esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline
**lons** & **Control** & **PMF 1** & **PMF 2** \\ \hline  & **mg/100 g** & **mg/100 g** & **mg/100 g** \\ \hline Al & \(1.47\pm 0.66\) \({}^{\text{a}}\) & \(2.53\pm 1.12\) \({}^{\text{a}}\) & \(1.59\pm 0.54\) \({}^{\text{a}}\) \\ \hline Ca & \(626.33\pm 6.97\) \({}^{\text{b}}\) & \(802.58\pm 3.26\) \({}^{\text{a}}\) & \(658.50\pm 5.07\) \({}^{\text{b}}\) \\ \hline Cd & \(0.02\pm 0.01\) \({}^{\text{a}}\) & \(0.03\pm 0.01\) \({}^{\text{a}}\) & \(0.01\pm 0.01\) \({}^{\text{a}}\) \\ \hline Fe & \(4.77\pm 1.05\) \({}^{\text{b}}\) & \(7.25\pm 1.24\) \({}^{\text{a}}\) & \(4.41\pm 0.88\) \({}^{\text{b}}\) \\ \hline K & \(659.08\pm 4.93\) \({}^{\text{c}}\) & \(1001.25\pm 2.17\) \({}^{\text{a}}\) & \(846.83\pm 6.08\) \({}^{\text{b}}\) \\ \hline Mg & \(344.58\pm 3.46\) \({}^{\text{c}}\) & \(489.41\pm 1.60\) \({}^{\text{a}}\) & \(390.83\pm 2.74\) \({}^{\text{b}}\) \\ \hline Mn & \(1.61\pm 0.15\) \({}^{\text{c}}\) & \(2.37\pm 0.18\) \({}^{\text{a}}\) & \(2.00\pm 0.08\) \({}^{\text{b}}\) \\ \hline Mo & \(0.05\pm 0.01\) \({}^{\text{a}}\) & \(0.06\pm 0.01\) \({}^{\text{a}}\) & \(0.05\pm 0.01\) \({}^{\text{a}}\) \\ \hline Na & \(315.08\pm 3.59\) \({}^{\text{c}}\) & \(543.91\pm 2.02\) \({}^{\text{a}}\) & \(485.16\pm 2.67\) \({}^{\text{b}}\) \\ \hline Ni & \(\text{<}\)LOQ & \(\text{<}\)LOQ & \(\text{<}\)LOQ \\ \hline P & \(353.35\pm 15.48\) \({}^{\text{c}}\) & \(762.75\pm 1.28\) \({}^{\text{a}}\) & \(661.58\pm 3.02\) \({}^{\text{b}}\) \\ \hline Pb & \(\text{<}\)LOQ & \(\text{<}\)LOQ & \(\text{<}\)LOQ \\ \hline S & \(738.40\pm 5.62\) \({}^{\text{c}}\) & \(1137.48\pm 6.50\) \({}^{\text{a}}\) & \(914.48\pm 5.87\) \({}^{\text{b}}\) \\ \hline Se & \(\text{<}\)LOQ & \(0.15\pm 0.02\) \({}^{\text{a}}\) & \(0.11\pm 0.01\) \({}^{\text{b}}\) \\ \hline Zn & \(7.03\pm 0.06\) \({}^{\text{c}}\) & \(11.39\pm 0.03\) \({}^{\text{a}}\) & \(8.69\pm 0.04\) \({}^{\text{b}}\) \\ \hline \end{tabular} Mean values with \(\pm\)SD and statistical analysis using Tukey’s test at the significance level of \(\alpha=0.05\); the results indicated with the same letters do not demonstrate a statistically significant difference (test performed as part of a comparison of the concentrations of individual ions with each other after the application of slowly changing magnetic fields and with a control group); \(\text{<}\)LOQ—result below the Limit of Quantification.  
\end{table} 
Table 3: Changes in the content of basic micro and macro components and heavy metals under the influence of applied pulsed magnetic fields for radish sprouts. -radial sprouts. These results suggest the advisability of research into the use of a pulsed magnetic field and the matching of the magnetic field parameters to the subject of research.
## 3 Experimental Set-Up
### Experimental Matrix and Pre-Test Analysis
#### 3.1.1 Preliminary Fluid Dynamics and Thermal Simulation of the Test Mock-Up Analysis
Thus, using the two-channel model and the results of the steady-state simulation as initial condition, analyses of two transients were performed: one where the flow rate in the first channel was completely lost in 12 s (total LOFA), and a second case where the flow in the same channel was reduced to half of its nominal value in the same amount of time (partial LOFA). In the second channel the flow rate was maintained at its nominal value during the whole transient. The imposed time evolution of the flow rate in the LOFA-affected channel is shown in Figure 6a, while Figure 6b shows the resulting temperature increase (surface maximum temperature value). For the total LOFA, the surface temperature (Figure 6b) reached in the hottest spot at the end of the transient (12 s) is close to 550 \({}^{\circ}\)C, which is the current operating limit for the breeding blanket structure. While this does not necessary mean that the FW might fail at this point, it indicates that the risk of failure of the FW increases very rapidly. In case of partial LOFA (mass flow rate reduced to half of the reference value), the rise of the maximum temperature on the top wall surface was less significant, about 27 \({}^{\circ}\)C in 12 s, as compared to 90 \({}^{\circ}\)C in the case of total LOFA. To determine in a more reliable way the time to failure of the FW in case of a LOFA event, a more exhaustive analysis including mechanical  
Figure 5: Temperature, pressure, and velocity along the channel centerline starting from inlet. stress analysis and failure modes analysis needs to be performed, but these topics were outside of the scope of the present investigation. In any case, this analysis suggests that the maximum duration for which the flow is reduced to zero (total LOFA) during the experiment should not exceed 12 s to avoid surface temperatures over 550 \({}^{\circ}\)C. When comparing these results with those obtained for the DEMO blanket FW in [1], it can be seen that, for the present case, the temperature rise after 8 s was close to 60 \({}^{\circ}\)C as compared to 90 \({}^{\circ}\)C in the reference case. This can be explained by the cooling effect on the main flow of the 180\({}^{\circ}\) turn area where there is no surface heating. This also explains the apparent \"drop\" in the temperature profile in Figure 4, where the low temperature area, in the middle of the graph, corresponds to points outside the heated zone. Nevertheless, despite these deviations from the reference case, the simulated dynamic behavior of the mock-up is found to be representative for a first investigation of a DEMO FW LOFA scenario.
## 2 System Model and Problem Formulation
### System Model  
As shown in Figure 1, the full-duplex decoding and forwarding (DF) wireless power communication system consists of a source node S, a relay node R, and a destination node D. In the system, both the source node and the destination node are equipped with one antenna, and the relay node that performs the FD transmission is equipped with two independent antennas, including one antenna for transmission and another for reception. The channel gains from the source node S to the relay node R and R to the destination node D are \(\left|h_{i}\right|^{2}\) and \(\left|g\right|^{2}\), where \(h\) and \(g\) are the channel coefficients, respectively. All channels are distributed complex Gaussian channels with zero-mean and unit-variance, i.e., \(CN(0,1)\). It is assumed that there is no direct link between the source node and the destination node. All channel power gains follow the fixed block fading model. They remain constant during one block, but remain independent for a different block. It is assumed that the relay node is an energy-limit node, and energy acquisition is performed from the source node.  
The system uses a time division protocol in Figure 2. The process is divided into two phases, namely, the energy harvesting phase and the information transmission phase. In the first phase, the relay node performs energy acquisition from the source node. In the second phase, the relay node use decoder forwarding to forward signals received from the source node to the destination node.  
In order to make full use of FD hardware resources (antenna units) for energy harvesting, we use the time-switch protocol, as shown in Figure 2b, to switch the receiving and transmitting antennas to the energy harvesting circuit in the first stage.  
Figure 1: System model of wireless-powered FD relay systems.  
Figure 2: _Cont._
## 4 Proposed Algorithm
### Ebba  
Conventional BA may confront some key challenges in solving the feature selection problems. First, when dealing with the big solution space of feature selection problem, BA may lack exploration ability, which may make the algorithm fall in local optima. Second, the bats of the BA are guided by the best bat of the swarm, i.e., \(X^{*}\), which means that the population diversity is lower for the large scale datasets. Third, the exploration and exploration abilities of BA should be further balanced. Finally, BA is proposed for continuous problems whereas the feature selection problems are with a binary solution space. Thus, these reasons motivate us to enhance BA for better feature selection performance. The main steps of the proposed EBBA are detailed in Algorithm 1, and the correspondingly improved factors are as follows:  
```
1 Define the related parameters: population size \(N_{pop}\), bat dimension \(N_{dim}\), maximum iteration \(t_{max}\), and fitness function, etc.;
2 Initialize the bat population, pulse frequency, pulse rates and loudness;
3 for\(t=1\) to \(t_{max}\)do
4 Generate new bats by adjusting frequency, and updating velocities and locations by using Equations (5), (6) and (11);
5 if\(N_{rand}\)\(<\)\(r_{i}\)then
6 if\(N_{rand}\)\(<\)\(r_{i}\)then
7 Select the second best bat;
8
9 end if
10
11 Select the best bat;
12
13 end if
14 Generate a new bat around the selected bat by using Equation (8) or (12);
15
16 end if
17 if\(N_{rand}\)\(<\)\(A_{i}\) and \(f_{fit}(X_{i})\)\(<\)\(f_{fit}(X^{*}orX^{**})\)then
18 Accept the new bats and update \(r_{i}\) and \(A_{i}\) by using Equations (9) and (15);
19
20 end if
21 Rank the bats and find the current best \(X^{*}\);
22 Generate \(X^{new}\) by using Equation (12);
23 if\(f_{fit}(X^{*})\)\(>\)\(f_{fit}(X^{new})\)then
24\(X^{*}=X^{new}\);
25
26 end if
27
28 end for Return \(X^{*}\); //\(X^{*}\) is the final feature selection result of the EBBA
```  
**Algorithm 1** EBBA
## 4 Results of Kinetic Modeling
### Carbonation Kinetics Comparative Analysis
#### 4.2.2 Diffusion Control Region (Apparent Model)

Table 7: Carbonation reaction: Kinetic parameters. \(k_{\mathrm{s}}\) and \(D\) are averages over the temperature range 650, 700, and 750 \({}^{\circ}\)C. \(S\) is provided in as-synthesized state or after one cycle. greatly influence kinetics in the kinetic control region [28], outlying values have been attributed to diffusion limitations in the material [37]. [PERSON] postulated that lower activation energies are associated with microporous materials that are susceptible to pore plugging, which would limit diffusion of CO\({}_{2}\), and is also associated with low carbonation conversion [37]. A study by [PERSON] et al. determined a much higher diffusion \(E_{a}\) of 163 kJ mol\({}^{-1}\) for mesoporous limestone [17], which has been associated with resistance to pore plugging [39]. In the case of P-CaO, however, BET analysis showed that the material was largely nonporous, with the majority of pore volume in the macroporous region [24]. Nevertheless, the lower diffusion activation energy could be the result of incomplete carbonation conversion (see Table 6). Cycling experiments with P-CaO performed elsewhere [24] show good performance with cycling, which suggests that the reaction rates, although initially slower than other materials, will be retained with subsequent cycles. Future analysis could involve calculation of kinetic parameters after multiple isothermal cycles. Carrying out a kinetic analysis with another generalized method is also recommended, as the apparent method uses only a small number of data points to produce an Arrhenius equation.
# Evaluating Effects of Electric Vehicle Chargers on Residential Power Infrastructure
## 1 Introduction
Contributing factors include imbalanced load distribution, limited transformer capacity, and insufficient harmonic mitigation in legacy systems. Addressing these issues requires a comprehensive understanding of power behavior during active charging, along with practical engineering responses drawn from the literature. This research, therefore, extends previous work by offering real-world measurement data and targeted analysis to support more resilient electrical design and policy planning for multi-residential environments. This research contributes to the existing body of knowledge by focusing on the specific context of condominiums in a rapidly urbanizing city with unique infrastructure challenges. The key contributions of this study are as follows:  
1. A detailed examination of the load profiles generated by EV chargers in a condominium setting, including variations in power consumption and peak demand periods;
2. An evaluation of the effects of EV charging on the electrical system, particularly focusing on phase imbalance, current flow in the neutral wire, and potential heat buildup;
3. An investigation of the harmonic distortions caused by EV chargers and their impact on power quality and the stability of the electrical system;
4. A field-based evaluation of EV charging behavior and electrical impacts under real operating conditions in a multi-residential building. The remainder of this paper is structured as follows: In Section 2, titled Experimental Configuration and Measurement Setup, the research framework and basic data before analysis are specified. In Section 3, titled Load Profile Analysis of EV Chargers, the behavior that affects the electrical system is analyzed and summarized; in Section 4, the direct impacts on the electrical system are discussed; and, finally, in Section 5, the conclusions are provided, encapsulating the key findings and implications of this study. In conclusion, this study aims to provide a comprehensive understanding of the challenges and solutions associated with the integration of EV chargers in condominium environments. By emphasizing direct measurements and analyzing real-world power quality impacts, this study seeks to contribute to the development of safe, efficient, and sustainable residential EV charging infrastructure.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 2 Related Work
### State Space Model  
Defocus-blurred images often exhibit significant variations in blur intensity across different regions, necessitating robust global feature extraction capabilities to capture long-range dependencies and restore global consistency. However, global modeling typically incurs high computational complexity, requiring a careful balance between accuracy and efficiency. While Transformers have achieved remarkable success in global modeling tasks due to their self-attention mechanism, their quadratic computational complexity (\(\mathcal{O}(N^{2})\)) limits their applicability in high-resolution image processing, particularly in deblurring tasks, where image resolution is typically high.  
To address this limitation, the State Space Models (SSMs), represented by Mamba [13], have emerged as a promising alternative. SSMs exhibit linear computational complexity (\(\mathcal{O}(N)\)) when modeling global dependencies, making them more suitable for processing high-resolution images. SSMs have already demonstrated significant potential in various vision tasks [25; 26; 27; 28; 29].  
A State Space Model (SSM) describes a set of states and predicts the next state based on input data, governed by state and output equations. Early SSMs were designed for continuous data and faced challenges in parallel processing and memory capacity. However, advancements such as discretization, cyclic or convolutional representations, and HiPPO techniques [30] led to the development of the S4 model [31]. The S4 model supports parallel training and can model longer dependencies, but its fixed coefficient matrices limit its ability to adapt to varying input importance. Mamba, or the Selective State Space Model (S6), addresses this limitation by introducing selective information processing, enabling dynamic adjustment based on input relevance.  
Similar to how Transformers were adapted for vision tasks through Vision Transformer (VIT) [32], Mamba has been adapted for vision tasks through models like Vision Mamba (VIM) [33] and Visual State Space Model (V Mamba) [34]. These models divide images into patches, convert 2D images into 1D sequences, and employ bidirectional or cross-scan mechanisms to capture 2D structural information. The cross-scan mechanism, in particular, scans feature maps along four directions, ensuring each element integrates information from all positions without increasing computational complexity. This approach is especially beneficial for defocus deblurring, as it enhances the recovery of complex and irregular blur boundaries while reducing errors caused by insufficient local feature extraction.  
Therefore, we adopt Mamba's cross-scanning mechanism (Figure 2c) for global feature modeling instead of Transformer's self-attention mechanism (Figure 2b). Mamba's linear computational complexity (\(\mathcal{O}(N)\)) significantly reduces computational costs, making it more suitable for high-resolution defocus-blurred images and promoting its application in this field.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 6 Conclusions  
In the industry, the information of alarm signal in conjunction with the LQE class is relevant and represents a new approach to evaluate data's reliability. In this paper, we proposed a novel application of a deep learning MLP model for the estimation of the LQE metric for a WSN integrated by ANDON towers that sends wireless information about alarm signals or about the operational status of a workshop's machinery.  
The random dataset was gathered from a realistic experimental scenario and split into 80% for training, 10% for validation, and 10% for testing. In order to find the best learning rate parameter, we realized a methodical tuning process for the MLP model and noted that a learning rate of LR = 2.95 \(\times\) 10\({}^{-5}\) achieved an accuracy of 99.8% for validation data without overfitting the training set.  
The MLP model achieved an accuracy of 99.3% for test data and showed better performance than other machine learning models such as SVM, RF, KNN, and GB. Despite the MLP training time, its time to carry out estimations was in the order of ms, i.e., the same order as that of the other models. From our research and experimentations, we could see that despite its simplicity, the MLP is a suitable model for workshops in developing countries with limited computational resources.  
Conceptualization, T.A.C.-A.; methodology, T.A.C.-A.; software, T.A.C.-A.; validation, J.A.C.-C.; formal analysis, T.A.C.-A.; investigation, T.A.C.-A.; resources, A.T.-A.; data curation, T.A.C.-A. and A.T.-A.; writing--original draft preparation, T.A.C.-A.; writing--review and editing, T.A.C.-A. and J.A.C.-C.; visualization, T.A.C.-A.; supervision, J.A.C.-C.; project administration, T.A.C.-A.; funding acquisition, A.T.-A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by El Consejo Nacional de Ciencia y Tecnologia de Mexico CONACYT and CIATEQ under research project \"Modelo de confabilidad para una red de sensores inalambricos para su implementacion en la manufactura inteligente\".  
Not applicable.  
The dataset used for this research is publicly available at: [[https://ieee-dataport.org/documents/wireless-andon-tower-nrf24l01](https://ieee-dataport.org/documents/wireless-andon-tower-nrf24l01)]([https://ieee-dataport.org/documents/wireless-andon-tower-nrf24l01](https://ieee-dataport.org/documents/wireless-andon-tower-nrf24l01)) (accessed on 9 March 2022).  
This article was partially produced thanks to the support of the Instituto Tecnologico Jose Mario Molina Pasquel y Henriquez, Unidad Academica Zapopan, Tecnologico Nacional de Mexico TecNM and Advanced Technology Center CIATEQ.  
The authors declare no conflict of interest.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EKF & Extended Kalman Filter \\ SLFN & Single-Hidden-Layer Feedforward Neural Network \\ KF & Kalman Filter \\ ML & Machine Learning \\ AI & Artificial Intelligence \\ NN & Neural Network \\ ELM & Extreme Learning Machine \\ ECI & Earth-Centered Intertial \\ RSSE & Root Sum Squared Error \\ OS-ELM & Online Sequential Extreme Learning Machine \\ \end{tabular}
## 2 Materials and Methods
### Building of the Triaxial Load Cell Prototype  
The design of the triaxial load cell was tailored to meet the requirements of a risk prevention device capable of measuring triaxial forces generated when the thumb activates a cigarette lighter. The design must consider the requirement of being implemented in any tool manipulated in a workstation. The prototype was built using two aluminium bars fixed with epoxy resin and two screws with a diameter of 4 mm and a length of 16 mm. The mechanical body of the cell has an \"L\" shape and a rectangular cross-section. An acrylic plate was adapted as the base of the horizontal bar (in a real process, the TLC can be adjusted in a hidden way on or inside any workstation surface, tool or product); on the plate, a pad with cables was fixed to transmit the signal from the extensometers to the Wheatstone bridge. To obtain experimental data, the cell was instrumented using strain gages. A strain indicator and recorder were used for dynamic and static monitoring; moreover, for data acquisition, a Strain Gage Vishay (USA): CEA-13-240 UZ-120 was implemented.  
The calibration was carried out individually for each internal cell to determine the value of K for different applied loads. Experimental tests were developed to determine K through a simulation of AF using standardised weights (W) ranging from 0.1 kg to 2.25 kg, as is depicted in Figure 3. The results and analysis are shown in Appendices A and B, respectively. With the resulting deformations and the test model, the average calibration constant K was obtained for the triaxial load cell established in Equations (15) to (17). The results were as follows: KH = 70,761,838,235 Pa, KL = 70,892,578,125 Pa and KV = 71,222,132,813. The average represents the three-dimensional calibration coefficient KAV = 70,958,849,725. The complete test results are in Tables A1-A3 of Appendix A.  
The application of forces can be performed either by applying dead weights or by using known forces with the support of another load cell or, if possible, with the help of a universal machine. The KAV was the value used in Equations (8)-(10) to determine the resulting triaxial force and its components (FR, Fx, Fy and Fz). For each test, known forces (W) were applied in different directions using \(\alpha\), \(\beta\) and \(\gamma\) angles. The results from test 1 are shown in Table 3. Applying Equations (8)-(11), the force components (Fx, Fy and Fz) and the resulting force FR were determined, and the complete experimental data from four tests with 15 known weights are provided in Tables A4-A6 of Appendix B.  
The deviation between the known force (W) and the applied force (AF) measured with the triaxial load cell was included as a validation procedure, like the percentage of deviation. As is observed in Table 3 and Appendix B's results, the variation rate was small, with a mean of 2.02% and standard deviation, SD, of 1.13. Thus, the triaxial load cell was