esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes the DPE-SI framework, integrating swarm intelligence algorithms with a low-complexity correlation strategy to efficiently solve the DPE problem. A novel ADBO algorithm based on FLA was developed to dynamically adjust its search strategy and incorporate a hybrid mutation strategy, enhancing both global and local search capabilities. Benchmark tests on CEC2020 functions demonstrate that ADBO outperforms conventional search methods and other swarm intelligence algorithms, with faster convergence and superior positioning accuracy. Simulation results show significant improvements in both convergence speed and positioning accuracy while reducing computational cost. Real-signal experiments confirm the algorithm's robustness, with ADBO improving positioning accuracy by 9.6% over 2 SP and 45.6% over MRG in static scenarios and 26.5% and 46.6% in dynamic scenarios, respectively. Computational time was reduced by 82.1% in static and 85.8% in dynamic tests. The combined DPE-SI framework and ADBO algorithm offer an efficient and accurate solution for GNSS positioning, showing significant potential for use in low-cost GNSS devices and applications with limited computational resources.  
Conceptualization, Y.D. and J.W.; Formal analysis, Z.T. and K.Y.; Investigation, Y.D. and Z.T.; Methodology, Y.D. and Z.T.; Software, Y.D. and K.Y.; Supervision, Z.T. and J.W.; Validation, Y.D., Z.T., J.W., and K.Y.; Visualization, J.S.; Writing--original draft, Y.D. and Z.T.; Writing--review and editing, Y.D., J.W., and J.S. 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 (Nos. 62171191 and 62271223).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the HVSR and VRSR Mean Curve Features  
In the study of engineering site dynamic characteristics, HVSR and VRSR are primarily used to estimate the site's predominant period and site amplification effect. According to [PERSON]'s assumption, the maximum peaks of the mean HVSR and VRSR curves are considered the maximum amplification factor (\(A_{0}\)) values of the site, and the corresponding period is the site's predominant period (\(T_{0}\)). In this study, strong-motion records from the Loess Plateau region were used to estimate the mean amplification factor and mean predominant period of the site through the HVSR and VRSR mean spectral ratio curves. Additionally, the relationships between the mean spectral ratio curves of HVSR and VRSR  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(R\) (km)} & \multicolumn{2}{c}{**EW**} & \multicolumn{2}{c}{**NS**} & \multicolumn{2}{c}{**UD**} \\ \cline{2-7}  & \(P_{max}\) **(gal\({}^{2}\)/s)** & \(T_{p}\) **(s)** & \(P_{max}\) **(gal\({}^{2}\)/s)** & \(T_{p}\) **(s)** & \(P_{max}\) **(gal\({}^{2}\)/s)** & \(T_{p}\) **(s)** \\ \hline
0–20 & 0.03 & 0.27 & 0.02 & 0.18 & 0.02 & 0.10 \\
20–50 & 0.02 & 0.18 & 0.02 & 0.17 & 0.02 & 0.12 \\
50–200 & 0.02 & 0.14 & 0.02 & 0.12 & 0.01 & 0.11 \\ \(\geq\)200 & 0.17 & 3.00 & 0.17 & 3.00 & 0.19 & 3.00 \\ \hline \end{tabular}
\end{table}
Table 4: List of \(P_{max}\) and \(T_{p}\) values for different epicentral distance subgroups.  
Figure 9: Mean power spectra for different magnitude groups in the Loess Plateau region.  
and factors such as magnitude (\(M_{\text{S}}\)), epicentral distance (\(R\)), focal depth (\(D\)), and loess thickness (\(H\)) were also analyzed.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Descriptive Security Analysis
#### 5.2.7 KCI Attack Resistance  
Resistance against KCI attacks refers to the inability of \(\mathcal{A}\) to impersonate another legitimate participant, [PERSON], to authenticate with [PERSON] after [PERSON]'s long-term private key disclosure. Suppose \(\mathcal{A}\) learns the long-term key \(k_{s}\) of the end device \(S\) and wants to impersonate \(SP\) to produce \(\{A_{sp},TS_{sp},V_{sp}\}\) to convince \(S\) that the message is legitimate and generated by \(SP\). For \(V_{sp}=h(WS_{s}\|TS_{sp}\|ID_{sp}\|SSK_{sp})\), where \(\mathbb{C}_{sp}=x_{sp}\cdot B_{sp}=k_{sp}x_{sp}\cdot A_{s}\), and \(k_{sp}\) has not been compromised, \(\mathcal{A}\) cannot impersonate server \(SP\) to perform authentication and key agreement with \(S\). Similarly, \(\mathcal{A}\) cannot carry out KCI attacks against \(SP\).
# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 2 Related Work
### Content-Based Filtering  
In the content-based filtering approach, music is recommended to exploit the system's comparison between the items' content and a user's profile. Each item's content is represented as a set of tags. In the case of textual documents, the tags can describe the words within a document. In the case of music, the tag--in the simplest form--can be related to the genre. Several issues must be considered when implementing such a category of systems:  
* tags can either be assigned automatically or manually;
* the tags must be generated or assigned such that both the user's profile and the items can be easily matched and compared to derive a similarity measure;
* a learning algorithm must be chosen that learns and classifies the user's profile based on played songs (i.e., seen items) and offers recommendations based on it.  
To recommend music, the song's features, such as loudness, tempo, and danceability, are analyzed. Among the widely used methods to perform content-based filtering and measure similarities between user's profile and songs are (i) clustering [27] and (ii) expectation maximization with Monte Carlo sampling. These techniques can recommend music tracks also with very little data; thus, they solve the cold-start problem (seen in Section 2.1). The major challenge of these approaches is in the appropriateness of the item model [28]. Another major drawback is that, with tags trying to describe the songs' macro-characteristics, these approaches fail to differentiate crucial musical differences between similar songs in terms of tags.  
In [29], the authors introduce MoodPlay, a system for recommending music artists based on the general mood of the artists and the self-reported mood of users. The authors proposed the method and the visual (graph-based) interface of the system. In addition, they performed an experiment with more than 200 final users. From these experiments, it emerged that mood plays a crucial role in the recommendation. The main differences from this work are (i) we recommend songs, not artists; (ii) we only base our recommendations on the starting emotion and target emotion; thus, we do not consider artists' general mood; (iii) our system is designed to induce a particular emotion, not to recommend a specific artist based on \"the similarity\" between certain user and artist moods; (iv) the ultimate goal of [29] was more related to understanding how users perceive recommendations through visual interfaces than generating an affective recommender system.  
With respect to these kinds of systems, _Moodify_ does not use pre-defined item content to compare with the user profile. It \"dynamically\" builds an intelligent agent capable of selecting the music most suited to the user's target emotional state simply by observing the choices and the ratings assigned by the user himself during a training phase.
# Demand-Response Control in Smart Grids
###### Abstract  
In the smart grid, electricity price is a key element for all participants in the electric power industry. To meet the smart grid's various goals, Demand-Response (DR) control aims to change the electricity consumption behavior of consumers based on dynamic pricing or financial benefits. DR methods are divided into centralized and distributed control based on the communication model. In centralized control, consumers communicate directly with the power company, without communicating among themselves. In distributed control, consumer interactions offer data to the power utility about overall consumption. Online auctions are distributed systems with several software agents working on behalf of human buyers and sellers. The coordination model chosen can have a substantial impact on the performance of these software agents. Based on the fair energy scheduling method, we examined Vickrey and Dutch auctions and coordination models in an electronic marketplace both analytically and empirically. The number of software agents and the number of messages exchanged between these agents were all essential indicators. For the simulation, GridSim was used, as it is an open-source software platform that includes capabilities for application composition, resource discovery information services, and interfaces for assigning applications to resources. We concluded that Dutch auctions are better than Vickrey auctions in a supply-driven world where there is an abundance of power. In terms of equity, Dutch auctions are more equitable than Vickrey auctions. This is because Dutch auctions allow all bidders to compete on an equal footing, with each bidder having the same opportunity to win the item at the lowest possible price. In contrast, Vickrey auctions can lead to outcomes that favor certain bidders over others, as bidders may submit bids that are higher than necessary to increase their chances of winning.  
s +
Footnote †: journal: Applied Sciences  
amart grid; demand response; centralized and distributed control; fair energy scheduling method; Vickrey auction; Deutch auction; GridSim software platform
## 4 Experiments and Discussion
### Discussions  
This experiment was performed on an industrial RCS messages data and its results turn out to be promising. The average accuracy achieved was 96.5% when two thirds of the RCS messages were chosen for training. The proposed models and methods perform better on ham messages by correctly predicting 122 out of the total 124 testing ham messages. The precision for ham messages hits 98.4%. On the other hand, the experiment produces acceptable results for spam messages. This is due to the shortage of spam RCS messages. Since the RCS service is just at its initial stage, spam messages are not easy to collect due to their limited quantity. Insufficient training limits the accuracy of spam messages. With the development of the RCS service and its economy ecosystem, we believe the proposed models and methods will produce better and more reliable results.
## 3 Results and Discussion
### Analytical Application
#### 3.2.2 Investigation of Method Selectivity  
The potential impact of common foreign ions on the estimation of copper was tested at optimized conditions and in the presence of 2 \(\mu\)g mL\({}^{-1}\) Cu(II). The tolerance limit of interfering ions was expressed by the concentration of these ions or species that makes the relative error in the copper determination greater than \(\pm 5\%\). Al\({}^{3+}\) and Mn\({}^{2+}\) seriously interfered--probably due to complex formation with AMT--as shown in Table 3. However, these interferences were eliminated by addition of 0.5 mL KF\(\bullet\)2H\({}_{2}\)O (0.1 mol L\({}^{-1}\)), which reduced the relative error to an acceptable \(\pm 5\%\) level at an interfering ion concentration of 100 \(\mu\)g mL\({}^{-1}\).  
Figure 10: The relationship between the concentration of AMT reagent and the absorbance of [Cu(_L_)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O at 434 nm. Cu(II) concentration = 2 \(\mu\)g mL\({}^{-1}\).
# Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
## 2 Materials and Methods
### Application of Models
#### 2.3.1 Application of ARIMA  
The time series showed random behavior, as indicated in Figure 4. Therefore, a series analysis was carried out to identify the ARIMA model that best fits the data according to the ARIMA algorithm (Figure 1).  
Figure 3: (**a**) Algorithm for several LSTM network layers; (**b**) LSTM cell [29,30].  
First, we verify the stationarity of the series using a Dickey-Fuller test at a significance level of \(\alpha=0.05\). We used the R software (v. 3.5.3) to codify ARIMA.  
The \(p\)-value = \(0.7546>\alpha=0.05\); therefore, the time series is not stationary. To make the time series stationary, we performed first-order differentiation followed by another stationarity test.  
The \(p\)-value = \(0.1<\alpha=0.05\); therefore, the series is stationary. The differentiated series and the ACF were then analyzed. The former presented white-noise behavior, and the ACF corroborated the independence of the data. Based on this analysis, the best model to fit random walk was the ARIMA model (0, 1, 0), which corresponds to the following expression:  
\[(1-B)Y_{t}=\varepsilon_{t} \tag{2}\]  
where \(B\) is the delay operator, \(BY_{t}=Y_{t-1}\), and \(\varepsilon_{t}~{}\sim~{}RB(0,\,\sigma^{2})\). By testing the assumptions, we can verify the stationarity, independence, normality, and homoscedasticity.  
Figure 4: Daily prices of the EUR/USD exchange rate.  
For stationarity, the \(p\)-value = 0.1 \(<\alpha=0.05\), so the residuals are stationary. For independence, the \(p\)-value = 0.2118 \(>\alpha=0.05\), so the residuals are independent. For normality, the \(p\)-value = 2.2\({}^{-16}\)\(<\alpha=0.5\), so the residuals do not follow a normal distribution. For homoscedasticity, the \(p\)-value = 0.1656 \(>\alpha=0.05\), so the residuals present homogeneous variance. Thus, the normality test was not fulfilled. Consequently, the ARIMA model will not yield reliable results a priori; however, we applied this model to the data for comparison purposes.
# Modelling Regime Changes of Dunes to Upper-Stage Plane Bed in Flumes and in Rivers
## 4 New Step Length Model
(2009) step length model, as can be seen in Figure 5. To reiterate, with this modification the step length no longer depends on only bed shear stress or friction velocity as with bed load, but also on the water depth as with suspended transport. It represents processes inherent in the turbulent mixing of suspended material--namely, that larger water depth leads to larger turbulent vortices, which in turn leads to sediment higher in the water column, and due to the size of the vortices, a larger settling distance. Furthermore, in the new step length model the dimensionless step length keeps increasing for \(\theta^{\prime}\) values above 0.8 (with the same slope as between \(\theta^{\prime}=0.5\) and \(\theta^{\prime}=0.8\)) because formulae for the spatial lag or adjustment length of suspended sediment are generally not capped at a certain value (see e.g., [PERSON] et al. (2011); [PERSON] et al. (2011)). Different values of \(\alpha_{min}\) at \(\theta^{\prime}=0.5\) and \(\alpha_{max}\) at \(\theta^{\prime}=0.8\) have been tested, and using \(\alpha_{min}=50\) and \(\alpha_{max}=350\) works best within the new dune evolution model compared to the dune evolution results of [PERSON] et al. (2009) [PERSON] et al. (2009). The water depth at the start of the transitional regime was 0.1166 m, which will be used as the reference water depth \(h_{ref}\). In Figure 5 the currently used model for \(\alpha_{g}\) is compared with the [PERSON] et al. (2009) [PERSON] et al. (2009) step length model for \(\alpha_{S}\). It should be noted that this figure is without the additional influence of the water depth as defined in Equation (18). Combining that equation with the figure above and all other previous considerations leads to the following new equation for \(\alpha\):  
\[\alpha\left(\theta^{\prime},h\right)=\left\{ \begin{array}{rl}\alpha_{min} \frac{h}{h_{ref}},&\theta^{\prime}\leq 0.5\\ \left[\alpha_{min}+\left(\theta^{\prime}-0.5\right)\frac{\alpha_{max}-\alpha_ {min}}{0.8-0.5}\right]\frac{h}{h_{ref}},&\theta^{\prime}>0.5\end{array} \right. \tag{19}\]  
where \(\alpha_{min}=50\), \(\alpha_{max}=350\), and \(h_{ref}=0.1166\) m.  
By multiplying the result with the median grain diameter, the dimensional step length is found (as defined by Equation (13)). By using this method, the value of \(\alpha\) can become higher than the maximum observed value of \(\alpha\) (250) found in the bed load experiments of [PERSON] and [PERSON] (1980) [PERSON] and [PERSON] (1980) [PERSON] and [PERSON] (1980). With this modified method for step length calculation, the specific influence of suspended sediment on the spatial lag of sediment transport (i.e., through the water depth) is introduced in a strongly schematized way. This leads to the possibility of also applying the model in the full-scale conditions of rivers (with larger water depths), in which suspended sediment is known to play a large role.
## 2 Upper Bound Solution
### Triangular Wedge Failure Mode  
Figure 2 schematically describes the failure mechanism of the triangular wedge failure mode. In Figure 2, surface \(B\) is an inclined bottom failure plane forming an angle \(\theta\) with  
Figure 1: Schematic diagram of the block failure mode for a single-track system.  
the ground surface, while surfaces \(S_{1}\) and \(S_{2}\) are two triangular side failure planes. In the triangular wedge failure mode, the prism-shaped soil block moves at velocity \(V\).  
In this case, total external power \(P\) is expressed as  
\[P=\big{(}F_{t}+W_{g}\text{sin}\,\beta\big{)}V\text{sin}\,\theta-W_{g}V\text{cos} \,\beta\text{cos}\,\theta, \tag{8}\]  
where \(F_{t}\) represents the soil thrust under the triangular wedge failure mode. For the triangular wedge failure mode, plastic dissipation rate \(D_{b}\) along the bottom failure surface \(B\) is defined in  
\[D_{b}=c_{u}\frac{H}{\text{cos}\,\theta}DV, \tag{9}\]  
whereas, along the two triangular-shaped side failure surfaces \(S_{1}\) and \(S_{2}\), plastic dissipation rate \(D_{\text{s}}\) is expressed as  
\[D_{s}=2\bigg{(}c_{u}\frac{H^{2}\text{tan}\,\theta}{2}V\bigg{)}=c_{u}\text{tan }\,\theta H^{2}V. \tag{10}\]  
Consequently, the total dissipation rate \(D\) is formulated as  
\[D=D_{b}+D_{s}=c_{u}\frac{H}{\text{cos}\,\theta}DV+c_{u}\text{tan}\,\theta H^{ 2}V=c_{u}H\bigg{(}\frac{D}{\text{cos}\,\theta}+\text{tan}\,\theta H\bigg{)}V. \tag{11}\]  
Assuming that total external power is entirely dissipated to produce the plastic deformation along failure surfaces, the soil thrust \(F_{t}\) can be written as  
\[F_{t}=c_{u}H\bigg{(}\frac{D}{\text{sin}\,\theta\text{cos}\,\theta}+\frac{H}{ \text{cos}\,\theta}\bigg{)}+W_{g}\bigg{(}\text{cos}\,\beta\frac{\text{cos}\, \theta}{\text{sin}\,\theta}-\text{sin}\,\beta\bigg{)}, \tag{12}\]  
which shows that the soil thrust \(F_{t}\) is a function of the geometric variable \(\theta\). Therefore, the stationary upper bound solution is identified at the angle \(\theta\) where \(\partial F_{t}/\partial\theta=0\); thus,  
\[\frac{\partial F_{t}}{\partial\theta}=c_{u}H\bigg{(}\frac{D}{\text{cos}^{2} \,\theta}-\frac{D}{\text{sin}^{2}\,\theta}+\frac{H\text{sin}\,\theta}{\text{ cos}^{2}\,\theta}\bigg{)}+W_{g}\bigg{(}-\text{cos}\,\beta\frac{\text{cos}^{2}\, \theta}{\text{sin}^{2}\,\theta}-\text{cos}\,\beta\bigg{)}, \tag{13}\]  
which can be reorganized by  
\[\text{sin}^{3}\,\theta+\bigg{(}\frac{W_{g}\text{cos}\,\beta}{c_{u}H^{2}}+ \frac{2D}{H}\bigg{)}\text{sin}^{2}\,\theta-\bigg{(}\frac{W_{g}\text{cos}\, \beta}{c_{u}H^{2}}+\frac{D}{H}\bigg{)}=0, \tag{14}\]  
Figure 2: Schematic diagram of the triangular wedge failure mode for a single-track system.  
which is a third-order polynomial equation of \(\sin\theta\). Although there is an analytical way to solve Equation (14) for \(\theta\), the present research numerically solves it using the bisection method, which is very efficient when the solution range is definite (in this problem, \(0<\theta<\tan^{-1}(L/H)\)).
## 3 System Framework and Model Training  
### Model Architecture  
We propose a novel approach to perform both vibration signal generation and machinery fault diagnosis, called BiWGAN-GP. It combined the architecture of BiGAN and the advanced training method of WGAN-GP. Figure 2 Shows the BiWGAN-GP's architecture. Overall, the BiWGAN-GP fault diagnosis model has three main modules: generator, encoder, and discriminator, respectively.  
The generator consists of a multi-layer neural network with an increasing number of hidden units to mapping a low-dimension noise \(z\) into a high dimension generated signal \(G(z)\). Therefore, the input dimension of the generator depends on the latent vector \(z\), and the output dimension matches the real signal length. The number of units in the latter hidden layer is double that of the former layer. Specifically, the topology of \(G\) is (64, 128, 256, 512).  
Different data preprocessing methods will affect the construction of the generator. If normalization is used, i.e., \(x^{\prime}=(x-x_{min})/(x_{max}-x_{min})\), the data amplitude is limited to (0,1). Then ReLU activation function is used between the layers of \(G\), and the SoftPlus is used in the last layer to ensure that there is no negative value in generated signals. Another commonly used data preprocessing operation is standardization, i.e., \(x^{\prime}=(x-\mu)/\sigma\), where \(\mu\) is the mean and \(\sigma\) is the standard deviation of the entire dataset. Since standardization may have negative values, to avoid truncation, we use LeakyReLU as the activation function of the last layer of \(G\) to get better generation results.  
The encoder is also composed of a multi-layer neural network but with a decreasing number of hidden units to extract the most salient features of the data. Specifically, the topology of \(E\) is (512, 256, 128, 64), where the number of units in the hidden latter layer is half that of the former layer. Its input and output dimensions are opposite to those of the generator. LeakyReLU is used as the activation function in the encoder.  
The discriminator is divided into three parts. Two multi-layer neural networks extract features of the joint pair \((x,E(x))\) that comes from real data or \((G(z),z)\) that comes from fake data, and their topology are (256, 128, 64) and (128, 64). Then the features are concatenated together as a joint feature which size is 128. Finally, through another multi-layer neural network which topology is (128, 64, 32, 1), the discriminatory result is obtained. Also, LeakyReLU is used as the activation function.  
Figure 2: The architecture of BiWGAN-GP.