esa-sceva/satcom-chunk-collection huggingface dataset:
# Unique Triboelectric Nanogenerator Using Carbon Nanotube Composite Papers
## 2 Materials and Methods  
### Fabrication of CNT Composite Paper  
The purpose of this work is to explore the conditions for the use of CNT composite papers as TENGs and to evaluate the performance of these papers in TENGs, rather than to develop a fabrication method. The procedure for the fabrication of the CNT composite paper is based on the traditional Japanese method of _washi_ paper making [24,27]. The method of preparing CNT composite papers adopted in this study, based on the method reported in Ref. [27], is described below.  
(1) Mix the CNTs and dispersant in pure water.  
2. Prepare the CNT dispersion by dispersing the mixture obtained in (1) with an ultrasonic homogenizer for 1 h. 3. Mix pulp material in pure water. 4. Prepare a pulp (raw material of paper, derived from eucalyptus) dispersion by stirring the mixture obtained in (3) with an agitator for 1 h. 5. Mix and stir the CNT dispersion prepared in (2) with the pulp dispersion prepared in (4) to prepare a mixture. 6. Pour the mixture prepared in (5) into the paper-making apparatus to dehydate it. 7. Remove the dehydrated raw ingredients prepared in (6) from the apparatus and subject them to heat pressing to enable drying and forming, resulting in the CNT composite paper. A schematic of the fabrication method is shown in Figure 1. Figure 2 shows the fabricated CNT composite paper obtained in this study. The evaluation of CNT composite papers through SEM, Raman scattering measurements, etc. has been performed in previous studies [24; 26; 27]. As the fabrication method is the same in this study, it is assumed that the physical properties are similar.  
Figure 1: Schematic of the CNT composite paper-making method: (**a**) preparation of mixture, (**b**) dehydration, and (**c**) heat pressing. (**d**) Completed samples (color differences were caused by the number of CNTs that they contained) (from Ref. [27] under License CC BY 4.0).  
Figure 2: Fabricated CNT composite paper (3 cm \(\times\) 3 cm).
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 4 Results  
### Experimental Results  
The XRF-scan intensities coupled with element fractions obtained in Core FXH-B2 and LGH2 are reported in Figures 2 and 3, respectively. The changes in XRF-scan intensities show higher variance than those for the conventional-fractions except for the elements K, Ca, Ti and Fe (Figure 2). Furthermore, all the XRF-scan intensities show low deviations relative to the conventional-fractions in the lower part of the sediment cores. However, elements Al and Si exhibit large deviations in the upper part of both cores (0-92 cm in core FXH-B2 and 0-744 cm in core LGH2), in which the water content was shown to be relatively high compared to below (Figures 2 and 3). The mean grain-size of core LGH2 varies from 3 to 33 \(\upmu\)m, with fine silt (mean: 10 \(\upmu\)m) in the top part (0-387 cm) and the bottom part (1361-1812 cm) of the core, and coarse clay (mean: 7 \(\upmu\)m) in the middle part of the core (388-1360 cm). The grain size of core LGH2 is typically well-sorted (ranging from 2.4 to 4.7; mean: 3.2), though sorting increases throughout the core from the bottom to the top.  
Figure 2: Downcore changes in XRF-scan intensities (gray and cyan lines) and conventional-fractions (black dots) of core FXH-B2. The black dash line indicates the depth position of 92 cm.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 4 Mechanical Reconfiguration  
In mechanical reconfiguration, the main radiator of the antenna can be reconfigured mechanically to provide different characteristics [54; 55]. In contrast to other reconfiguration techniques with the switches, this type of reconfigurable antenna does need active element integration, biasing systems or swathing mechanisms. However, the performance flexibility of this type of antenna is limited, and it is difficult to provide multi-function reconfigurable characteristics [56; 57; 58].  
In [59], a reconfigurable antenna is proposed that uses a liquid metal to mechanically reconfigure its performance. The configuration of the design is represented in Figure 7a. The main radiator of the design is a structurally embedded vascular antenna (SEVA) with a parallel-strip feed network and 50-ohm parallel-strip feed lines to provide a balun-like transition in the form of an antipodal dipole which creates physical space between the insertion points for the liquid metal. The S\({}_{11}\) performances of the design for different configurations and different values of the channel filling parameter (t: 0-2) are illustrated in Figure 7b. As can be observed, by changing the size of channel filling, the operation frequency and impedance bandwidth of the proposed mechanically reconfigurable antenna can be easily tuned for different frequencies.  
Another design of the mechanically reconfigurable antenna is introduced in [60]. Its structure consists of a circular rotating part with four different shapes which can be connected to the microstrip feeding line of the design to generate different sets of resonant frequencies. As can be seen from Figure 8a, with every rotation, different shapes (shape 1-shape 4) with different radiation patches can be structured. For different configurations from shape 1 to shape 4, the antenna is able to cover various operation frequency bands including 2.3-2.6, 2.6-3.4, 4-5, and 3-4 and 5.26-7 GHz. It should be noted that the antenna preserves the same radiation pattern with omnidirectional mode while performing a frequency reconfiguration. Various reconfigurable antennas with mechanical reconfiguration switches are introduced in [61; 62; 63; 64; 65].  
Figure 7: (**a**) Schematic and (**b**) measured/simulated results of the mechanically reconfigurable antenna (redrawn from [59]).
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 3 Methodology  
### Dataset  
The TUAB dataset is divided into two folders: training sessions and test sessions, containing a total of 2993 EEG recordings collected at sampling frequencies of 250 Hz, 256 Hz, and 512 Hz. For this study, we excluded all recordings sampled at 256 Hz (189 files) and 512 Hz (18 files), retaining only the 2786 recordings with a sampling frequency of 250 Hz. Integrating data with varying sampling rates would have required downsampling the 256 Hz and 512 Hz recordings to 250 Hz, a process that may introduce inconsistencies, particularly in time-frequency representations such as spectrograms and scalograms, which are highly sensitive to sampling rate differences. Such discrepancies can degrade feature quality and negatively impact model training and performance. To ensure consistency across the dataset and facilitate reliable feature extraction, only recordings originally sampled at 250 Hz were included in the analysis.  
Among these, 2518 recordings are from the training session folder and 268 from the test session folder. The training data were further split into a training set (80%, \(n=\) 2015) and a validation set (20%, \(n=\) 503). All 268 recordings in the test session folder were used as an independent test set to evaluate the performance of the proposed method. There is no overlap between the patients in the training and test sets. To avoid any potential data leakage, the test set was not used at any stage of model development or hyperparameter tuning. Furthermore, all preprocessing steps were performed using parameters computed solely from the training data, not from the full dataset.
## 2 Dataset and Overview of MOGA-SVM
### Overview of MOGA-SVM  
Figure 1 shows the flow chart of MOGA-SVM for organ inflammations classification. The typical waveform of the wrist pulse signal is shown in Figure 2, which is characterized by a percussion wave, tidal wave, dicrotic wave, peak systolic velocity, reverse velocity, peak diastolic velocity, and end diastolic velocity.  
The training of organ inflammations classifier, the datasets contain WPS of healthy, appendicitisis, acute appendicitisis, duodenitis, and pancreatitis are considered [10]. Each record of WPS is carried out DC drift elimination and low-pass filter following the approach, as in [11]. Afterwards, the local maxima and minima points of the WPS are located. The detail is not being discussed in this paper, as the authors would like to mainly focus on the proposed MOGA-SVM.  
Figure 1: Overview of multi-objective genetic algorithm based support vector machine (MOGA-SVM) for organ inflammations classification.  
Figure 2: Typical waveform of wrist pulse signal.  
The similarity coefficients of every pair of WPS are computed, which form the customized similarity kernel. After MOGA, the optimal kernel \(K_{CS}\) is designed. A classifier for organ inflammations classification is constructed. Section 3 discusses the details (Figure 3 is drawn to summarize the key steps of the MOGA-SVM). In this paper, the 10-fold cross-validation is adopted to evaluate the classifier, as it is a practical order in literature [27; 28].  
When it comes to practical application, the WPS of the candidate is measured and it served as the input of the trained organ inflammations classifier. The outputs maintain five possibilities, healthy, appendicitisis, acute appendicitisis, duodenitis, and pancreatitis. If the status is one of the four organ inflammations, a report will be sent to a nurse and doctor for further examination and treatment.
## 4 Mission
### Mission Data
#### 4.3.2 Collision Avoidance  
Figure 16a-e shows a complete collision avoidance process of our USV during the mission. As shown in Figure 16a, the USV was sailing normally according to the planned path, and a large rescue vessel was coming from the right side of the USV. As shown in Figure 15b-d, when the USV detected the vessel, the collision avoidance process started, the USV began to change the path according to the collision avoidance parameters, steered to the right, and detoured from the rear side of the vessel until it successfully and safely bypassed the vessel, and then returned to the planned path. Figure 16e shows the change of the path during the whole collision avoidance process.  
Then, we analyzed the changes of the USV's heading angle and lateral offset during collision avoidance process. As can be seen from Figure 17a,b, during the initial stage, the USV was sailing along the path with an angular direction of 70.219\({}^{\circ}\), while the lateral offset was controlled within 3 m. Starting from 55 s, the USV detected the obstacle and then turned to the right. At this time, the lateral offset started to decrease (the lateral offset was positive on the left side of the path), and, at most, the lateral offset was about \(-\)20 m. After completely avoiding the obstacle, the USV began to quickly adjust its path, turn left, and return to the planned path. So the lateral offset gradually increased until it returned to near 0, indicating that the USV returned to the planned path. As shown in Figure 17c, during the whole collision avoidance process, the nearest distance between the USV and the obstacle was 16 m, which is sufficient to ensure navigation safety.  
Figure 15: Path following error of the USV: (**a**) Path of the USV, (**b**) changes of the path following error during the mission.
## 5 Simulation-to-Reality Transfer
Although action, reward and transition spaces are often similar in simulation and reality, state spaces show more pronounced differences [232]. This is mainly due to the tools for perception and feature extraction employed [233]. For this reason, this technique can be found in many applications as state adaptation. Among the most popular domain adaptation methods, the discrepancy-based [234], adversarial-based [235] and reconstruction-based approaches [236] are noteworthy. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Domain** & **Domain** & **System** \\  & **Randomisation** & **Adaptation** & **Identification** \\ \hline Deep Reinforcement Learning for Robotic Control in High-Dexterity & & & \\ Assembly Tasks—A Reward Curriculum Approach [103] & & & \\ \hline Towards Real-World Force-Sensitive Robotic Assembly through Deep & & & \\ Reinforcement Learning in Simulations [154] & & & \\ \hline Variable Compliance Control for Robotic Peg-in-Hole Assembly: A & & & \\ Deep-Reinforcement-Learning Approach [104] & & & \\ \hline A flexible manufacturing assembly system with deep & & & \\ reinforcement learning [158] & & & \\ \hline A digital twin-based sim-to-real transfer for deep reinforcement & & & \\ learning-enabled industrial robot grasping [159] & & & \\ \hline A digital twin to train deep reinforcement learning agent for smart & & & \\ manufacturing plants: Environment, interfaces and intelligence [69] & & & \\ \hline Learning to Centralise Dual-Arm Assembly [237] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sim-to-real transfer methods used in DRL applications in manufacturing.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions
#### 4.4.5 Impact on Model Accuracy  
Recent research by [PERSON] et al. [54] has quantified the impact of distribution selection on model accuracy. Their comparative analysis of Emergency Department (ED) patient flow models revealed that selecting an appropriate distribution reduces prediction errors by 23%. Moreover, combining multiple distributional approaches enhances model accuracy by 31%, while empirical distribution fitting outperforms theoretical distributions in modeling complex processes.
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 2 RC Slab and Vibration Testing
### Variation of MAC  
In damage detection, modal assurance criterion (MAC), which ranges from 0 to 1, is often used in automatically pairing and comparing the analytical and experimental mode shapes, here 1 and 0 mean a perfect correlation and no correlation, respectively.  
In the paper, MAC is defined as [36]:  
\[MAC(\varphi_{i}^{(k)},\overline{\varphi}_{i})=\frac{\left|\varphi_{i}^{(k)}T \overline{\varphi}_{i}\right|^{2}}{(\varphi_{i}^{(k)}T\varphi_{i}^{(k)})( \overline{\varphi}_{i}^{T}\overline{\varphi}_{i})} \tag{1}\]  
where \(\varphi_{i}^{(k)}\) is the experimental mode shape at the \(k\)-th vibration testing and \(\overline{\varphi}_{i}\) is the analytical mode shape, which are adopted as a baseline mode shape.  
The MAC values of the first four modes with respect to temperature are shown in Figure 10, whose variation ranges are (0.985, 1), (0.985, 1), (0.974, 1) and (0.975, 1), respectively. The obtained results show that all the MAC values are bigger than 0.97 and very close to unity, which implies that there is no clear correlation between MAC value and temperature, and the mode shape is less affected by environmental factors. Because there is a lower test precision for the high-mode modal shape than  
Figure 9: Damping ratio vs. temperature. (**a**) 1 st mode. (**b**) 2 nd mode. (**c**) 3 rd mode. (**d**) 4 th mode.  
the low-step modal shape, the discretization degree of the MAC value is higher than the low-mode modal shape.
# Study About the Performance of Ascon in Arduino Devices
## 2 The Ascon Cipher Suite
### The Ascon Permutation
#### 2.4.1 Addition of Constants Layer, \(p_{C}\)  
The first step of the permutation \(p\) is the addition of constants layer, \(p_{C}\), which is based on adding a round constant to the register word \(x_{2}\) of the state \(S\) in round \(i\). Both index \(i\) and \(r\) are initialized to 0 and there are differences in the value of \(r\) depending on whether \(p^{a}\) or \(p^{b}\) is being applied, i.e., the value of \(r=i\) for \(p^{a}\) and \(r=i+a-b\) for \(p^{b}\). As a result of this process, the 64-bit \(x_{2}\) register word would be \(x_{2}\gets x_{2}\oplus c_{r}\), where \(c_{r}\) is a constant.  
Figure 1: The three permutation layers: \(p_{C}\), \(p_{S}\), and \(p_{L}\).  
Table 3 shows the constant \(c_{r}\) (in hexadecimal) used in each round \(i\), distinguishing between the permutations \(p^{a}\) and \(p^{b}\). In particular, \(p^{12}\) corresponds to \(p^{a}\) while \(p^{b}\) and \(p^{8}\) correspond to \(p^{b}\), in Ascon-128 and Ascon-128a, respectively.  
The round constants have been chosen in such a way that the size is large enough to avoid sliding, rotation, self-similarity, or similar attacks. In addition, the values of the constants have been chosen (one increasing and one decreasing counter for the two halves of the affected byte) so that it is easy to calculate them by a simple counter and inverse operation. Finally, the constants are added to the third word \(x_{2}\) because it allows pipelining with the new or previous operations.
# Verification Methodology for Simulation Models of the Synchronous Generator on Transients Analysis
## 1 Introduction
The aim of this research was to determine deviations in the values of electrical parameters when using the dynamic simulation model, according to accurate calculations with the factory values of the synchronous generator. Comparing the simulation results obtained by these methods for a sudden three-phase short-circuit, a fine adjustment of the dynamic model parameters could be made to reduce these deviations. After such calibration of the dynamic model during the analysis of a particular event, the model can be used to analyze other disturbances in the network, as well as to analyze transients in other parts of the network in case of failure in the synchronous generator whose model is calibrated. By analyzing the impact of interference in one part of the power system on other network elements, an appropriate selection of protective devices can be carried out, and the existing elements can be dimensioned to reduce the adverse impact of interference on their operation. Thereupon, the possibilities of applying different simulation models of the power network for transients analysis could be verified by the methodology presented here, which is the main contribution of this paper. Furthermore, the time dependence of the generator currents and voltages is presented, as well as the obtained results for rotational speeds, excitation voltages, and mechanical powers. This paper is organized as follows: after the main parameters of the analyzed generator are presented, the calculation of the sudden three-phase short-circuit using the FEM is described; then, a description of a system of differential equations is used to calculate the transients in a dynamic model; this algorithm is built into the MATLAB software and used in the dynamic simulation model presented in this paper; a model of the network used for the simulation in the MATLAB software is presented in the next section; an overview of the obtained results related to current densities and magnetic field at characteristic moments of the transition is then provided, as well as the results obtained via a simulation model; a comparison of the calculation results of both methods is provided; and finally, the most important conclusions are summarized.
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
## 2 Materials and Methods
### Constructing Guidance Blades
It should be noted that the only difference between Case III and Case II is that, in Case III, blades that are within \(0.2\,R\) from the pipe centre are removed, thus removing the axial flow restriction in the middle. To conclude, the mesh morphing process can be specified as Algorithm 1 which changes the coordinates of each point (vertex) in the mesh, in order to obtain the desired twist and deviation angles. As an example for Case I, the process is as follows:  
```
Require:\(\theta(z,r)=\theta_{0}\left(1-\cos\left(\frac{\pi z}{2}\right)\right)\) for all\(x\),\(y\),\(z\) coordinates do \(r\leftarrow\sqrt{x^{2}+y^{2}}\) if\(z>6\)then \(\theta_{t}\gets 2\theta(1,r)\) elseif\(z>5\)then \(\theta_{t}\gets 2\theta(1,r)-\theta(6-z,r)\) elseif\(z>4\)then \(\theta_{t}\leftarrow\theta(z-4,r)\) else \(\theta_{t}\leftarrow\theta(0,r)\) endif \(x_{\text{new}}\gets x\cos(\theta_{t})+y\sin(\theta_{t})\) \(y_{\text{new}}\leftarrow-x\sin(\theta_{t})+y\cos(\theta_{t})\) \(z_{\text{new}}\gets z\) endfor
```  
**Algorithm 1**Mesh morphing process. Figure 7: Case II, with four blades and twist angle dependent on radius. (**upper left**) twist angle as a function of axial location for various deviation angles; (**lower left**) deviation angle as a function of parameter \(a\); (**right**) the blade set up in the pipe. Note that the arrow shows the direction of the flow in front of the inlet. Note that the twisting continues after the blade exit point in order to get a smooth transition from the twisted part to the straight part, hence the definition \(\theta_{t}=2\theta(1,r)\) in the algorithm. This is also seen in Figure 3 where \(\theta\) covers only the part where the swirl device is present.
## 3 Analysis and Discussion
### Characteristics of Changes in Dissolution Amount per Unit Area and Dissolution Rate  
As demonstrated in Figure 4, the dissolution amount per unit area and dissolution rate both exhibit a positive correlation with increasing acidity of phosphogypsum leachate, indicating enhanced carbonate rock dissolution under more acidic conditions. In static experiments, comparative analyses of specimens with varying fracture apertures at identical pH revealed a distinct inverse relationship between fracture aperture and dissolution intensity, following the order HY04 < HY05 < HY06. This phenomenon may be attributed to the preferential accumulation of flocculent reaction precipitates within narrow fractures and pores, which artificially inflates measured specimen mass through pore-clogging effects. Conversely, the dynamic experiments demonstrated that larger fracture apertures resulted in higher dissolution amounts per unit area and higher dissolution rates in the rock specimens.  
Comparative analysis of dissolution amounts per unit area between static dissolution and dynamic dissolution experimental results (Table 9) revealed that under identical pH conditions and fracture apertures, rock specimens subjected to 15-day dynamic dissolution exhibited 10- to 15-fold higher areal dissolution values compared to those from 30-day static experiments. This significant disparity quantitatively demonstrates that hydrodynamic conditions substantially enhance carbonate rock dissolution intensity relative to  
Figure 4: Dissolution amount per unit area and dissolution rate for each rock specimen. Static dissolution group (HY01\(-\)HY06): HY01\(-\)HY03 with different pH; HY04\(-\)HY06 with identical pH and different fracture apertures. Dynamic dissolution group (HY07\(-\)HY12): HY07\(-\)HY09 with different pH; HY10\(-\)HY12 with identical pH and different fracture apertures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **HY01** & **HY02** & **HY03** & **HY07** & **HY08** & **HY09** \\ \hline Surface oxygen content before experiment [\%] & 43.86 & 41.60 & 39.14 & 40.17 & 42.31 & 38.27 \\ Surface oxygen content after experiment [\%] & 76.09 & 76.18 & 72.46 & 68.14 & 70.33 & 67.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Surface oxygen content of each specimen before and after the experiment.  
stagnant systems, with advective solute transport mechanisms dominating the accelerated dissolution processes.