esa-sceva/satcom-chunk-collection huggingface dataset:
# InoT Sensor Network for Wild-Animal Detection near Roads
## 2 Materials and Methods
### Structure of IoT Sensor Node  
Figure 2 shows the structure of the DSN. The node consists of a microcontroller (MCU), a radio communication module (LoRa transceiver), motion sensors (PIR--passive infrared sensor), and visual signaling elements (LED--light emitting diode). DSN is powered by batteries that are charged using a solar panel. A low-dropout regulator (LDO) provides 3.3 V to all components. When choosing the components of the DSN node, radio communication module, microcontroller, and motion sensors, the focus was on energy efficiency and price. Semtech SX1276 modules using LoRa modulation were selected for wireless communication between the Gateway and DSN nodes. These modules are characterized by low energy consumption when working in both sleep and active modes and long (up to 10 km) transmission distances [41]. When choosing a microcontroller, the power consumption in sleep mode, price, and availability in the market were considered. The selected Atmega 328 microcontroller, which consumes up to 10 uA in sleep mode, has enough digital input-output pins (DIO), a number of analog inputs (ADC), and an SPI interface (serial peripheral interface) for controlling the wireless communication module [42].  
Figure 1: Animal detection IoT network with the possible placement of the DSN nodes.
## 4 Prediction Algorithm of Maritime Traffic Condition  
### Construction  
The construction process of the artificial intelligence algorithm consisted of 6 steps: obtaining the dataset, data pre-processing, feature augmentation, model design, training/validation of the model, and results validation. Figure 1 shows a construction diagram of the algorithm.  
The dataset used in this study included the data from the automatic identification system (AIS). AIS is an important equipment required for ship operation, such as monitoring ship operation and identifying safe navigation conditions. According to the International Convention for the Safety of Life at Sea (SOLAS), it is compulsory to install AIS on all passenger ships and sailing ships with a gross tonnage of 300 tons or more.  
Data pre-processing refers to the process of creating a shape suitable for data analysis and processing, and generally interpolates missing data or processes erroneous data. No matter how good the tool or analysis technique is, it is difficult to obtain good results with low-quality data, so about 80% of the total time is usually spent on data pre-processing. In the data pre-processing step, a range was selected according to the characteristics of the maritime traffic data, and pre-processing was performed using the interquartile range (IQR).  
Figure 1: Algorithm construction diagram.  
Similar to data augmentation, feature augmentation is an operation of extracting features by processing original data using domain knowledge. In the feature augmentation step, various features were obtained from the basic features to increase the prediction reliability for maritime traffic congestion.  
Considering that maritime traffic data are time-series data, the long short-term memory (LSTM) model was used, which is a representative model for processing time-series data.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 3 Mathematical Model of Structural Damage Detection  
### Definition of Structural Damage  
Damage detection of structures based on model updating is a common theoretical method in the field of structural damage detection. Generally speaking, without considering the damping of the structure, the vibration differential equation of the structure can be expressed as:  
\[\left(\mathbf{K}-\omega^{2}\mathbf{M}\right)\mathbf{\Phi}=0 \tag{14}\]  
where \(\mathbf{K}\) and \(\mathbf{M}\) represent the global stiffness matrix and the global mass matrix of the structure, respectively. \(\omega\) and \(\mathbf{\Phi}\) represent the frequency and the mode shape matrix of the structure, respectively.  
The location and degree of structural damage can be detected by identifying changes in structural modal parameters, \(\omega\) and \(\mathbf{\Phi}\). The modal parameters of the structure are only related to the stiffness matrix and mass matrix of the structure as shown in Equation (14). Therefore, the damage of the structure can be simulated by changing the stiffness or mass of the structure. In this study, the structural damages are simulated by reducing the stiffness of the structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Calculated Performance** & **ChOA** & **W-ChOA** \\ \hline \multirow{2}{*}{Rastrigin} & Optimal value & \(5.68\times 10^{-14}\) & 0 \\  & Average value & \(3.72\times 10^{-9}\) & \(2.96\times 10^{-13}\) \\ \hline \multirow{2}{*}{Quartic} & Optimal value & \(3.34\times 10^{-4}\) & \(1.57\times 10^{-4}\) \\  & Average value & \(1.66\times 10^{-3}\) & \(6.27\times 10^{-4}\) \\ \hline \multirow{2}{*}{Griewank} & Optimal value & \(5.68\times 10^{-14}\) & 0 \\  & Average value & \(2.59\times 10^{-12}\) & \(5.80\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of benchmark functions.  
Figure 3: Convergence curves of three benchmark functions. (**a**) Rastrigin function. (**b**) Quartic function. (**c**) Griewank function.  
Based on the finite element theory, the model of the detected structure can be divided into \(Q\) elements. The stiffness matrix of the structure with damages can be expressed as:  
\[\mathbf{K}_{d}=\sum_{j=1}^{Q}\bigl{(}1-\alpha_{j}\bigr{)}\mathbf{K}_{e}^{j} \tag{15}\]  
where \(\mathbf{K}_{d}\) represents the global stiffness matrix of the structure under damaged condition and \(\mathbf{K}_{e}^{j}\) represents the intact condition of the \(j\)-th element, respectively. The damage factor \(\alpha_{j}\) belongs to (0, 0.90) in this paper, which represents the stiffness reduction coefficient of the \(j\)-th element.
# A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs
## 3 A Hybrid Multi-Objective Optimization Method
### Nga-Ii  
NSGA-II follows the basic framework of a genetic algorithm (GA), which imitates the evolution process in nature. The procedure of standard NSGA-II is shown in Algorithm 1. After the initialization, \(N\) individuals are generated in order to form the population \(P\), thenthe non-dominated front of each individual is determined and the corresponding crowding distance is calculated. Next, the algorithm follows basic GA operators, including selection, crossover, and mutation, to generate new individuals \(Q\). Then, the non-dominated front and crowding distance of each individual in the mixed population \(L\) is calculated. The individuals with a comparatively low rank of non-dominated front and small crowding distance values survive to become the population of the next generation. The above steps repeat until the termination criterion is satisfied. The main characteristics of NSGA-II are: (1) a fast, non-dominated sorting strategy is proposed to reduce the algorithm complexity; (2) an elite retention mechanism is used to prevent the loss of excellent individuals; (3) the concept of crowding distance is proposed to maintain the diversity of the population.  
```
Input:\(N\) (population size) Output:\(P\) (final population)
1\(P\leftarrow\) Initialization(\(N\));
2\([F_{1},F_{2},\cdots]\leftarrow\) Non-dominated-sort(\(P\));/\(F_{l}\) is the set of solutions in the \(i\)-th non-dominated front
3\(CrowdDis\leftarrow\)Crowding-distance-assignment(\([F_{1},F_{2},\cdots]\));
4 whiletermination criterion not fulfilleddo
5\(P^{\prime}\leftarrow\)Tournament-selection(\(P\));
6\(Q\leftarrow\)Genetic-operator(\(P^{\prime}\));/Crossover and mutation of \(P^{\prime}\)
7\(L\leftarrow\)\(P^{\prime}\cup Q\);
8\([F_{1},F_{2},\cdots]\leftarrow\)Non-dominated-sort(\(L\));
9\(CrowdDis\gets\)Crowding-distance-assignment(\([F_{1},F_{2},\cdots]\));
10\(m\leftarrow\)Minimum value s.t.\(|F_{1}\cup\cdots\cup F_{l}|\geq N\);
11\(k\leftarrow\)The number of \(|F_{1}\cup\cdots\cup F_{m-1}|_{j}\)
12\(F_{m}\leftarrow\)Sort \(F_{m}\) by crowding distance;
13\(P\gets F_{1}\cup\cdots\cup F_{m-1}+F_{m}[1:(N-k)]\);/Remove the redundant solution from \(F_{m}\) with the relatively small CrowdDis
14 return\(P\);
```  
**Algorithm 1**NSGA-II
## 3 Results and Discussion  
### Morphology and Encapsulation Efficiency  
All capsules appeared globular and irregular in shape with a rough surface and a drop-like shape. This result is probably due to the high surface tension of the used hardening solution (CaCl\({}_{2}\)) that determines an imperfect sphere formation [27]. However, no surface cavities or fractures were visible. The average size of capsules was 3.5 \(\pm\) 0.52 (mm) without any significant difference among the different microcapsules types. The capsules with S + G showed a more dense structure, as confirmed by the viscosity of G solution, due to electrostatic interactions between amino groups of G and carboxyl groups of S, which make cells more resistant to enzymatic and acidic hydrolysis [28].  
Encapsulation efficiencies (EE%) of the four microcapsule types are reported in Table 1. EE% is a parameter that describes both the survival of viable cells and the efficacy of entrapment of the encapsulation procedure [29]. The yield of _S. thermophilus_ TH982 viable cells co-encapsulated using S + F (96.13%) was significantly higher (\(p\) < 0.01) than that of cells encapsulated with S alone (91.07%), used as control, and was similar (not statistically different) to S + I (98.61%). The high EE% obtained with S + F and S + I matrices might be due to the decrease in the porosity of the gel-beads and, consequently, to a reduction in the leakage of entrapped _S. thermophilus_ TH982 cells [30]. Besides, incorporating prebiotics as material for encapsulation may better protect probiotics in food systems and inside the gastrointestinal tract due to mutual cells/prebiotic interactions [31]. By contrast, the number of viable cells encapsulated in S alone and S + G showed no significant difference (EE% 91.07 and 90.50, respectively). The main reason why EE% is normally lower than 100% is linked to cell damage due to detrimental conditions caused by the encapsulation process itself, such as shear stress and the use of concentrated solutions. Furthermore, during the time required for the hardening of capsules, a physical loss of cells can occur in significant numbers [29]. It should also be noted that a dissolution process is required to determine the number of viable cells in the microcapsules, and therefore an incomplete disintegration can underestimate the calculated EE% value. In particular, for the solubilization of S-based capsules, sodium citrate (10% _w/v_) was used, which is a chelating agent [30] that can remove calcium from the \"egg-box\" structure, leading to the destabilization of alginate coating and to the effective release of cells [32].
## 2 Methods
### Inversion of VTEC Distribution from the FR Angle  
The linearly polarized signal will experience a rotation of the polarization plane by an angle \(\Omega\) when it propagates through the ionosphere layer. This angle is known as the FR angle [47]. The magnitude of FR angle \(\Omega\), for a wave of frequency \(f\), traveling vertically one way through the ionosphere can be expressed as [48]:  
\[\Omega=\frac{\left|e^{3}\right|}{8\pi^{2}c\varepsilon_{0}m^{2}}\cdot\frac{ \overline{B\cdot\cos\theta\cdot\sec\phi}\cdot VTEC}{f^{2}}=\frac{2.365\times 1 0^{4}\cdot\overline{B\cdot\cos\theta\cdot\sec\phi}\cdot VTEC}{f^{2}} \tag{8}\]  
where \(e\) and \(m\) are the charge and mass of an electron respectively; \(c\) is the speed of light; \(\varepsilon_{0}\) is the permittivity of free space; \(B\) is the intensity of earth's magnetic field, which can be calculated from geomagnetic field data, such as International Geomagnetic Reference Field (IGRF) data; \(\theta\) is the angle between the magnetic field and satellite pointing vector; \(\phi\) is the SAR incidence angle; \(\overline{B\cdot cos\theta\cdot sec\phi}\) is the magnetic field factor at a constant height (e.g., 400 km); and f is the radar frequency. Generally, the value of \(\overline{B\cdot cos\theta\cdot sec\phi}\) depends on the geographic coordinates, orbit and imaging geometry of satellite, and can be calculated from [11]. Once the FR angle is determined by (7), VTEC is calculated by:  
\[VTEC=\Omega\cdot\frac{f^{2}}{2.365\times 10^{4}\cdot\overline{B\cdot cos\theta \cdot sec\phi}} \tag{9}\]
## 5 Conclusions  
In general terms, excellent accuracies have been obtained from the ML models. The statistical tests' results and the different algorithms' accuracy have shown that the frequency-domain features, particularly the logarithmic ratios between the maximum amplitudes of the envelope spectrum around the pulses spaced at the faults' characteristic frequency, have been the most important ones. These, in addition to some of the most critical time-domain features, have been able to yield the highest accuracies in this paper. Among the models, the k-NN and SVM classifiers were the best performers. The accuracy of these algorithms is given in Tables 8-11. It can also be seen from Figures 10 and 11 that signals corresponding to REBs with faulty balls have been the most difficult for the algorithms to classify. The CNN's accuracy can be improved by increasing the dataset's size, as mentioned in Section 4. These models can be potentially helpful for automatically diagnosing the health status of REBs.  
The main drawback of the proposed method is that the database on which the study has been conducted is imbalanced, as seen in Table 1. This means that the classification result may be biased, as the classifiers are more sensitive to detecting the majority class and less sensitive to the minority class. A common technique to counteract the imbalanced data problem is to oversample the dataset. Nevertheless, after applying cross-validation, it is known to lead to over-optimistic results, for which conventional model evaluation metrics such as accuracy are not good indicators of the algorithms' performance. In the industry, faults in REBs are typically not evenly distributed, with malfunctions such as those affecting inner and outer races occurring more frequently than others. Therefore, dealing with imbalanced databases is a common challenge. The best practice to address this issue is to apply cross-validation by stratifying each k-fold to capture the unbalanced distribution of groups on each target feature. In this paper, as discussed, conventional five-fold cross-validation has been implemented. This is an essential point of improvement for future research.  
Conceptualization, E.Q.-C.; methodology, E.Q.-C., D.C.-F. and E.G.-M.; software, D.C.-F.; validation, E.Q.-C., D.C.-F. and E.G.-M.; formal analysis, D.C.-F., E.Q.-C. and E.G.-M.; investigation, D.C.-F., E.Q.-C. and E.G.-M.; resources, D.C.-F., E.Q.-C. and E.G.-M.; data curation, D.C.-F.; writing--original draft preparation, D.C.-F.; writing--review and editing, E.Q.-C.; visualisation, E.Q.-C.; supervision, E.Q.-C. and E.G.-M.; project administration, E.Q.-C. and E.G.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are openly available in FigShare and have been cited accordingly.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 2 Results
### Scores and Ranking of the Cannabinoids Using ToxPi  
The data regarding the scores of the docking in aromatase, ER, and AR, as well as aromatase inhibition and ER and AR agonistic/antagonistic activities of the nine studied cannabinoids and **CBD** were added to ToxPi and used to score and rank the compounds overall. As expected, **CBD** obtained the best ToxPi score (0.7121) mainly due to its aromatase inhibition results, score of the docking in aromatase, ER antagonistic activity over T, and AR inverse agonistic and antagonistic activities. Regarding the minor phytocannabinoids, **CBDB** scored the best (0.5248) because of the high values determined for ER antagonistic activity over E\({}_{2}\) and AR docking score, as well as AR inverse agonistic and antagonistic activities. **CBN** ranks in third place with a ToxPi score of 0.4864 for which ER and AR docking scores, ER antagonism over E\({}_{2}\) and T, and AR antagonistic activity contributed the most. The scores for the remaining minor cannabinoids were mainly determined by AR modulation, except for **CBDA**, in which ER modulation contributed significantly, and for **CBDM**, where the aromatase binding score was also relevant. The cannabinoid with the worst ToxPi score was **CBGVA** (0.1219). The overall rank and ToxPi scores are presented in Figure 9.  
Figure 9: ToxPi scores and ranks. The information regarding the docking analysis in aromatase, ER, and AR, as well as the information about aromatase inhibition and ER and AR agonistic/antagonistic  
activities for the nine selected cannabinoids and CBD, was inserted in different slices in ToxPi and used to rank the compounds.
# Addressing the Algorithm Selection Problem through an Attention-Based Meta-Learner Approach
## 3 Solution Model
### Dataset
Furthermore, each problem was solved thirty times with each of the three algorithms for selecting the specific algorithm that produced the best result for a particular VRPTW instance. Then, we computed the mean cost of the best solution across all iterations. We selected the meta-heuristic that generated the solution with the lowest average cost as the best meta-heuristic for that particular problem instance. Once again, it is important to stress that the VRPTW is not the actual focus of this research but a field for testing our solution approach. Although we care about the cost of the solutions, the importance relies on clearly defining a single algorithm out of the three that, on average, produces the best solution for a given VRPTW instance. For this reason, the hyperparameters used for the meta-heuristic and especially the three neighborhood operators that differentiate them from each other were specifically selected so that each  
Figure 1: Distribution of best cost solutions for each VRPTW problem type. of the three configurations generates, on average, the best solution for only a third of the VRPTW instances. Finally, we complemented the dataset by characterizing each of the VRPTW instances with 26 different features to provide an adequate description of the structure of each problem instance. Such features are summarized as follows:  
* F1. Total number of vehicles [41; 44]. * F2. Maximum capacity of a vehicle [41; 44]. * F3. Total number of customers [45]. * F4-F5. \(X\) and \(Y\) coordinates of depot [44]. * F6-F10. Average, total, standard deviation, skewness, and kurtosis of demand [41; 44]. * F11. Standard deviation of demand as a proportion of vehicle capacity. The standard deviation is computed from the values obtained by having the demand for each customer node divided by the total vehicle capacity [44]. * F12. Minimum vehicles required computed as total demand divided by total vehicle capacity [44]. * F13. The largest demand of a single customer node divided by the total vehicle capacity [44]. * F14. Average customers per route computed as number of customers divided by number of vehicles [46]. * F15. Average service time [31]. * F16. Sum of ready time [31]. Figure 2: Heuristics used as neighborhood operators by SA. (**a**) Intraroute 2-opt; (**b**) Intraroute Or-opt; (**c**) Geni exchange. * F17-F20. Average, standard deviation, skewness and kurtosis of time windows [41]. * F21. The total demand of a problem instance divided by the total vehicle capacity [44]. * F22-F26. Average, total, standard deviation, skewness and kurtosis of the overlap of two time windows [31]. In the case of features 22 to 26, the overlap \(O_{ij}\) of the time windows of customers \(i\) and \(j\) is computed as the ratio between the intersection and union of such time windows [31], as depicted in Equation (3). \[O_{ij}=\frac{\text{intersection}(i,j)}{\text{union}(i,j)} \tag{3}\]
###### Abstract  
With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.  
indoor ranging algorithm; channel state information; received signal strength indicator; extended Kalman filter +
Footnote †: journal: Applied  
1
## 3 Results and Discussion
### Hazardous Metal Leaching and Identification Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Max. Allowed** & **Ba** & **Cu** & **Cr** & **Ni** & **Zn** \\ \hline
**Decree 4741 (mg/L)** & **100** & **-** & **5** & **-** & **-** \\ \hline CRSI003 RED H\({}_{2}\) & 0.421 & - & \textless{}0.01 & 0.049 & 3.625 \\ CRSI003 OXI H\({}_{2}\) & 0.182 & - & 0.137 & 0.011 & 0.281 \\ FEMA004 RED H\({}_{2}\) & - & 0.139 & \textless{}0.009 & 0.027 & 0.674 \\ FEMA004 OXI H\({}_{2}\) & - & 0.136 & \textless{}0.007 & 0.006 & 0.426 \\ FEMA011 RED H\({}_{2}\) & - & \textless{}0.004 & \textless{}0.005 & \textless{}0 & - \\ FEMA011 OXI H\({}_{2}\) & - & \textless{}0.007 & \textless{}0.011 & 0.339 & - \\ FEMA011 RED CH\({}_{4}\) & - & 0.158 & \textless{}0.009 & 0.098 & - \\ FEMA011 OXI CH\({}_{4}\) & - & 0.197 & \textless{}0.009 & 0.040 & - \\ ILME007 RED H\({}_{2}\) & - & - & \textless{}0.008 & 0.019 & 0.596 \\ ILME007 OXI H\({}_{2}\) & - & - & \textless{}0.008 & 0.009 & 0.738 \\ OXMN009 RED H\({}_{2}\) & 3.447 & 0.010 & 0.086 & 0.026 & 0.127 \\ OXMN009 OXI H\({}_{2}\) & 2.107 & \textless{}0.008 & 0.119 & 0.026 & 0.350 \\ OXMN010A RED H\({}_{2}\) & 0.816 & 0.054 & \textless{}0.006 & 0.291 & 0.230 \\ OXMN010A OXI H\({}_{2}\) & 1.829 & 0.225 & 0.558 & 0.030 & 0.559 \\ OXMN010A RED CH\({}_{4}\) & 0.429 & \textless{}0.006 & 0.351 & \textless{}0 & \textless{}0.001 \\ OXMN010A OXI CH\({}_{4}\) & 1.591 & \textless{}0.005 & 0.274 & \textless{}0 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Concentration of metals in the leachate of OC residues and maximum values allowed by Colombian regulations (Decree 4741. 1-27, 2005). Regarding the effect of the pH of the extraction fluid, it is known that the solubility of inorganic compounds is strongly influenced by the pH and the redox potential, while that of organic compounds is dictated by polarity. According to the robustness analyses using the TCLP method, described in resolution 0062-2007, the acidity of the extraction fluid has a high impact on the extraction of metals, meaning that the lower the pH of the extraction fluid, the higher the extraction [52]. The maximum permissible values reported in Table 7 were extrapolated for an L/S = 20 ratio (ratio used in the leaching test performed in the present investigation), taking into account that the European standard prescribes values for L/S ratios of 2 and 10. According to the results presented in Table 7, none of the residues could be classified as inert waste; however, all would be classified as nonhazardous waste. These results are very important, mainly for FEMA011 and OXMN010A, which have shown great potential for use in CLC and iG-CLC technologies [22; 23].