esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 5 Conclusions  
This paper investigated usage PPG signals for reliable emotion recognition. Arousal and valence theory, a typical method of modeling emotions, is adopted to classify emotions. For short term emotion recognition, the PPG signal was segmented as a single pulse which had a tendency depending on emotions and the personal normalization method is used to resolve the differences of PPG signal between the people. We applied a 1D CNN to extract features and classify emotions using normalized single pulse PPG signal. The final fully connected CNN layer incorporated the softmax function to assign high or low arousal and valence.  
We used the DEAP dataset to verify that the proposed 1D CNN model achieved remarkable performance for classification accuracies and recognition interval. Thus, short term emotion recognition can be accurately achieved using normalized single pulse PPG signals. Our future work will attempt to use a combined model that shares the weights till convolution layers and applies different classification layers according to the emotional parameters. This work can generalize the model and save the training time.  
**Author Contributions:** Conceptualization, M.T.L. and T.K.K.; Methodology, M.S.L. and D.S.P.; Software, M.S.L. and Y.K.L.; Validation, M.T.L. and D.W.K.; Formal analysis, M.S.L. and T.K.K.; Writing-original draft preparation, M.S.L.; Writing-review and editing, T.K.K. and M.T.L.  
**Funding:** This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2016R1D1A1B0106071 and NRF-2016R1D1A1B03936281).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Bio-Signal**} & \multicolumn{2}{c}{**Classification Accuracy**} & \multirow{2}{*}{**Recognition Term**} \\ \cline{3-3} \cline{5-5}  & & **Valence** & & **Arousal** \\ \hline CNN ([PERSON] et al., 2013) [34] & BVP, SC & 63.3 & 69.1 & 30 s \\ \hline SVM ([PERSON] et al., 2014) [35] & EEG & 70.9 & 67.1 & 60 s \\ \hline Hidden Markov models ([PERSON] et al., 2014) [36] & RSP, GSR, EEG, TEMP & 58.8 & 75.0 & 60 s \\ \hline Deep belief networks ([PERSON] et al., 2016) [37] & EEG & 66.9 & 69.8 & 30 s \\ \hline Multimodal deep learning ([PERSON] et al., 2016) [38] & EOG, EEG & 85.2 & 80.5 & 63 s \\ \hline Deep sparse auto-encoders ([PERSON] et al., 2017) [32] & RSP & 73.06 & 80.78 & 20 s \\ \hline Multivariate empirical & & & & \\ mode decomposition ([PERSON] et al., 2018) [19] & EEG & 72.87 & 75.00 & 60 s \\ \hline Multiband feature matrix and & & & & \\ CapsNet ([PERSON] et al., 2019) [39] & EEG & 66.73 & 68.28 & 3 s \\ \hline Proposed 1D CNN & PPG & 75.3 & 76.2 & 1.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy and recognition term for emotion recognition based on physiological signals.
## 2 Theoretical Analysis of the near Field of an Ultra-Large-Scale Metasurface
### Calculation of a Noiseless Near-Field Metasurface
It can also be observed from the OAM spectra shown in Figure 3b-d that the calculated OAM spectral pattern exhibits a gradual improvement in purity with increasing metasurface size. The models in Figure 3b,c achieve close to perfect purity. Compared to Figure 2, the near-field amplitude radius on the observation surface also increases as the observation distance expands. Figure 2: Near-field simulation of metasurfaces with different sizes at an observation distance of 0.9 m. (**a**) \(R=20\times p\), \(R=0.2\) m, sampling interval = 0.007 m, and metasurface size of 6.66 \(\times\) 6.660\({}^{\mathrm{h}}\)2. (**b**) \(R=40\times p\), \(R=0.4\) m, sampling interval = 0.005 m, and super surface size of 13.33 \(\times\) 13.330\({}^{\mathrm{h}}\)2. (**c**) \(R=80\times p\), \(R=0.8\) m, sampling interval = 0.005 m, and metasurface size of 26.66 \(\times\) 26.660\({}^{\mathrm{h}}\)2. (**d**) \(R=120\times p\), \(R=1.2\) m, sampling interval = 0.005 m, and metasurface size of 40 \(\times\) 400\({}^{\mathrm{h}}\).
## 3 Simulation Signal Analysis
Figure 6: The performance of fast-independent component analysis (ICA): (**a**) recovered shock signal in time-domain; and (**b**) recovered harmonic signal in time-domain. In Table 1, it can be seen that the correlation coefficient of IMF4 and IMF7 are greater than that others. Since they have large relativity with the original signal, these IMFs are chosen as the representation of source signal, and the others IMFs belongs to unconcerned noise signal. Then, the Fast-ICA method is applied to them and the results are plotted (Figure 8). Figure 8a presents the recovered shock signal in the time-domain and Figure 8b presents the recovered harmonic signal. From the graph, it can be seen that, same as Fast-ICA, the EMD-ICA decomposition also has poor performance in extracting the shock signal and the harmonic signal. Thus, an advanced method should be developed. Furthermore, the conventional TSSA based on CP-ALS is applied to the simulation signal. The corresponding result is plotted in the Figure 9. It is demonstrated that TSSA based on CP-ALS has better reconstruction performance than Fast-ICA and EMD-ICA. However, the reconstruction accuracy should still be improved. The results of proposed TSSA method based on CP-WOPT in this paper are shown in Figure 10. Figure 10a presents the recovered shock signal in the time-domain and Figure 10b presents recovered harmonic signal. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **IMF9** & **IMF10** \\ \hline
0.052 & 0.043 & 0.003 & 0.352 & 0.203 & 0.311 & 0.806 & 0.086 & 0.056 & 0.069 \\ \hline \end{tabular}
\end{table} 
Table 1: The correlation coefficient between each IMF and the simulation signal. Figure 8: The result provided by empirical mode decomposition and independent component analysis (EMD-ICA): (**a**) shock signal in time-domain; and (**b**) harmonic signal in time-domain. Figure 7: The intrinsic mode functions (IMF\({}_{\text{S}}\)) of simulation signal. In Figure 10, it can be seen that the proposed method successfully extracts the two source signals from the composite single-channel signal. To evaluate the capacity of the proposed method more accurately, the index of similarity is chosen as the evaluation index. If the calculated value approaches 1, it indicates the extracted signal is very similar to the original signal. Otherwise, the extracted signal is not needed. After calculating the similarity between the recovered signal in Figure 10 and original signal in Figure 4, the value is close to 1, which demonstrates the advantage of proposed method for blind source separation.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 ET Trend Analysis in the YRB  
In this study, the interannual and intra-annual trends of ET in the YRB were analyzed by combining the Theil\(-\)Sen median trend analysis and the Mann\(-\)Kendall test. The combination of the two methods has become an important method to judge the trend and significance of long-time series data [38; 39; 40]. Theil\(-\)Sen median trend analysis [41] is used to quantify the trend of change, indicating the amount of change per unit time. The calculation formula is:  
\[\beta=\text{median}\left(\frac{\text{X}_{\text{i}}-\text{X}_{\text{j}}}{ \text{i}-\text{j}}\right), \tag{1}\]  
where \(1<\text{j}<\text{i}<\text{n}\), and \(\text{n}\) is the number of data. \(\beta\) is the trend of change: when \(\beta>0\), it indicates an upward trend. When \(\beta<0\), it indicates a downward trend. When \(\beta=0\), the trend is unchanged.  
The \(\beta\) obtained by the Sen trend cannot be used to judge the trend significance of time series. Therefore, the Mann\(-\)Kendall method is introduced to analyze the significance of the trend [42]. This method has no requirement for data distribution of sequence and is insensitive to outliers [43]. For a time series of ET, the statistic \(Z\) is defined as:  
\[Z=\begin{cases}\frac{\text{S}-1}{\sqrt{\text{V}(\text{S})}},&\text{S}>0\\ \begin{array}{ll}0\ \,&\text{S}=0\\ \frac{\text{S}+1}{\sqrt{\text{V}(\text{S})}},&\text{S}<0.\end{array}\end{cases} \tag{2}\]  
where \(\text{S}=\sum_{\text{k}=1}^{\text{n}-1}\sum_{\text{j}=\text{k}+1}^{\text{n}} \text{Sgn}\left(\text{X}_{\text{j}}-\text{X}_{\text{k}}\right)\), \(\text{Var}(\text{S})=\text{n}\times(\text{n}-1)\times(\text{2n}+5)/18\),  
\[\text{Sgn}(\text{X}_{\text{j}}-\text{X}_{\text{k}})=\begin{cases}+1,&\left( \text{X}_{\text{j}}-\text{X}_{\text{k}}\right)>0\\ 0,&\left(\text{X}_{\text{j}}-\text{X}_{\text{k}}\right)=0\\ -1,&\left(\text{X}_{\text{j}}-\text{X}_{\text{k}}\right)<0\,\end{cases} \tag{3}\]  
In the bilateral trend test, there is significant variation in the time series data when the Mann\(-\)Kendall statistic \(|Z|\geq Z_{1-\alpha/2}\). Confidence intervals in this study were calculated at two levels of significance (\(\alpha=0.01\) and \(\alpha=0.05\)), which the trend of change to be classified into five levels (Table 1).
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 3 MIMO-VLC Using ADTs  
In this section, we present the principle of MIMO-VLC systems using ADTs. First, the structure of ADT is introduced, then the LOS channel gain using ADT is derived, and finally the average achievable rate of MIMO-VLC systems using ADTs is obtained.
## 3 Numerical Experiments and Results
### Preliminary Experiment and Results  
The method proposed in this paper is an approach based on GA. GA is a probability-based algorithm in which probability parameter values used in the operator influence the algorithm performance. Thus, population size and probability of crossover (\(P_{C}\)) and probability of mutation (\(P_{m}\)), which were parameter factors of the operator used in the algorithm, were determined in the preliminary experiment [29].  
The preliminary experiment was performed with the deterministic optimization model, and parameter combinations that exhibited the best performance were determined after configuring 27 (\(3\times 3\times 3\)) parameter combinations in Table 1. From target number \(n=15\), three examples were randomly generated and iterative experiments for each were conducted 30 times. If the solution was not updated for more than 1000 times, the experiment was set to terminate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Levels** & **Values** \\ \hline Population Size & 3 & \(n\), \(2n\), \(4n\) \\ Probability of Crossover (\(P_{C}\)) & 3 & 0.1, 0.5, 0.9 \\ Probability of Mutation (\(P_{m}\)) & 3 & 0.01, 0.05, 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental factors and their levels.  
The parameter combinations that showed the best result in the preliminary experiment were _population size_\(=2n\), \(P_{c}=0.9\), and \(P_{m}=0.1\). The iterative experiments using the parameter combinations were conducted, and the results showed that all combinations reached the same solution within several seconds as summarized in Table 2.
## 9 Conclusions  
This paper gives the syntax and security description of CLR-SS-AIBBE and proposes a concrete CLR-SS-AIBBE scheme. The private key is continuously updated through state division. The proposed scheme can resist the continual leakage about the private key. The relative leakage rate reaches one-third. Based on the general subgroup decision hypothesis, it is proven that our scheme is secure under the standard model. In addition, through the special treatment of a private key, this given scheme also has the characteristics of anonymity. It has three advantages.  
First, our scheme has better application value. Since the adversary in the real environment can carry out continuous-leakage attacks, the continuous-leakage model is closer to the application needs of the real environment. In this paper, the leakage-resilient performance of IBBE mechanism is achieved under the continuous-leakage model, so the scheme is more practical.  
Second, our scheme has better user-identity privacy protection. In the identity-based broadcast encryption scheme, broadcasters usually encrypt messages by combining the public identity of the receiver and system parameters. This may reveal the identity of the receiver to the public, which causes users to worry about identity privacy. Most identity-based broadcast encryption (IBBE) schemes are not anonymous, which means that attackers can obtain the identities of all recipients from the ciphertext. The paper provides anonymity and has a good role in protecting user identity privacy.  
Third, the given scheme is suitable for some intelligent systems. The public parameter size and private key size of the proposed scheme are constant, and the decryption cost is independent of the number of recipients. Therefore, this scheme requires less computing energy consumption and is very suitable for intelligent city information systems.  
Conceptualization and methodology, Q.Y. and J.L.; formal analysis, Q.Y. and S.J.; writing--original draft preparation, Q.Y.; writing--review and editing, Q.Y. and S.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Ref. 62172292, 62072104, 61972095, U21A20465).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Low-Profile Dual-Polarized Transmitarray with Enhanced Gain and Beam Steering at Ku Band
## 7 Conclusions  
A low profile, dual-polarized 3-bit TA is proposed, simulated, and tested in this research. This proposed TA achieves a low profile (0.126\(\lambda_{0}\)), dual-polarized operation, and efficient beam steering up to \(\pm\)30\({}^{\circ}\) with minimal scan loss, with a 1 dB gain bandwidth of BW\({}_{\text{g}}\) = 10.8% (11.1-12.4 GHz), and a measured peak side lobe level SLL\({}_{1}\) = \(-\)20.8 dB at f\({}_{0}\) = 12 GHz. In particular, a measured scan loss of \(\bigtriangleup\)G\({}_{\text{MSL0}}\) = 4.03 dB and \(\bigtriangleup\)G\({}_{\text{MSL1}}\) = 2.73 dB is achieved at f\({}_{0}\) = 12 GHz and f\({}_{1}\) = 12.4 GHz, respectively. The measured peak gain is G\({}_{\text{m0}}\) = 21 dB at f\({}_{0}\) = 12 GHz with an aperture efficiency of \(\eta_{0}\) = 17% at whereas the maximum aperture efficiency is \(\eta_{\text{max}}\) = 19% found at f\({}_{2}\) = 11.2 GHz. All these features make this TA suitable for Ku-band satellite and 5G applications.  
Conceptualization, investigation, and methodology, M.I.K., A.M.L., F.A., V.V.F., A.U.K., M.S., G.S. and F.P.; writing--original draft preparation, M.I.K.; writing--review and editing, M.I.K., A.M.L., F.A., G.S. and F.P.; supervision, F.P.; funding acquisition, F.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by MIUR PRIN 2022, NRPP-DD n. 1181 del 27-07-2023--Innovative technologies for non-invasive assessment of plant health condition to support precision farming \"VEGFATION\" (P2022 ZF9P2, CUP: 153D2300571001); H2020-ICT-37-2020 \"Photonic Accurate and Portable Sensor Systems Exploiting Photo-Acoustic and PhotoThermal Based Spectroscopy for Real-Time Outdoor Air Pollution Monitoring--PASSEPAROTU\" n. 101016956; the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, with reference to the partnership on \"Telecommunications of the Future\" (PE00000001--program \"RESTART\", CUP: P93C22000910001)--STRUCTURERAL PROJECT Antennas & Devices for mixing, detection And Manipulation of mmWaves; ASI 687/2022--MUR 341 15/03/22 \"SPACE IT UP\" (CUP: D53C24000570006); ASI 2024-68-I.0 \"Origami Reconfigurable Beam-steering Antennas with METAsurfaces for Communication on Small Satellites--ORBIT-META\" (CUP: F93C24000550001); and HORIZON TMA MSCA Staff Exchanges (101182995) \"Cr4+:YAG/Polymer nanocomposite as alternative materials for Q-switched lasers: properties, modeling, and applications--ALTER-Q\".  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
TA Transmitarray UC Unit cell ECM Equivalent circuit model
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 5 Discussion  
### Strategy for Selecting Non-Landslide Points  
The quality of the sample dataset is crucial for constructing landslide susceptibility assessment models. Because landslide samples are typically derived from historical landslide observation data or satellite images, and non-landslide samples are not as readily available as usual [22], the selection of non-landslide samples considerably impacts the final landslide susceptibility assessment results.  
Currently, most studies determined non-landslide samples via random sampling [33]. However, the reliability of non-landslide samples selected by this method is unknown and might misclassify potential landslides as non-landslides, hindering the model's ability to learn effective features [25]. Therefore, considering the spatial distribution relationship--where the greater similarity in geographical environments correlates with greater similarity in geographic features--the IV model is employed to quantify geographical and environmental factors into weighted information quantities [34]. A lower total IV indicates a lower degree of similarity between an area's geographic environmental factors and those of the landslide-prone area [22]. Combining the natural break classification method, areas with low IVs are classified as candidate regions for non-landslide samples. However, the single-natural-break classification method cannot ensure that all areas within the candidate region fulfil the criteria for non-landslide samples, and the choice of threshold exerts a significant effect [35]. To ensure that the selected non-landslide samples were as close as possible to the true classification, two spatial topology rules--distance from historical landslides greater than 500 metres and slope less than 10 degrees--were combined to finalise the candidate region for non-landslide samples. The final non-landslide sample points were randomly selected from the candidate region, with the additional rule that the distance between non-landslide points is no less than 2 km, constraining their spatial distribution.  
This study advances a non-landslide sampling strategy that considers the spatial correlation of geographical and environmental factors with landslide susceptibility. By con  
Figure 9: Spatial distribution of landslide susceptibility in the study area.  
structing a selection method based on environmental and spatial topology constraints, the strategy effectively lowers bias in non-landslide sample selections, enhances the model's classification accuracy and ensures a reasonable distribution of samples, preventing excessive concentration of non-landslide samples. This technique can effectively address the problem of low reliability in landslide susceptibility assessment results generated by conventional random sampling methods.
## 2 Methods
### Loss Functions  
Since the idea of Node2 Node is similar to that of Noise2 Noise, we also use the MSE loss between \(f_{\theta}(I_{i})\) and \(I_{j}\) to train the network, where \(f_{\theta}\) represents the nonlinear function expressed by the network with \(\theta\) being the network parameters. The loss can be formulated as:  
\[Loss=\operatorname*{arg\,min}_{\theta}(f_{\theta}(I_{i})-I_{j})^{2} \tag{10}\]  
Considering that MSE loss is prone to over-smoothing the denoised image, to address this issue, we propose an edge-aware loss implemented with erosion and dilation operation through max-pooling and average pooling, detailed as:  
\[W_{edge}=\frac{|AvgPool(f_{\theta}(I_{i}))+MaxPool(-f_{\theta}(I_{i}))|}{ \operatorname{Max}(|AvgPool(f_{\theta}(I_{i}))+MaxPool(-f_{\theta}(I_{i}))|)} \tag{11}\]  
where \(|\cdot|\) indicates the absolute and Max the maximum value. \(AvgPool(x)\) can dilate the image \(x\), while \(-MaxPool(-x)\) can erode the image \(x\) and using \(AvgPool(x)-(-MaxPool(x))\) can emphasize the image edges. Therefore, the normalized edge weight is designed like Equation (11). To retain the edge details during the denoising, we propose an edge-aware loss that be written as:  
\[Loss_{w}=(1+W_{edge})Loss. \tag{12}\]  
This allows the model to adjust the loss weights on different regions.
## 5 Experimental Results
### Tests in a Mobile Scenario  
In the third phase of our field tests, we consider a more realistic scenario, denoted as Scenario \(3\), in the following, where the smartphones are positioned within vehicles moving along a predetermined path. The experimental setup is described in Subsection 5.4.1, while the obtained results are discussed in Subsection 5.4.2.
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 5 Discussion  
1. In the specific case study, the outcomes achieved through the classical SINDY algorithm do not align with physics-based identification. Moreover, they do not qualify as sparse identification, as all candidate functions are present in the matrix \(\Xi\).
2. Based on the knowledge of the structure of the complex system to be identified, a more precise algorithm has been introduced without a significant increase in computational cost.
3. Although the identified model appears to have significant differences from the related model found in the literature, it enables the design of a controller capable of stabilizing the original high-order nonlinear system with coupled dynamics. This, in turn, demonstrates its notable approximation degree.
4. The primary drawback of the proposed approach is that the identification problem transforms into a coefficient identification task. Nevertheless, in many real-world applications, this outcome may still be appealing because, in numerous cases, it is impossible to precisely determine the coefficients of a specific system, even when the structure of its mathematical model is available.