esa-sceva/satcom-chunk-collection huggingface dataset:
# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 4 Conclusions  
In this work, a machine learning driven an ANN model was developed to rationally design the desired dielectric constant of polymer composites, using hundreds of experimentally measured data. The hyper parameters were optimized by a hyperband method to improve the prediction performance. The designed ANN model can represent the correlations between 8 input variables and a dielectric constant of polymer composite with an accuracy of 97%. The main conclusions obtained from this ANN model are listed as the follows. Firstly, when considering the polymer matrix and the ceramic filler, it was found that the dielectric constant of the matrix was more dominant in increasing the dielectric constant of the composite than that of the filler. Secondly, regarding the dimension of the filler, it was shown to increase the dielectric constant of the composite in the order of 1D and 0D. As for 2D, additional studies are required because other 2D fillers except BN were not considered in this study. Lastly, the ANN model shows a good prediction performance for a dielectric constant at various frequencies (spanning from 100 Hz to 100 kHz). Therefore, we demonstrated the AI-based ANN model can design the dielectric constant of polymer composites for a high energy density in electrical energy storage applications.  
Figure 9: Comparison of experimental and predicted dielectric constant as a function of filler volume fraction depends on increasing frequency: (**a**) 100 Hz, (**b**) 1 kHz, (**c**) 10 kHz, and (**d**) 100 kHz.  
**Author Contributions:** Conceptualization, S.J. and S.Y.; methodology, S.J., D.-Y.J. and C.K.; investigation, S.J.; resources, D.-Y.J. and S.Y.; data curation, S.J.; writing--original draft preparation, S.J.; writing--review and editing, S.Y.; supervision, C.K. and S.Y.; funding acquisition, D.-Y.J. and S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported in part by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0017303) supervised by the Korea Institute for Advancement of Technology (KIAT); and in part by the Korea Institute of Industrial Technology, under the project \"Development of root technology of multi-product flexible production (KITECH-EO-22-0006)\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Free-Space and On-Body Link Budget
### On- and Off-Body Communications  
In the on- and off-body communication cases, measurements at the lower and upper bands were performed. For this part, the antennas were placed on various locations of the body (chest and arm) to measure the S\({}_{21}\) values, as shown in Figure 14. To investigate the effect of the distance (\(r\)) on the S\({}_{21}\) parameter, the antenna separation distances were set at 2 m, 5 m, and 8 m between the Tx and Rx antennas with a gap of 10 mm created between the antenna and the skin layer to mimic the real scenario, the same as mentioned in the S\({}_{11}\) measurement.  
Figure 14: Measured S\({}_{21}\) of the MMAs for on-chest and on-arm in line-of-sight (LoS) and non-line-of-sight (NLoS) at various distances between the Tx and Rx antennas (\(r\): 2 to 8 m).  
Figure 13: Measured S\({}_{21}\) of the MMAs in free-space at various distances between the Tx and Rx antennas (\(r\): 2 to 11 m).  
In the first set of measurements, two identical antennas were placed on the chest of two male subjects, with an average weight of 72 kg and height of 1.58 m. The arms were stretched along the body of the subject. The path losses in the presence of the body are larger due to the power absorption of lossy human body tissues, and the S\({}_{21}\) levels were reduced in comparison to the free-space measurements. Similar to the free-space measurements, the distance between the Tx and Rx antennas was increased from 2 to 8 m for on- and off-body communications. Using Equation (1) and \(r_{0}=2\) m, and \(r=8\) m, we estimated the value of \(n=3\), which was slightly larger than the free-space case. This was expected due to the obstruction by the bodies and the losses and power absorbed by the lossy tissues. We also examined one NLoS case, where the antennas were placed on the chest and the arm of two male subjects with a separation of \(r=2\) m. We observed more path losses of 90 dB and 95 dB in the lower and upper bands, respectively. In the next scenario, the antennas were attached to the arms of the two male subjects, and LoS path losses were measured. Path losses of about 80 dB and 86 dB were obtained in the two bands, respectively.  
Overall, the path losses in the three scenarios of free-space, on-chest, and on-arm at lower bands were lower than those for the off-body at the upper band. To comply with the SAR regulations [35], the antenna input power was kept at 20 dBm. For example, when the receiver showed a received power of \(-75\) dBm [36; 37], the maximum loss of 99.4 dB (S\({}_{21}=-99.4\) dB) was considered to be acceptable. The measurement results showed that the lower band has better performance and can be used for LoS, and the upper band might be better suited for the NLoS. Thus, our proposed antenna provides suitable path losses in an open-space in both bands and can be used for the link budget calculations. An average path loss exponent of \(n~{}=~{}3\) was observed.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### The Policy Enforcement Point  
During an authorization check, the _PEP_ is the entity that uses the access token (which the _Device_ previously obtained from the _IdP_) to retrieve from the _IdP_ the roles assigned to the _Device_. Therefore, a mapping between applications and OAuth 2.0 consumers can be made. To perform that request, the _PEP_ will also use its own credentials, which the _Service Admin_ obtained from the _IdP_ when registering the _Service_, and later set in the _PEP_. Once roles have been retrieved, the authorization check is sent to the _PDP_.  
It is important to highlight that, in order for the PEP to intercept the requests to the _Service_ and enforce authorization policies, a different _PEP_ has to be set for each _Service_. This way, a completely transparent, as a service authorization system is provided, because the only thing that the _Service Admin_ must take care of (apart from configuring credentials and policies) is deploying an instance of the _PEP_ along with the _Service_. Besides, the AC provider will be most probably providing and supporting the implementation of the _PEP_ component, in order to foster this procedure.
## 6 Results and Discussions  
Several experimental tests are performed on SC-IM with healthy and faulty working conditions under different loading conditions. The _abc_ stator currents are converted to _qd_ signals (e.g., _Iq_ and _Id_) using Clark's Transformation. The number of samples obtained is 125,000 samples for every signal. The _qd_ signals are pre-processed using DWT. The mother wavelet \"haar\" is selected, and the maximum decomposition level was obtained to perform DWT analysis. The data set generated from DWT is used to train and test the ANFIS model. The testing procedure is utilized to assess the performance of the ANFIS model.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 2 Methods and Algorithms  
### Methods  
#### 2.1.1 BPNN  
A BPNN is a neural network trained according to the error back-propagation algorithm, consisting of an input layer, several hidden layers and an output layer ([PERSON] and [PERSON], 2015). Each layer can have several neurons, and the connection state of the neurons between the layers is reflected by the weights. A BPNN is a multilayer feedforward neural network with a strong learning ability that can be used to process the complex mapping relationships of nonlinear functions and is often used in classification or prediction problems. [PERSON] et al. (2018) employed a BPNN model to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. [PERSON] et al. (2018) developed a BPNN model to correct heading angle velocity output and vehicle speed and to add the synthesized relative displacement to the previous absolute position to realize a new vehicle position. Moreover, based on BPNN model, [PERSON] et al. (2018) proposed a 60 GHz impulse radio positioning algorithm, which has obtained a good positioning result.  
Differing from the above algorithms, in this study we apply a BPNN model to indoor Wi-Fi RSSI calibration that corrects the Wi-Fi RSSI values received by different mobile terminals based on the standard mobile terminal. Firstly, we utilise a WOA to help the BPNN model find the optimal initial weights and thresholds. Secondly, to speed up the establishment and optimisation of the calibration algorithm and to increase the updating and iterating speed, we propose a nonlinear convergence factor, \(a\). The model principle is shown in Figure 2, and the algorithm steps are explained in Section 2.2.1.
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 7 Conclusions  
A community detection algorithm can fail in many different ways; for example, it may only be able to detect a subset of the desired community structure, or it may detect all of the desired communities but also several spurious communities. To overcome such problems, in this paper, we proposed a community detection method to reveal the weak or the hidden communities in the multi-layer network. The process goes on by weakening the structure of the dominant layers of communities to get the hidden structure inside, which may present some useful information when considering it in a real-world scenario. Through experiments on real-world networks from different domains, we observed that networks have hidden community structure (weak communities) in them. By applying the HCD method, it has become possible to get the hidden structure of communities. HCD has an advantage over modularity-based algorithms in that it simplifies the community structure and gives quality results with a clear picture of all the communities (including weak communities). We further compared the results obtained by the HCD method with two algorithms, i.e., Louvain and Infomap, along with an attribute based comparison with other five multi-layer community detection algorithms; we observed that the HCD method performs better and satisfies the attributes described in the above section.  
Moreover, the study of detecting community structures in multi-layer networks is experiencing significance since the last decade. One of the applications of the HCD method could be the detection of suspect or fraud groups on social media; this can lead to confusion because not all hidden communities on social media have the aim of crime. So, a number of extensions or improvements to our method may be possible. This issue can be addressed in future research regarding the same problem to more specifically detect weak structures of communities by using different baseline methods other than the Louvain algorithm.  
The authors participated and designed the experiment together. [PERSON] under the supervision of [PERSON] and [PERSON]. [PERSON] and [PERSON] analyzed and evaluated the results. [PERSON] wrote the manuscript. All authors reviewed the manuscript. [PERSON] and [PERSON] are co-first authors. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key Research and Development Program of China. under grant No. 2018 YFB1003602.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Active Control Design
### Linear Poles Placement  
Pole placement can then be done using the state space form of Equation (11) with \(\mathbf{Y}=\begin{bmatrix}\mathbf{X}&\dot{\mathbf{X}}\end{bmatrix}^{\mathbf{T}}\)  
\[\begin{cases}\dot{\mathbf{Y}}=\mathbf{A}\,\mathbf{Y}+\mathbf{B}\,\bar{\mathbf{ u}}\\ \bar{\mathbf{u}}=-\mathbf{G}\,\mathbf{Y}\end{cases} \tag{12}\]  
where the matrix of the system dynamics \(\mathbf{A}\) and the matrix of the distribution of the control on the different degrees-of-freedom \(\mathbf{B}\) are given by  
\[\mathbf{A}=\begin{bmatrix}\mathbf{0}_{n,n}&\mathbf{Id}_{n,n}\\ -\mathbf{M}^{-1}\left(\mathbf{K}+\mathbf{K}_{\mu}\right)&-\mathbf{M}^{-1} \mathbf{C}\end{bmatrix} \tag{13}\]  
and  
\[\mathbf{B}=\begin{bmatrix}\mathbf{0}_{n,n}\\ \mathbf{M}^{-1}\end{bmatrix} \tag{14}\]  
where \(\mathbf{Id}_{n,n}\) denotes the \(n\times n\) identity matrix. The \(2n\) conjugate poles \(\lambda\) of the matrix \(\mathbf{A}\) can be used to compute the control gain matrix \(\mathbf{G}\) using a classical pole placement algorithm [22] so that  
\[\lambda_{c}=\gamma\,\Re\left(\lambda\right)+j\,\Im\left(\lambda\right) \tag{15}\]  
where \(j=\sqrt{-1}\). \(\Re()\) and \(\Im()\) are the real and the imaginary part respectively. \(\lambda_{c}\) are the poles of the controlled system and \(\gamma\) the control gain applied on the real part of the uncontrolled poles.
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 4 Results and Discussions
### Stereo Matching
#### 4.1.1 Middlebury Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Initial Disparity**} & \multicolumn{2}{c}{**Final Disparity**} \\ \cline{2-5}  & **Out\_\_n** & **Out\_a** & **Out\_n** & **Out\_a** \\ \hline BF [29] & 6.51 & 17.42 & 5.91 & 11.98 \\ GF [13] & 5.16 & **16.68** & 5.22 & 11.51 \\ FCGF [18] & 8.67 & 18.99 & 6.83 & 12.86 \\ NL [27] & 6.66 & 17.20 & 6.64 & 12.67 \\ ST [17] & 6.27 & 17.34 & 6.08 & 12.30 \\ DT [19] & 9.65 & 23.27 & 5.30 & 11.62 \\ LRNL [28] & 6.21 & 18.09 & 5.98 & 12.09 \\ Ours & **5.05** & 17.14 & **5.17** & **11.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison with typical cost aggregation approaches on the Middlebury dataset [8]. Out-a and Out-n are the percentages of erroneous pixels in all and non-occluded regions, respectively (/%). The bold is the best result in each metric.
## 3 Results
### Comparison of Different Quality Factors and Training Times  
To explore whether the method designed by this study can restore the watermark embedded in a watermarked image after JPEG compression, the experiment in this section will use different quality factors to observe the image quality evaluation indicators under different training times, and discuss how to tune the model parameters based on the results. The results of the experiment are shown in Table 2, which shows the values of various evaluation indexes of the original watermark and the watermark generated by the model under different quality factors and training times.  
In the case of a quality factor of 50, when the number of training epochs is 50 and 100, the PSNR values of the restored (retrieved) watermark generated by the model are approximately 40.87 dB and 44.06 dB, respectively. When the number of training epochs rises to more than 250, the PSNR value of the restored (retrieved) watermark generated by the model increases significantly and remains at about 47.5-51.57 dB. However, a closer observation reveals that when the Epoch is 200, the PSNR of the restored (retrieved) watermark generated by the model is only about 42.75 dB, which shows that the PSNR does not continue to increase as the number of training epochs increases. SSIM and BER have similar results. When we observe the compression of different quality factors (quality factor = 10, 20, 30, \(\ldots\), 90), we will find that there are similar results regardless of the quality factor. In other words, the model has not yet converged when the Epoch is 50-100, but when the number of training epochs increases to 200 or more than 250, the generator and discriminator models have already converged, so the loss function of the model will not continue to decline. However, it will remain near a certain value; even if the Epoch is increased, the loss function value of the model will only make a small difference.  
On the whole, the method proposed in this paper is robust to any quality factor. Among them, the watermark generated by the trained model is compared with the original watermark, the average PSNR can reach 49.99 dB, the average SSIM reaches 0.95, and the average BER can be as low as 0.008. Therefore, the method proposed in this paper sets different quality factors in the training phase and optimizes the parameters for different training times. These optimal parameters will be stored as model parameters for restoring (retrieving) the watermark. That is, when a picture with a hidden watermark is compressed by JEPG, the DCGAN model in the method designed by us is capable of generating the watermark. Moreover, the generated watermark is quite similar to the original watermark.
## 4 Potential Research Directions of RSMA-Assisted ISAC Systems
### Reconfigurable Intelligent Surface  
RIS has received considerable attention in next-generation wireless communication systems, due to their ability to actively manipulate the radio environment and effectively reduce interference between multiple users [65]. In recent years, ISAC systems that exploit RIS have been extensively studied [66]. In such systems, beamforming technology can be effectively adopted to improve target detection and parameter estimation performance [67]. In [68], spare arrays with partially active elements are exploited for channel estimation in RIS-assisted ISAC systems. Recently, RSMA has been further considered in RIS-assisted ISAC systems [39], aiming to maximize the SNR for target detection. In [39], a study on RSMA-RIS-assisted ISAC systems was conducted for the first time, maximizing the SNR for target detection. The overview diagram of the RSMA-assisted RIS-ISAC systems is shown in Figure 6.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 2 Preliminaries
### Relevant Definitions of Lattice  
**Definition 1**.: _(Lattice) Let \(\{\mathbf{b}_{1},\mathbf{b}_{2},\ldots,\mathbf{b}_{m}\}\in\mathbb{R}^{n}\) be \(m\) linearly independent vectors on the \(n\)-dimensional Euclidean space \(\mathbb{R}^{n}\). Set \(\mathbf{B}=\{\mathbf{b}_{1}|\mathbf{b}_{2}|\ldots|\mathbf{b}_{m}\}\in\mathbb{R}^{n\times m}\), and lattice \(\Lambda(\mathbf{B})\) can be expressed linearly by the integer coefficients of all these vectors of \(\mathbf{b}_{1},\mathbf{b}_{2},\ldots,\mathbf{b}_{m}\), as defined follows:_  
\[\Lambda(\mathbf{B})=\left\{\mathbf{y}\in\mathbb{R}^{n}:\ \exists\mathbf{s}\in\mathbb{Z}^{m}, \mathbf{y}=\mathbf{B}\mathbf{s}=\sum_{i=1}^{m}\mathbf{s}_{i}\mathbf{b}_{i}\right\}\]  
where the linear independent vector \(\{\mathbf{b}_{1},\mathbf{b}_{2},\ldots,\mathbf{b}_{m}\}\) which is a basis of the lattice form a lattice space, with dimension \(n\) and rank \(m\), for \(m>n\). When \(n=m\), the \(\Lambda(\mathbf{B})\) is a full-ranklattice, the scheme is usually constructed with the full-rank lattice. Here, we are interested in integer lattices, i.e., when \(\mathbf{s}\) is contained in \(\mathbb{Z}^{m}\).  
**Definition 2**.: _(\(q\)-Module Lattice) For \(n,m,q\in\mathbb{Z}\), where \(q\) is prime, \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) and \(\mathbf{u}=\mathbb{Z}_{q}^{n}\), define:_  
\[\Lambda_{q}(\mathbf{A})=\Big{\{}\mathbf{y}\in\mathbb{Z}_{q}^{m}:\;\exists\mathbf{s}\in \mathbb{Z}_{q}^{n},\mathbf{A}^{\top}\cdot\mathbf{s}=\mathbf{y}(\text{mod}\;q)\Big{\}}\]  
\[\Lambda_{q}^{\perp}(\mathbf{A})=\Big{\{}\mathbf{x}\in\mathbb{Z}_{q}^{m}:\;\mathbf{A}\mathbf{x} =0(\text{mod}\;q)\Big{\}}\]  
\[\Lambda_{q}^{\mathbf{u}}(\mathbf{A})=\Big{\{}\mathbf{x}\in\mathbb{Z}_{q}^{m}:\;\mathbf{A}\mathbf{x} =\mathbf{u}(\text{mod}\;q)\Big{\}}\]  
where \(\Lambda_{q}^{\mathbf{u}}(\mathbf{A})\) is the coset of \(\Lambda_{q}^{\perp}(\mathbf{A})\). \(\Lambda_{q}^{\mathbf{u}}(\mathbf{A})\) is a shift of \(\Lambda_{q}^{\perp}(\mathbf{A})\) which satisfies \(\mathbf{t}+\Lambda_{q}^{\perp}(\mathbf{A})=\Lambda_{q}^{\mathbf{u}}(\mathbf{A})\), for \(\mathbf{t}\in\Lambda_{q}^{\mathbf{u}}(\mathbf{A})\).
## 3 Numerical Experimentation and Analysis
### Failure Process Analysis
#### 3.1.2 The Failure Process of Concrete Specim with Pore Structure  
The failure process of concrete specimens with pore structure is similar to that of concrete specimens without pores, which is also divided into three stages in the same way. This conclusion is consistent with [PERSON] findings [29]. However, the existence of pores makes the cracking process of concrete present different characteristics, which are described below.  
1. The relatively stable stage of micro-cracks (\(\sigma/\sigma_{\rm max}<\) 0.3\(\sim\)0.5)  
Figure 11a,b, respectively, show the distribution of the CIEs with damage variables D = 0.25 and D = 0.45 when \(\sigma/\sigma_{\rm max}\approx 0.36\), where black circles represent pores. It can be seen from Figure 11a that in the later stage of this stage, the damage in the cement mortar is mainly concentrated in some pore concentration areas around the specimen, because the stress concentration in the pore concentration areas is more intense. It can be seen from Figure 11b that the damage in the ITZ has expanded from the periphery to the center of the specimen at the later stage of this stage, indicating that the presence of pores accelerates the expansion of the damage.  
2. The stable development stage of cracks (0.3\(\sim\)0.5 \(<\sigma/\sigma_{\rm max}<\) 0.75\(\sim\)0.9)  
Figure 12a,b, respectively, show the distribution of the CIEs with damage variables D = 0.35 and D = 0.7 when \(\sigma/\sigma_{\rm max}\approx\) 0.72. As shown in Figure 12a, the damage in the cement mortar extends to the center of the specimen at this stage. From the comparison of Figures 12a and 7a, it can be seen that the presence of pores makes the damage in the cement mortar show stronger localization. Even with the existence of pores, the most severely damaged part of the concrete is the ITZ, as shown in Figure 12b.  
3. The unstable development stage of cracks (0.75\(\sim\)0.9 \(<\sigma/\sigma_{\rm max}\))  
Figure 13 shows the distribution of the CIEs with damage variable D = 0.8 when \(\sigma/\sigma_{\rm max}\approx\) 0.94(\(\varepsilon<\varepsilon_{\rm p}\)). It can be seen that the most severely damaged areas in the cement mortar are not the pore-concentrated areas but are distributed around the aggregates, which may be caused by the propagation of cracks in the ITZ to the cement mortar. Figure 14 shows the distribution of macro cracks in some specimens with the same aggregate structure but different pore structures. It can be seen that the pore structure has an important influence on the initiation position and propagation direction of macroscopic cracks, and the macro cracks must pass through pores and ITZ.  
Figure 11: The distribution of CIEs with D = 0.25 and D = 0.45 when \(\sigma/\sigma_{\rm max}\approx\) 0.36 in the 1-1-S specimen. (**a**) D = 0.25; (**b**) D = 0.45.  
Figure 12: The distribution of CIEs with D = 0.35 and D = 0.7 when \(\sigma/\sigma_{\rm max}\approx\) 0.7 in the 1-1-S specimen. (**a**) D = 0.35; (**b**) D = 0.7.