*})\), \(K^{*}=cov(\mathbf{f}^{*})\), and \(D\) is the dimensionality of \(\mathbf{x}^{*}\) (here onwards it is assumed that a single mode shape is tested at a time; even if it is theoretically possible to test jointly a set of more mode shapes at once, this will have little practical sense). Therefore, the whole test set \(\{\mathbf{x}^{*}_{i},\boldsymbol{\varphi}^{*}_{i}\}\) is reduced to a single scalar value corresponding to its likelihood. In turn, this can be used to fit an EV distribution over its left tail (i.e., for a very low probability of occurrence, as expected for damaged conditions). The lower 10% of the validation dataset was used to this aim. Specifically, the Cumulative Distribution Function (CDF) is here fitted on low values of the Gaussian log probability \(\ln(z)\). For the GEV minima distribution, the exact formulation of the CDF is  
\[L(\ln(z),\lambda,\delta,\gamma)=1-\exp\left(-\left(1+\gamma\left(\frac{ \lambda-\ln(z)}{\delta}\right)\right)^{\frac{1}{\gamma}}\right) \tag{13}\]  
where \(\lambda,\delta\), and \(\gamma\) represent, in the same order, the location, scale, and shape parameter of the GEV distribution, which needs to be inferred from the data. Importantly, the shape parameter \(\gamma\) controls the specific form of the limit minima distribution, which simplifies in a Gumbel distribution for \(\gamma\to 0\), in a Weibull distribution if \(\gamma<0\), or in a Frechet distribution if \(\gamma>0\)[30].  
The estimation of the GEV parameters has been achieved utilising a Differential Evolution (DE) algorithm, as suggested in Reference [31]; specifically, the Self-Adaptive Differential Evolution (described in Reference [32]) was applied, considering 10 runs, 100 generations, and population size equal to 30. The Normalised Mean Square Error (NMSE) was set as the target cost function, minimised against the empirical CDF calculated according to the Hazen plotting position; an example is depicted in Figure 1. Having constructed the CDF it is then possible to define a threshold value \(\ln z_{\text{lim}}\) in correspondence of an arbitrarily chosen quantile \(\alpha\). Here in this study, \(\alpha=1\%\) was imposed. This completes the description of the whole procedure.  
Figure 1: Example of SADE optimisation. (**a**) Cumulative Distribution Function (CDF) fitting; (**b**) evolution of cost with number of generations (costs reported in logarithmic scale).
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 4 Resource Sharing Beamforming Access
### Determination of the Number of Terminals
Note that if we subtract the redundant samples \(\mathbf{Y}_{m,2}\) from \(\mathbf{Y}_{m,1}\mathbf{C}_{i}\), we get:  
\[\mathbf{Z}_{m,i}=\mathbf{Y}_{m,1}\mathbf{C}_{i}-\mathbf{Y}_{m,2}=\sum_{k\in \mathcal{N}_{i}^{c}}\mathbf{h}_{k}\big{(}\mathbf{x}_{k}\mathbf{C}_{i}-\mathbf{ x}_{k}\mathbf{C}_{p_{k}}\big{)}+\mathbf{W}_{m,1}\mathbf{C}_{i}-\mathbf{W}_{m,2}, \tag{31}\]  
where \(\mathcal{N}_{i}^{c}\) is the complementary set of \(\mathcal{N}_{i}\). This means that \(\mathcal{N}_{i}^{c}\) gathers the index of those users that do not use the pattern \(\mathbf{C}_{i}\). The noise matrices are defined as:  
\[\mathbf{W}_{m,1}=[\mathbf{w}[m(N+L_{R})],\cdots,\mathbf{w}[m(N+L_{R})+N-1]] \in\mathbb{C}^{N_{\text{R}}\times N} \tag{32}\]  
\[\mathbf{W}_{m,2}=[\mathbf{w}[m(N+L_{R})+N],\cdots,\mathbf{w}[(m+1)(N+L_{R})-1 ]]\in\mathbb{C}^{N_{\text{R}}\times L_{R}}. \tag{33}\]  
The remarkable property of (31) is that the signal subspace of \(\mathbf{Z}_{m,i}\) has dimension \(N_{\text{U}_{i}}^{c}=N_{\text{U}}-N_{\text{U}_{i}}\). In addition, the singular values of the noise subspace have the same magnitude. As a result, analogously to the procedure that is followed to estimate \(N_{\text{U}}\), we can leverage on the sample-covariance matrix of \(\mathbf{Z}_{m,i}\) to determine \(N_{\text{U}_{i}}^{c}\). Bearing in mind that the column vectors of \(\mathbf{Z}_{m,i}\) are statistically independent and that they obey the Gaussian distribution, we can obtain the second estimate as:  
\[\hat{N}_{\text{U}_{i}}^{c}=\underset{0\leq l\leq N_{\text{R}}-1}{\text{argmin }}\text{AIC}_{\lambda}(l), \tag{34}\]  
\[\text{AIC}_{\lambda}(l)=-2(N_{\text{S}}L_{R})\log\left[\frac{\prod_{i=l+1}^{N _{\text{R}}}\lambda_{i}}{\left[\frac{1}{N_{\text{R}}-l}\sum_{i=l+1}^{N_{ \text{R}}}\lambda_{i}\right]^{N_{\text{R}}-l}}\right]+2l(2N_{\text{R}}-l) \tag{35}\]  
where \(\big{\{}\lambda_{1},\cdots,\lambda_{N_{\text{R}}}\big{\}}\) represent the singular values of:  
\[\hat{\mathbf{R}}_{z}=\frac{1}{N_{\text{S}}}\sum_{m=0}^{N_{\text{S}}-1}\mathbf{ Z}_{m,i}\mathbf{Z}_{m,i}^{\text{H}}, \tag{36}\]  
for \(1\leq i\leq P\). It is evidenced from (29) and (34) that the number of users that choose the \(i\)-th repetition pattern is computed as \(\hat{N}_{\text{U}_{i}}=\hat{N}_{\text{U}}-\hat{N}_{\text{U}_{i}}^{c}\).
## 2 Proposed Method
### Proposed Method Process
#### 2.4.4 Decryption  
In the traditional process, the decryption is simple as the ciphered records contain the access policy. However, the access policy is separated from the ciphered record. Hence, the  
Figure 5: Bloom filter access.  
attribute extraction is performed first, and then the decryption process is performed. The Algorithm 3 for this process are given below.  
```
Input: S--user-selected attributes. BFO--bloom filter output. PK--public key Output: Mapped attributes \(\rho^{\prime}\) Begin For each selected attribute at in S \(RS=\{0\}^{\lambda}\) For i = 1: k  J = Hi + 1 (at) RS = RS \(\oplus\) ABE [J] Ae = RS[LSB]  remove lead zeros in Ae if Ae = at  rm =RS[MSB]  remove lead zeros in rm End \(\rho^{\prime}\)= [urm, Ae] end  end
```  
**Algorithm 3**Attribute Extraction Using BF Reconstruction  
Here, the attribute mapping is performed using the doctor-selected attributes and the bloom filter output to identify the row number (rn) in the total attribute set (ae). With these mapped attributes, the ciphered text is decrypted using the following Algorithm 4.  
```
Input: Secret key SK, C-EHR, MG, \(\rho^{\prime}\), K- hash functions Output: EHR  Begin If \(\rho^{\prime}\) is satisfied \(s=\sum\limits_{i\in l}C_{i}*\lambda_{i}\) \(e^{\prime}(g,g)^{\textit{as}}=e^{\prime}(C^{\prime},K)/\prod\limits_{i\in l }e^{\prime}(C_{i},L)\ e^{\prime}\Big{(}C^{\prime},K_{\rho^{\prime}(i)}\Big{)}\) \(EHR=C-EHR/e^{\prime}(g,g)^{\textit{as}}\) Else EHR = null value End End
```  
**Algorithm 4**Decryption  
Using these two algorithms, the ciphered electronic health records are decrypted. The decryption cannot be easily performed; this helps to improve the data confidentiality, compared to the traditional ABE.