esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[17] highlights the significance of noise on heart rate variability (HRV) metrics, presenting a preprocessing technique that proficiently reduces the impact of artifacts on essential diagnostic indicators. The literature reviewed indicates a transition from traditional filtering techniques to more sophisticated machine learning and hybrid methodologies. Traditional filters establish a fundamental basis for the attenuation of noise; however, contemporary methodologies emphasize the importance of computational efficiency and adaptive functionalities. Subsequent investigations ought to focus on the amalgamation of these methodologies, thereby guaranteeing maximal noise attenuation while preserving signal integrity to the greatest extent possible. The advancements observed in this domain present considerable promise for the enhancement of ECG signal quality, which, in turn, could lead to improved diagnostic outcomes within healthcare environments. Recent advances in deep learning have shown promise in signal processing tasks, yet existing neural network architectures for ECG denoising have limitations in capturing long-range dependencies and complex temporal relationships within the signal. This is particularly crucial when dealing with EMG artifacts, which can occur at various time scales and intensities. To address these challenges, we present ECGnet, a novel denoising network that leverages transformer architecture to effectively remove EMG artifacts from ECG signals. Our approach exploits the transformer's superior ability to model long-range dependencies and its self-attention mechanism to identify and preserve crucial ECG morphological features while selectively removing muscle-related noise. The paper is structured in the following way: in the Section 2, we present ECGDnet, elaborating on its architecture and its innovative methodology for ECG signal denoising. The datasets employed for training and validation are detailed, along with the preprocessing methods implemented to incorporate EMG noise into the data. Subsequently, we delineate the performance metrics employed to assess the denoising efficacy of the proposed approach. The Section 4 emphasizes a comparative analysis of ECGDnet against other leading models, highlighting its performance and computational efficiency. In Section 6, the paper offers valuable insights and recommendations for future research avenues in the realm of ECG signal processing and its implementation in environments with limited resources.
## 2 Materials and Methods
### Eddy Covariance and Meteorological Measurements  
The CO\({}_{2}\) and CH\({}_{4}\) turbulent fluxes at the experimental site were continuously measured using the eddy covariance method [10]. The flux tower has a height of 6 m and it is situated at the central part of selected palsa mire.  
The eddy covariance system was mounted on the top of the tower and included enclosed CO\({}_{2}\)/H\({}_{2}\)O gas analyzer (LI-7200A, LI-COR, Lincoln, NE, USA), open-path CH\({}_{4}\) analyzer (LI-7700, LI-COR, Lincoln, NE, USA), and 3D ultrasonic anemometer USA-1 (Metek, Elmshorn, Germany). Meteorological equipment includes devices for continuous measurements of net radiation (CNR4, Kipp & Zonen, Delft, The Nederlands), photosynthetically active radiation (LI-190 SA, LI-COR, Lincoln, NE, USA), air temperature and humidity (HMP155, Vaisala, Finland), precipitation (TR-525M, Texas Electronics, Dallas, TX, USA) and soil temperature and humidity (Stevens Hydra Probe II, LI-COR, Lincoln, NE, USA). The eddy covariance data were collected using analyzer interface unit (LI-7550, LI-COR, Lincoln, NE, USA) with frequency of 10 Hz.  
Figure 1: Geographical location of the study area.  
Data post-processing was conducted according to existed recommendations for data analysis [10]. The CO\({}_{2}\) and CH\({}_{4}\) fluxes were calculated from the raw data at 30 min time intervals using the EddyPro data processing software (LI-COR Lincoln, NE, USA), which implemented all required statistical tests and corrections (de-spiking, rotating coordinates, correcting for time delays, de-trending, correcting for frequency response and density, etc.). After data post-processing all fluxes containing the spikes, associated with, e.g., rain and dew events, as well as the fluxes measured under weak turbulence and low wind, were removed from the data sets.  
Intensive field measurement campaigns were conducted from the late winter (early May) to fall (early October) since 2016 (from snow to snow). For data analysis the periods from April to September 2017 and from July to October 2018 were selected. These periods were selected because of the minimal number of gaps in the measured data series.
## 4 Results and Discussion
### Performance Analysis of HDS
#### 4.1.2 Regeneration Temperature  
Figure 6a shows the relationship between regeneration temperature and \(SMER\). When the regeneration temperature increases, the \(SMER\) decreases. Figure 6b shows the effects of regeneration temperature on \(D_{c}\) and \(W_{tot}\). When the regeneration temperature increases, the \(D_{c}\) increases, but the rate slows down and \(W_{tot}\) increases roughly linearly. It could be due to the increment of desiccant wheel temperature, which hinders the mass transfer between air and desiccant. As a result, \(D_{c}\) increased at a slowed rate. HDS can also improve  
Figure 4: The effects of evaporation temperature on performance. (**a**) Specific moisture extraction rate (\(SMER\)). (**b**) \(D_{c}\) and \(W_{tot}\). (**c**) \(SMER\). (**d**) \(D_{c}\) and \(W_{tot}\).  
\(D_{c}\) by increasing the regeneration temperature, but the range of adjustment is limited due to the maximum regeneration temperature generally below 140 \({}^{\circ}\)C and comes at the cost of high \(W_{tot}\). This is also the problem exposed by the DWDS of the suspension bridge.  
Based on the above analysis, HDS should primarily regulate the evaporation temperature and regeneration temperature secondarily to adapt for the suspension bridge's corrosion protection in various cases.
## 2 Description of the System  
The combined cycle power plant system with a storage system consists of the water/steam paths, gas turbine section, and thermal storage system (TSS). The schematic representation of the combined cycle power plant with the thermal storage system (TSS) can be seen in Figure 1. In this work, the TSS is only implemented with hot and cold tanks connected via heat exchangers. Two working media (molten salt and heat transfer fluid (HTF)) are used in the TSS. Each of the two salt tanks has a diameter of 38.5 m and a height of 14 m. The total amount of molten salt used is about 28,500 t. The TSS and the process of charging and discharging are explained in more detail in the following sections.
## 3 Design and Analysis of the Sliding-Mode Controller
### Transversality Analysis  
The transversality condition evaluates the controller's capability to modify the power system's behavior. Such an evaluation is focused on verifying the presence of the control variable \(u\) in the derivative of the switching function, which defines the trajectory of the SMC. The formalization of this test is the following:  
\[\frac{d}{du}\left(\frac{d\Psi}{dt}\right)\
eq 0 \tag{26}\]  
Therefore, the time derivative of the switching function (24) is calculated:  
\[\frac{d\Psi}{dt}=(k_{c}-1)\cdot\left[\frac{(v_{C1}+v_{C2})\cdot u -v_{b}}{L_{2}}\right]-\left[\frac{v_{C2}\cdot u-v_{C1}\cdot\overline{u}}{L_{1 }}\right]...\] \[+\frac{di_{R}}{dt}-k_{v}\cdot\left[\frac{-(i_{L1}+i_{L2})\cdot u }{C_{2}}\right] \tag{27}\]  
Figure 3: \(C_{2}\) voltage and current ripple waveforms.  
Then, evaluating the transversality value using the previous derivative leads to the following equation:  
\[\frac{d}{du}\bigg{(}\frac{d\Psi}{dt}\bigg{)}=(v_{\text{C1}}+v_{\text{C2}})\cdot \bigg{(}\frac{2\cdot d-1}{1-d}\cdot\frac{1}{L_{2}}-\frac{1}{L_{1}}\bigg{)}+k_{ \text{\tiny{0}}}\cdot\frac{i_{\text{L1}}+i_{\text{L2}}}{C_{2}} \tag{28}\]  
Finally, the design of the parameters of both the Zeta converter and SMC must fulfill the transversality condition (26) using Equation (28); this evaluation is presented in Section 4.3.
## 2 Materials and Methods  
### Satellite Data  
For this study, Meteosat Second Generation (MSG) SEVIRI satellite data from 2014 to 2017, which has a temporal resolution of 15 min and a nominal spatial resolution of 3 km at sub satellite point, were used. This data was processed with the state-of-the-art hybrid fog retrieval algorithm outlined in [23] to obtain information on cloud base altitudes (CBA). The algorithm uses a random forest machine learning model trained with cloud base altitudes measured by synoptic weather observation data (SYNOP) and Meteorological Aviation Routine Weather Reports (METAR) stations and the pixel information from the satellite data corresponding to these station locations to predict cloud base altitudes. The general procedure is to train a model for every time slot of the MSG data with station data corresponding to that time where clouds were detected and then predict CBA for the whole MSG scene (see Figure 1 for example scene). This product has been validated extensively using a leave-location-out cross-validation scheme and shows overall good performance with a probability of detection (POD) of 0.61 and a Heidke Skill Score (HSS) of 0.58 [23]. The CBA data for 2006 to 2017 with a temporal resolution of 15 min is available upon request.
## 2 Materials and Methods
### Neural Network Architecture  
MLP was implemented as a DNN model for a prediction of the scintillation position within the monolithic crystal (Figure 1). To select appropriate hidden layers/units, we varied the number manually and determined the best combination. Lastly, 4 hidden layers and 256 hidden units were carefully chosen based on the minimum position resolution. In the training phase, data of approximately \(5\times 10^{6}\) scintillation points--including the number of SiPM pixels (from 1 to 144) activated by scintillation photons, the weight of each pixel (the total number of optical photons absorbed by a certain pixel) and the accurate 3D position of the scintillation point within the crystal's volume--were determined through MC simulations as reference. The DNN model was trained to synthesize the 3D position of the scintillation event from the map of optical photon distribution. For this work, a rectified linear unit (ReLU) was used as an activation function and the 144 SiPM's pixel weights were converted into 4 hidden layers and 256 units. The number of epochs was set to 150 and [PERSON]'s optimizer, with a weight decay of \(10^{-4}\), was implemented for regularization and optimization purposes.  
To obtain the 3D position (X, Y, Z)estimated, the final hidden layer is transformed into 3 outputs. For the testing phase, \(10^{6}\) new interaction data (unseen by the model) were fed into the model. The mean square error (MSE) was considered as a loss function. Both training and testing were carried out on a graphics processing unit (NVIDIA Quadra K5200 with 8 GB of memory).
## 3 Results
### Acceptability, Usability, and User Experience of TMT-VR  
The overall acceptability of the TMT-VR was high, with participants scoring a mean of 49.34 (SD = 7.02) out of a possible 60 on the Acceptability Questionnaire (see Table 1). This suggests that the majority of participants were satisfied with the neuropsychological assessment of cognitive functions in Virtual Reality. This high level of acceptability indicates that participants found the TMT-VR to be a satisfactory method for cognitive assessment and would be comfortable using similar tools in the future.  
Usability was evaluated using the System Usability Scale (SUS), where participants provided a mean score of 39.48 (SD = 4.79) out of 50 (see Table 1). This indicates a generally positive perception of the system's usability. According to standard interpretations of SUS scores, this result suggests that the TMT-VR is considered to have good usability, allowing users to effectively interact with the virtual environment without significant difficulty. The User Experience Questionnaire-Short Version (UEQ-S) revealed that participants had a positive overall user experience, with a mean score of 46.76 (SD = 5.98) out of 56 (see Table 1). The majority of participants found the VR system to be efficient and enjoyable, suggesting that the TMT-VR provided a user-friendly and engaging platform for cognitive assessment.  
The participants in this study provided generally favorable ratings for the acceptability, usability, and user experience of the VR setup used for the TMT-VR tasks. These findings suggest that the TMT-VR setup not only meets the technical requirements for usability but also aligns well with participant expectations in terms of comfort and effectiveness, thereby supporting its use in future cognitive assessment research.
## 1 Introduction
However, despite numerous efforts made in the field of modelling and simulation under these paradigms, most of them mainly focused on production and maintenance analysis with discrete event simulations; there is a lack of research oriented towards the development of frameworks for the management of geometric variations, especially in modelling and simulating the effects of geometric variations for quality assurance, productivity, and their interrelations. To overcome this limitation, this work proposes the development of a multi-domain and multi-scale modelling and simulation prototype that allows visualizing and evaluating the functionalities and operation modes of multistage assembly systems (MAS), both in terms of geometric quality and productivity. The demonstrator is implemented using Modelica language, an object-oriented language for modelling and simulation of complex systems. The analysis of the logistic flow is based on Discrete Event System Specification (DEVS) formalism, while the geometric quality analysis is conducted under the engineering-driven method defined by the SoV model. The paper is organized as follows: Section 2 presents the background of the study with the description of the SoV modelling approach for geometric variation propagation and the methodology followed to develop and validate the proposed meta-model of simulation for MAS. Next, Section 3 describes the adopted MAS type for this work, a brief SysML description of the proposed meta-model for the MAS simulation system, and its subsequent implementation in OpenModelica. In order to validate the proposed meta-model, Section 4 presents a case study of a MAS composed of four stages, and different scenarios are analyzed in order to compare potential control logics to improve productivity and quality. Finally, a discussion about the results is provided in Sections 5 and 6 concludes the paper.
## 3 Traffic Management Model
### Infrastructure Unit  
The infrastructure unit is located on the application level, and the routing path can be robustly calculated using the vehicle position and speed via DNNs (DNN architecture with optimization for BA in Figure 2). Figure 2 specifies the workflow of the infrastructure unit.
## 2 Analysis of Two-Step Method
### Analysis of Convergence Rate
The case \(A_{1}<0\) leads to the analysis of function \(\varphi_{2}(\lambda)=\frac{1}{2}(\sqrt{A_{1}^{2}(\lambda)-4A_{2}(\lambda)}-A_ {1}(\lambda))\) on the interval, defined by inequality (see case \(D=0\))  
\[\lambda>\frac{1+\beta_{1}}{h(1+\beta_{2})}. \tag{18}\]  
The first derivative of \(\varphi_{2}(\lambda)\) is written as  
\[\varphi_{2}^{\prime}(\lambda)=\frac{1}{2}\left(\frac{A_{1}^{\prime}(\lambda)A _{1}(\lambda)-2A_{2}^{\prime}(\lambda)}{\sqrt{A_{1}^{2}(\lambda)-4A_{2}( \lambda)}}-A_{1}^{\prime}(\lambda)\right).\]  
According to \(-A_{1}^{\prime}=h(1+\beta_{2})>0\), we obtain that if \(A_{1}^{\prime}A_{1}-2A_{2}^{\prime}>0\) (this takes place when \(\lambda>\eta(\beta_{1},\beta_{2})\)), this derivative is strictly positive. According to (17), it is valid for the interval defined by (18), so \(\varphi_{2}(\lambda)\) and, as a consequence, function \(r\) increases in the case of \(A_{1}<0\) corresponding to (18) and its maximum takes place in the right boundary point \(\lambda=L\), if intervals \([l,L]\) and (18) have an intersection. Thus, for all values of \(D\), we can see that \(r\) reaches its maximum value at the boundaries of interval \([l,L]\). **Notation 1**. : _Formulated theorems for the case of function (5) provide the conditions that guarantee global convergence [7]:_  
\[||x^{k}-x^{*}||\leq(\rho+\varepsilon)^{k}||x^{0}-x^{*}||,\quad\forall\varepsilon \in(0,1-\rho),\quad\forall k\leq 0,\]  
_where \(\rho=\max(r(s,l),r(s,L))\)._  
_If the non-quadratic \(f(x)\in\mathcal{F}_{LL}^{2,1}\) is considered, then these conditions provide a local convergence (see Theorem 1 from subsection 2.1.2 in [7]). Any sufficiently smooth function \(f(x)\) in the neighborhood of \(x^{*}\) can be presented as_  
\[f(x)\approx f(x^{*})+\frac{1}{2}(\
abla^{2}f(x^{*})(x-x^{*}),x-x^{*}),\]  
_and according to the following property:_  
\[f(x^{k})-f(x^{*})\leq\frac{L}{2}||x^{k}-x^{*}||^{2},\]  
_we can see that if \(\exists\;\delta>0\), \(||x^{0}-x^{*}||\leq\delta\), then for method (4) the following inequality is obtained \(\forall k\geq 0\):_  
\[f(x^{k})-f(x^{*})\leq\frac{L}{2}\delta^{2}(\rho+\varepsilon)^{2k},\quad \forall\varepsilon\in(0,1-\rho).\]  
**Notation 2**. : _Theorem 2 provides an approach to obtain optimal parameters with the solution of the following problem for obtaining an optimal convergence rate:_  
\[\rho_{opt}=\min_{s\in\Sigma\subset\mathbb{R}^{3}}\max(r(s,l),r(s,L)), \tag{19}\]  
_where \(\Sigma\) is defined as:_  
\[(1+\beta_{2})^{2}t^{2}-2((1+\beta_{1})(1+\beta_{2})-2\beta_{2})t+(1-\beta_{1} )^{2}\geq 0.\]0_Similar minimax problems arise in the theory of the standard HBM (3) [7] and multiparametric method in [18]._
## 4 Experimental Validation  
Two experiments were carried out to validate the mathematical models. The first experiment focused on the validation of the kinematic model, while the second experiment aimed at validating the dynamic model. Both experiments followed a similar setup, as shown in Figure 6. Two types of cameras were used in the experiments. The Olympus SP-320 camera was used in the experiments for kinematic validation, while a Sony PlayStation Eye camera (30 frames per second) was used for the experimental validation of the dynamic model. Both cameras were used in the same two positions for data recording.  
In the first position, the camera was positioned in front of a tripod to record the movements of the second joint (\(\theta_{2}\)), the third joint (\(\theta_{3}\)), and the end tip position in the X-Z plane. The second position of the camera was from the top to capture the movement of the first joint (\(\theta_{1}\)) and the end-tip position in the X-Y plane.  
Figure 4: The 3D workspace.  
Figure 5: Extracted slice from 3D workspace at x = 0 plane.
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 3 SPH Formulation  
The partial differential equations (PDEs) outlined in Equations (1), (2), and (4) need to be discretized in space and time. The spatial discretization is performed by applying an stabilized SPH scheme. [PERSON] et al. [27] provide a good overview of the SPH formulations and its derivation details. The time discretization is then carried out using an explicit second-order leapfrog stepping. [PERSON] [28] provides the implementation procedure of this time integration scheme for SPH models.  
Using a modified SPH approach expressed in [29; 30], the mass, momentum, and heat equations for particle \(i\) can be written in the following discrete forms:  
\[\left\langle\frac{d\rho}{dt}\right\rangle_{i} \approx-\rho_{i}\sum_{j}(\underline{v}_{j}-\underline{v}_{i})\cdot \
abla W_{ij}\,V_{j} \tag{10}\] \[\left\langle\frac{d\underline{v}_{j}}{dt}\right\rangle_{i} \approx\sum_{j}\left(\frac{\underline{\sigma}_{i}}{\rho_{i}^{2}}+ \frac{\underline{\sigma}_{i}}{\rho_{j}^{2}}+\underbrace{\Pi_{ij}\underline{ \underline{\underline{\underline{\underline{\underline{\underline{\Delta}}}}}}} }_{\text{stabilizers}}\right)\cdot\
abla W_{ij}\,m_{j}+\frac{1}{m_{i}} \underline{b}_{i}\] (11) \[\left\langle\frac{dT}{dt}\right\rangle_{i} \approx\frac{k}{\rho\,c_{p}}\sum_{j}2\left[\left(\frac{T_{i}-T_{j} }{|r_{ij}|}\right)\underline{\epsilon}_{ij}\cdot\
abla W_{ij}\right]V_{j}+ \left(\frac{Q^{\text{plast}}+Q^{\text{fric}}}{\rho\,c_{p}}\right)_{i} \tag{12}\]  
where \(V_{j}=m_{j}/\rho_{j}\) is the integration volume of particle \(j\) and \(W_{ij}=W_{h}(\underline{r}_{i}-\underline{r}_{i},h)\) is a kernel function with the smoothing length \(h\), chosen to be the cubic B-spline function [31] in this work. Furthermore, \(\Pi\) and \(\underline{\underline{\underline{\Delta}}}\) are the artificial viscosity term [32] and the artificial stress tensor [33] with the parameters taken similarly as in [17; 19]. For the heat equation expressed in Equation (12), the method suggested by [PERSON] [30] is considered to discretize the Laplacian operator, where \(\underline{\epsilon}_{ij}=\underline{r}_{ij}/|r_{ij}|\) is a unit vector in the inter-particle direction. After computing these equations for each particle, the particles position \(\underline{r}\) is updated with a smoothed velocity instead of the actual velocity. This smoothing scheme was introduced by [PERSON] [34] and is called the X-SPH correction. The same X-SPH parameter as [17; 19] is used for the numerical simulations of this work.
## 2 Materials and Methods
### Solvent and Micellar LCE of Grapevine Buds  
Prior to performing the LCE process of grapevine buds, the solvent and micellar extraction media were prepared. A 4 wt. % aqueous solution of the polyglycerol-4 laurate/sebacate and polyglycerol-6 caprylate/caprate surfactant mixture (compound borrowed from a final facial serum composition) was used as the micellar-extraction medium. For the solvent-extraction medium, an aqueous solution of 1,3-propanediol at the concentration of 4 wt. % was utilized.  
Harvested, deep-frozen grapevine buds were ground in a laboratory knife mill (Cutter Mixer R5 Plus, Robot Coupe, Palinges, France) with the addition of dry ice.  
A total of 15 g of ground grapevine buds were dispersed in 150 g of the micellar-extraction medium and automatically shaken for 3 h at room temperature, at 2500 rpm (BenchMixer XL multi-tube vortexer, Benchmark Scientific, Sayreville, NJ, USA), yielding the micellar extract (Surf E). Similarly, the solvent extract (SolvE) was obtained by dispersing 15 g of ground grapevine buds in 150 g of a solvent-extraction medium. Table 1 displays the formulation used to prepare the micellar and solvent extracts.  
The prepared solvent and micellar extracts were vacuum filtered (Vacuum Pump V-700, Buchi, Flawil, Switzerland). A non-woven polyester (ULESTER 32S) and polyamide (UHELON 53S) filters (Silk and Progress, Br\(\ddot{\text{n}}\)n\(\ddot{\text{e}}\)ec, Czech Republic) were used. The filtrates underwent centrifugation for 10 min at 7500 rpm (Universal 320R centrifuge, Andreas Hettich GmBH, and Co, Tuttlingen, Germany). Then, a 0.5% mixture of benzyl alcohol, benzoic acid, dehydroacetic acid, andtocopherol was added to the resulting solutions as a preservative. The solvent and micellar extracts of grapevine buds prepared this way were used directly in the following studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ingredient (INCI Name)** & **SurfE** & **SolvE** \\  & **[wt. \%]** & **[wt. \%]** \\ \hline
1 & polyglycerol-4 laurate/sebacate (and) polyglycerol-6 caprylate/caprate & 4 & - \\ \hline  & 1,3-propanediol & - & 4 \\ \hline
2 & benzyl alcohol, benzoic acid, dehydroacetic acid,tocopherol & 0.5 & 0.5 \\ \hline
3 & aqua & 85.5 & 85.5 \\ \hline
4 & grapevine buds & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ingredients and their concentrations used to prepare the micellar and solvent extracts.