esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
According to our knowledge, this kind of signal was not considered in the context of the comparison of different techniques and thus in our opinion, such a comparison is worthy to demonstrate. The signals with non-cyclic impulsive behavior we observe in the real environment. One of the examples is the crushing machine [42] used in mines. During the operation of the machine (the crushing process), apart from the background noise (which is often assumed to be Gaussian White Noise) large observations appear in the vibration signal, due to the nature of the machine's work. Moreover, in the case of local damage, additional cyclic impulses are hidden in the noise. In this case, the detection of local damage is very difficult. In this paper, we have used simulated signal with specific properties (cyclic, non-cyclic impulses and noise components). The use of a generic model has allowed presenting the problem and test algorithms for various cases. The problem can be formulated as the extraction of the SOI from the Gaussian and non-Gaussian noise. Recently the last, more demanding problem has been recognized by several authors and some new ideas have been developed [21; 42; 43; 44]. Our analysis will cover spectral kurtosis [45], kurtogram [19], stability index (Alpha selector) [46], spectral Gini selector [47], spectral smoothness index [17], infogram [48] and conditional variance-based selector [49]. The first six methods were considered in the literature as the most effective and classical ones in the problem of local damage detection. In our recent research [49], we have utilized the statistic called the conditional variance statistic and proposed a new algorithm for local damage detection which seems to be applicable when the background noise is contaminated by the non-cyclic large impulses. In the paper [49], we have indicated the limitations of the proposed method and proved its effectiveness, especially when the ratio of the amplitudes of non-cyclic to cyclic impulses is large. In the current paper, which can be considered as the extension of our previous research, we demonstrate that the resultsobtained for the conditional variance-based selector are superior with respect to the results based on the mentioned other algorithms. The rest of the paper is organized as follows. In Section 2, we remind the reader of the definitions of seven considered selectors for local damage detection. Next, in Section 3 we give a short description of the model of the simulated signal and comparatively study the results for the considered algorithms. At the end of this section the efficiency of the analyzed selectors has been tested on real data. The last section concludes the paper.
## 4 The Proposed Routing Algorithm
### Human Location Prediction (HLP) Model  
We propose a human location prediction model based on a RNN and LSTM cells [16] for estimating mobile users' next positions, using information from their previous movements. In particular, the proposed model takes movement information of mobile users in current and previous time slots as input and outputs estimated locations for the next \(k\) time slots.  
The information that will be used as input for the prediction model is discussed next. First, to distinguish mobile users, each one is assigned a unique ID. Then, a one-hot vector is used to represent the user ID. Let \(\overrightarrow{a_{u}^{\overrightarrow{j}}}\) denote the one-hot vector that indicates the ID of user \(u\). Second, a time slot index is also used as an input feature. The one-hot vector that represents time slot \(h\) is denoted \(\overrightarrow{a_{h}^{\overrightarrow{j}}}\). Third, the day of the week is considered. Let \(\overrightarrow{a_{h}^{D}}\) be the one-hot vector that indicates the day of the week of time slot \(h\). Fourth, the locations of mobile users are taken into account. The one-hot vector that indicates the location of user  
Figure 2: The HLPRP routing process.  
in time slot \(h\) is \(\overrightarrow{a_{u,h}^{\top}}\). Finally, let \(t\) denote the current time slot. From time slot \(t\), the human location prediction model will predict the location of mobile user \(u\) in the next time slot, \(q\) (e.g., with \(k=4\), \(q\) could be time slot \(t+1\), time slot \(t+2\), time slot \(t+3\), or time slot \(t+4\)).  
To determine time slot \(q\), a one-hot vector is used. Let \(\overrightarrow{a^{PT}}\) denote the time slot in which we want to predict the location of user \(u\). \(\overrightarrow{a^{PT}}\) is considered an input feature as well. One-hot encoding is used for all input features because those features are nominal and not ordinal. The input will be information from time slot \((t-m+1)\) to current time slot \(t\). Let us define \(\overrightarrow{x_{u,h}}\) as the input vector of user \(u\) in time slot \(h\), specifically, \(\overrightarrow{x_{u,h}}=\{\overrightarrow{a^{PT}},\overrightarrow{a_{u}^{ \top}},\overrightarrow{a_{h}^{\top}},\overrightarrow{a_{u,h}^{\top}}, \overrightarrow{a_{u,h}^{\top}}\}\).  
This model is based on a RNN with LSTM cells. After the last hidden state of the LSTM cell, a fully connected layer with ReLU activation and a fully connected layer with a softmax activation function is used for the output layer. Let \(\widehat{y}_{u,q}\) denote the output vector that shows the probability that mobile user \(u\) will visit locations in time slot \(q\) (e.g., \(q=t+1\)). For example, \(\widehat{y}_{u,q}=[0.1,0.05,0.01,\ldots,0.3]\) means that in time slot \(q\), the probability of visiting the first location is 0.1; the probability of visiting the second location is 0.05, and so on. The notations used in the human location prediction model are shown in Table 1.
# Power-Cadence Relationships in Cycling: Building Models from a Limited Number of Data Points
## 1 Introduction
In this regard, we previously proposed a residual-based filtering method in which only the data point with the highest value in each 5-RPM cadence bin was retained [5]. While this method improves the likelihood of capturing truly maximal efforts, it loses validity when cadence bins include only one or no data points, thereby increasing the risk of including submaximal efforts. When such points are incorporated, the resulting power-cadence curve may be artificially lowered or flattened, and ultimately underestimating Pmax. This issue can often be identified through visual inspection of power-cadence models overlaid with experimental data points, where truly maximal and cadence-specific data points appear poorly fitted [6; 7; 21; 22; 28; 34; 38]. To improve the selection of a limited number of truly maximal data points, it is essential to consider the mathematical characteristics of the power-cadence relationship and the third-order polynomial regression. The accuracy of the model depends on using maximal data points strategically distributed across three cadence regions: low cadences (influencing the initial slope), cadences near the apex (shaping the curvature), and high cadences (defining the descending limb). By varying external resistance across efforts, it is feasible to generate data points spanning the lower, middle, and upper ends of the cadence spectrum [5; 7; 11; 12; 22]. Therefore, a small set of strategically placed data points may be sufficient for accurate curve fitting, thereby reducing the total number of efforts required and minimizing participant fatigue. The primary objective of this study was to develop and evaluate a new filtering method for selecting a limited number of data points that best represent an individual's maximal power-generating capacity across key cadence ranges, with the goal of improving the accuracy of individual-specific power-cadence profiling. We hypothesized that a third-order polynomial model based on five strategically selected maximal data points; one drawn from low cadences, three drawn from medium cadences (around the apex of the curve), and one drawn from high cadences; would provide estimates of Pmax comparable to, or better than, those obtained from models using larger numbers of data points. To test this hypothesis, we analyzed data from 23 participants. We compared three modeling approaches: (i) a High-Number (HN) model based on all available data, (ii) a Moderate-Number (MN) model based on our previously proposed 5-RPM bin method, and (iii) a Low-Number (LN) model based on five data points selected from key regions of the curve using a residual-based filtering strategy. We evaluated each model in terms of curve goodness of fit, Pmax estimates, and how well each model predicted experimentally measured peak power (Ppeak) at the corresponding cadence.
# Modulation Recognition System of Electromagnetic Interference Signal Based on SDR
## 2 Modulation Recognition System
\begin{aligned} V\big{(}f\big{)}&=-\frac{E_{s}^{2}}{T ^{4}}\sum_{n}A\big{(}f\big{)}\delta\bigg{(}f-\frac{n}{T}\bigg{)}\\ A\big{(}f\big{)}&=G\big{(}f\big{)}*G\big{(}f\big{)}*G \big{(}f\big{)}*G\big{(}f\big{)}\end{aligned}  \tag{7}\]  
where, \(G(f)\) is the Fourier transform of \(g(t)\) and \(*\) represents linear convolution. According to the property of rising cosine pulse, the spectral band limited interval of quadratic spectrum is\([-2(1+\alpha)/T,2(1+\alpha)/T]\). Under the conditions of roll off coefficient \(\alpha<0.5\), \(n\in\{0,\pm 1,\pm 2\}\), therefore, the fourth power spectrum \(V(f)\) of QPSK has non-zero values only at the frequencies \(f\in\{0,\pm 1/T,\pm 2/T\}\), that is, there are discrete spectral lines. Due to the attenuation of \(A(f)\), the discrete spectral line intensity at frequency \(f=\pm 2/T\) is much less than that at frequency \(f=\{0,\pm 1/T\}\). For quadrature amplitude-modulated QAM signals with symmetrical origin and equal probability distribution, the statistical expectations in the form of quadratic and quartic spectra. \[E[s^{2}(t)]=E[s^{8}(t)] \tag{8}\]  
\[E\bigg{[}s^{4}(t)\bigg{]}=(-E_{s}^{2})\sum_{n}s^{4}(t-nT) \tag{9}\]  
It can be seen that different kinds of modulation signals have discrete spectral lines in different high-power spectra. For example, the 16 QAM signal has obvious discrete spectral lines in the fourth power spectrum, while the 8 PSK signal only has obvious discrete spectral lines in the eighth power spectrum. Therefore, to estimate the frequency and symbol rate of each modulated signal, it is necessary to calculate the quadratic spectrum, the quartic spectrum, and the eighth spectrum of the signal, calculate the peak-to-average ratio of the spectral line of the signal in the three spectra, respectively, and find the spectrum where the maximum peak-to-average ratio is located. The discrete spectral line position corresponding to the maximum peak-to-average ratio in the spectrum is the estimated value of the signal frequency. The distance between the position of the spectral line corresponding to the maximum value and the second maximum value of the peak-to-average ratio is the estimated value of the signal symbol rate.
## 4 Implementation
### Smart EMS System  
The EMS (emergency medical service) statistics from National Fire Department of Korea in 2021 [19] show the following:  
* an 11.3% increase in transfer cases from 2020, which is 1,775,000 transfer cases;
* a 12.4% increase in transfer cases from 2020, which is 1,823,000 individual transferred patients.  
Compared with those from 10 years ago, the increase rates of the cases and individual patients are 27.8% and 25.6%, respectively, which show that the rates have been increasing consistently.  
Among the individual patients, the four major critical patient types are those with illnesses of the heart (cardiovascular disease), illnesses of the brain (cerebrovascular disease), cardiac arrest, and severe trauma, rating 44.2%, 39.2%, 11.4%, and 5.3%, respectively [19].  
The smart EMS supported by smart IoT systems may be able to increase the survival rate of the critical patients dramatically if the proper EMS is provided.  
EMS is the service utilizing the IoT technologies to enforce the activities of emergency medical workers responsible for on-spot emergency medical treatments and patient transfer at the time of emergency medical dispatch in order to increase the survival rate of the critical patients [20; 21; 22; 23; 24].  
Compared with the existing EMS, smart EMS provides patients with IoT devices at the time of emergency medical workers arrive at the place of the emergency occurrence.  
The medical information of the patients, including pulse, oxygen saturation, etc., is collected on the spot and transmitted to the 911 service and the proper medical institutes so that the proper treatments and prescriptions can be made by the medical doctors or specialists and, consequently, the golden time to rescue the patients' lives can be obtained by the emergency medical teams at the time of the patients arrival at the medical institutes [25; 26].  
Such smart EMS based on the IoT is a futuristic EMS system where the biometrics of the patients, including electrocardiography, blood pressure, etc., are collected on the spot and transmitted to the proper medical institutes using a 5G network, their emergency medical situations are analyzed by the medical specialists in the institutes, the emergency medical workers are informed of the proper emergency medical pre-treatments for the patients by the specialists, and the most suitable medical institutes and the shortest and safest routes to the institutes are provided to the workers for the transfer of the patients.
## 1 Introduction
A total of 140 samples were examined, and the following properties were measured: uniaxial compressive strength (UCS), tensile strength (TS), abrasion (Abr), and slack durability index (SDI), in addition to the physical properties of density (D), P-wave velocity (PWV), and water absorption (W). Furthermore, the material cost per cubic meter was included in the analysis. To determine the appropriate selection, we applied the technique for order of preference by similarity to ideal solution (TOPSIS). The principal concept of TOPSIS offers the best decision support, and thus leads to a solution that is close to the optimum. This technique is based on benefit criteria that are related to positive or negative ideal solutions. High and low solutions are compared to determine the distance between alternative solutions. This methodology has been widely applied in several fields, but to the best of the authors' knowledge, this is the first application of TOPSIS to canal linings and relevant construction materials [1]. A mathematical model was adopted to calculate the distance between positive and negative alternatives; the authors made assumptions to define and create the rate and weight for each characteristic. The remainder of this manuscript is organized as follows: Section 2 reviews the relevance of TOPSIS, its multiple uses in different scientific fields, and its increased efficiently relative to other methods. Section 3 describes the site investigations and documents the sites with photographs of actual canal linings. Section 4 illustrates and summarizes the laboratory test results of the mechanical and physical properties of the rock samples. Section 5 discusses material applications based on their properties for different purposes. Section 6 presents the methodology, data collection, constraints, problem formulation, raw material requirements, and implementation according to the analysis methods, includingthe relevant mathematical formalism. Moreover, it provides quality index calculations with respect to property weights and rates. Section 7 presents and discusses the results. Finally, the conclusions are drawn in Section 8. Section 2 reviews the different uses of TOPSIS to direct decision makers toward sustainable and optimal alternatives among a list of options. This section highlights the relevance of the study as the first to focus on building materials and their use in canal lining.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 6 Conclusions  
In this paper, a DQN-based framework for geoacoustic inversion is proposed. The framework can be defined as an optimization-based inversion method with a learnable search strategy, which keeps both advantages of optimization-based and ML-based approaches. Its performance is assessed by two numerical cases for estimating the shear wave velocity profile based on Scholte wave dispersion curves and compared with that of three popular optimization methods. Compared to the fastest conventional method, the running time of the proposed framework can be further reduced by 37.7% (Case 1) and 68.9% (Case 2), respectively. Compared to the best conventional method, the misfit of the proposed framework can be further reduced by 51.6% (Case 1) and 9.4% (Case 2), respectively. The results demonstrate the potential of DRL for geoacoustic inversion and the superior performance of the proposed framework. More specifically, the proposed framework can provide a faster, lower misfit, lower relative error, and lower uncertainty inversion. Please note that the application scope of the proposed framework is the whole geoacoustic inversion field. The proposed framework can easily be applied to different inversion tasks by using an appropriate forward model.  
The future research direction will focus on the representation of the misfit space (i.e., the environment state) and the design of new available actions in the action space for the agent to explore the parameter space.  
Implementation code availability: The code will be made available for the peer reviewers and the public upon request after the manuscript is published.  
Figure 9: Action execution by the DQN framework in an independent inversion.  
**Author Contributions:** Formal analysis, X.Z. and H.D.; funding acquisition, H.D.; methodology, X.Z. and H.D.; resources, X.Z. and H.D.; software, X.Z. and H.D.; writing--original draft, X.Z.; writing--review and editing, H.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The simulated dispersion curves will be made available upon request after the manuscript is published.  
**Acknowledgments:** The authors would like to acknowledge the Norwegian Research Council and the industry partners of the GAMES consortium at NTNU for financial support (grant no. 294404). This work was partially supported by the SFI Centre for Geophysical Forecasting under grant 309960. [PERSON] would like to acknowledge the China Scholarship Council (CSC) for the fellowship support (no. 201903170205).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of an Axial Turbine for Highly Downsized Internal Combustion Engines
## 3 Methodology
### CFD Simulation Details  
CFD simulations were performed with H and O grid meshes and the k-\(\omega\) Shear Stress Transport (SST) model. The O mesh was used to model the boundary layer around the blade, as this is ideal for capturing the typical flow behavior in the area with an elevated number of prism layers of increasing size. The H grid is a classic mesh type for turbomachinery, with a lower computational cost compared to polyhedral meshes, allowing for timesaving but effective modelling of the fluid dynamics problem. The reason for using the k-\(\omega\) SST turbulence models stays in the effective use of the k-\(\omega\) and k-\(\varepsilon\) turbulence models in different parts of the fluid domain. In fact, while the k-\(\omega\) model is used in the boundary layer, thanks to the SST formulation, the k-\(\varepsilon\) model is used for the flow outside the boundary layer, overall making the simulation less sensitive to the initial turbulence level (a feature of particular importance for this case, as it depends on the engine operating point, exhaust and scroll design, etc.).  
The simulations were started with a low-order RANS solver and coarser grid. Upon convergence, the grid was improved to a finer one, determined with the mesh sensitivity study, until convergence. At this point, the CFD solver was upgraded to a higher order in such a way to have a more precise solution. The operation of solving the problem with increasing mesh quality and solver order allows the reduction of the CPU time (which can often be more than 12 h for just the stator and rotor periodic simulation with an Intel i7-7700 HQ at 3.8 GHz).  
Finally, the mesh quality was always controlled, ensuring that the mesh Jacobian determinant ratio, which is an index of the mesh distortion, was always greater than zero and with the majority of the elements (more than 95% in the most precise simulations) having higher quality values (Figure 13).  
Figure 14 illustrates an example of the correction of a low-quality mesh with volumes with a low Jacobian ratio to a higher quality one in order to ensure a correct cell growth rate and appropriate size in every part of the fluid domain. These corrections were mainly performed acting on the O-grid thickness and number of elements (basically the boundary layer mesh zone) and mesh refinements at the leading edge and trailing edges (especially on the suction side of the blades, in order to properly catch any possible separation bubble or stall).  
Figure 13: Typical CFD mesh quality distribution graph—number of elements versus Jacobian determinant det(J) ratio
## 4 Experiments
### Handwritten Digit Clustering on USPS  
In this experiment, we test the clustering performance of our methods on the handwritten digit dataset of USPS. In total, this dataset contains 9298 handwritten digit images of \"0\" through \"9\", each of which is in the size of 16 \(\times\) 16 pixels, with 256 gray levels per pixel. Some sample images are provided in Figure 4. We represent the digital images as 256-dimensional vectors and randomly select \(c=[2,3,4,5,6,8,7,8,9,10]\) digits for clustering. For each selected digit, 48 images are randomly picked up. We repeat these clustering tests 30 times for each \(c\) digits and the mean clustering errors are computed. Here the parameters \(\lambda_{1}=800,\lambda_{2}=10,000\) for LLE-SSC and \(\lambda_{1}=1,\lambda_{2}=10\) for LLE-LRR.  
Table 4 reports the clustering results. It is observed that the proposed methods, LLE-SSC and LLE-LRR, almost outperform the compared methods in all cases. Compared with the experimental results on the Extended Yale B face dataset, the improvement of our methods in this experiment is not significant, which is analysed and explained as the handwritten digits written by different people, having too much diversity both in the shape and texture as shown in Figure 4.  
Figure 4: Sample digits images from the United States Postal Service (USPS) dataset.  
Figure 3: Sample faces images from the Extended Yale B databset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Classes**} & \multicolumn{8}{c}{**The Clustering Error (\%)**} \\ \cline{2-7}  & **SSC** & **LRR** & **NSLLRR** & **SMR** & **LapLRR** & **SSC-LLE** & **LRR-LLE** \\ \hline
2 & 7.74 & 6.87 & 3.22 & 3.49 & 4.09 & 2.00 & **1.23** \\
3 & 10.83 & 9.81 & 5.42 & 6.16 & 5.13 & 2.91 & **1.80** \\
5 & 23.81 & 18.01 & 17.01 & 9.05 & 10.84 & 3.31 & **2.78** \\
8 & 32.47 & 33.81 & 31.94 & 21.23 & 20.92 & 3.53 & **3.18** \\
10 & 34.73 & 36.15 & 34.33 & 28.36 & 25.52 & 3.68 & **3.55** \\ mean & 21.92 & 20.93 & 18.38 & 13.66 & 13.30 & 3.09 & **2.51** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clustering results on Extended Yale dataset B. The clustering errors for 2, 3, 5, 8, 10 classes and their mean error are shown here. The best results are in bold font.