esa-sceva/satcom-chunk-collection huggingface dataset:
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 1 Introduction
Due to the difficulties of finding appropriate nodes to act as CHs and the accompanying complexity, clustering in WSNs takes a long time. In computer science, clustering is an NP-hard problem [14; 15; 16; 17; 18; 19; 20]. On the other hand, previous research has shown that evolutionary algorithms are necessary to improve network efficiency when using traditional methods to choose cluster heads [21; 22; 23; 24]. This article proposes an energy-efficient clustering algorithm that can be used for data transmission purposes in sensor nodes during emergency disaster supervision in WSN [25; 26; 27; 28; 29; 30; 31; 32]. The routing purpose in the clustering manner is done with the help of optimal CH, which is selected by involving different concerned parameters. An image compression method named SPIHT is employed to decrease the transmission load. Moreover, an SRCM model is employed to find the best route towards the destination, compensating for the node energy level and communication link quality [33; 34; 35; 36; 37; 38; 39; 40]. All these steps are followed by aggregation techniques using an XOR gate operation, eliminating identical information from different nodes [41; 42; 43; 44; 45; 46]. The following are some of the critical contributions made by this paper:  
* In this paper, we develop a strategy to cluster WSNs in a way that is both energy-efficient and sensitive to the characteristics of emergencies. * We try to Improve the CH excertion method by devising a new function that takes energy efficiency, cluster construction time, trust value and other parameters. * Surpassing existing systems in terms of their energy consumption, the number of live nodes, network development time, and the number of sink sites in catastrophic scenarios, among other metrics. The remainder of the paper is arranged in the following manner. Section 2 provides an overview of relevant research, followed by Section 3, which presents our suggested technique. Section 4 discusses the simulation findings and evaluations that were conducted. In Section 5, there is a brief discussion about the result. Finally, at last, the conclusion and potential future work has been discussed in Sections 6 and 7.
###### Abstract  
The grouted sleeve lapping connector called the APC connector has the advantages of high fault tolerance, convenient construction, compacted grouting, and low cost, together with realizing full prefabrication of vertical components. In this paper, a quasi-static test of two fully prefabricated L-shaped walls connected with two types of APC connector and a cast-in-place wall was carried out under high axial compression ratio (0.5) to compare their seismic performance. The results indicated that the two types of connectors effectively transferred the rebar stress in the prefabricated walls, and the failure modes and final crack distribution of the prefabricated walls and the cast-in-place wall were basically identical. The failure of the cast-in-place wall occurred at the root of the wall limb, while the failure of the prefabricated walls occurred at the top of the sleeve due to the constraint of the sleeve. The bearing capacity, stiffness, ductility, and energy dissipation of the prefabricated specimen connected by the type-1 sleeve was comparable to that of the cast-in-place wall, while the prefabricated wall connected by the type-2 sleeve showed greater bearing capacity, stiffness, and ductility. Finally, some suggestions for seismic design of prefabricated components connected with APC connectors are proposed.  
prefabricated L-shaped shear wall; high axial compression ratio; APC connectors; seismic behavior; quasi-static test +
Footnote †: journal: Applied Sciences
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Position
#### 3.1.5 Mode Shapes  
The mode shapes of even modes are usually of a symmetric pattern. Because the sum of the velocities cancel out over the whole panel area, these modes do not appear in the frequency response of the normalized mean surface velocity \(\overline{v}_{x,y}(f)\). With the asymmetric placement of an ABH, it is possible to activate the lower even modes. The simulations of the mode shapes presented in the following were conducted without the air back cavity by the ANSYS Modal Superposition Solver. In Figure 9, the mode shapes of the first four modes \(f_{1,1}=84\) Hz, \(f_{2,1}=147\) Hz, \(f_{1,2}=192\) Hz and \(f_{3,1}=250\) Hz are shown for the reference panel.  
The mode shapes for placing the ABH at the best position P22 are shown in Figure 10. The impact of the Acoustic Black Hole on the panel is recognizable for all four mode shapes. The peak amplitudes are shifted towards the center of the ABH as intended. While the  
Figure 8: Comparison of the normalized mean surface velocity \(\frac{\overline{v}_{x,x}}{p}(f)\) at the three best ABH positions P12, P21, P22.  
Figure 9: First four mode shapes for the uniform reference panel.  
basic shape for the first two modes is only deformed, the velocity of the third and fourth modes is bundled in the area of the ABH.
# Continuous Control Set Predictive Current Control for Induction Machine
## 2 Induction Machine Model  
Dynamic mathematical model of the induction machine is described by Equations (1)-(4). The reader is referred to [31] for further understanding of the model.  
\[\mathbf{v}_{sa\beta}=\mathbf{R}_{s}\mathbf{i}_{sa\beta}+\frac{\mathrm{d}}{ \mathrm{d}t}\mathbf{\psi}_{sa\beta} \tag{1}\] \[0=\mathbf{R}_{r}\mathbf{i}_{n\beta}+jp\omega_{r}\mathbf{\psi}_{ra\beta}+ \frac{\mathrm{d}}{\mathrm{d}t}\mathbf{\psi}_{ra\beta}\] (2) \[J\frac{\mathrm{d}}{\mathrm{d}t}\omega_{r}=T_{e}-T_{l}\] (3) \[T_{e}=\frac{3}{2}p(\psi_{sa}i_{s\beta}-\psi_{s\beta}i_{sa}) \tag{4}\]  
The model is written in stationary \(\alpha\beta\)-reference frame where \(\mathbf{v}_{sa\beta}=\begin{bmatrix}v_{sa}&v_{s\beta}\end{bmatrix}^{T}\) describes stator voltage vector, \(\mathbf{i}_{sa\beta}=\begin{bmatrix}i_{sa}&i_{s\beta}\end{bmatrix}^{T}\) and \(\mathbf{i}_{ra\beta}=\begin{bmatrix}i_{ra}&i_{r\beta}\end{bmatrix}^{T}\) describe stator and rotor current vector, \(\mathbf{\psi}_{sa\beta}=\begin{bmatrix}\psi_{sa}&\psi_{s\beta}\end{bmatrix}^{T}\) and \(\mathbf{\psi}_{ra\beta}=\begin{bmatrix}\psi_{ra}&\psi_{r\beta}\end{bmatrix}^{T}\) describe stator and rotor flux linkages. \(J\) is inertia constant of the machine, \(\omega_{r}\) is rotor shaft speed, \(p\) is number of pole pairs, \(T_{e}\) is electromechanical torque and \(T_{l}\) is load torque. \(R_{s}\) and \(R_{r}\) are stator and rotor resistance matrices, while \(j\) represents the rotation matrix described by Equation (5).  
\[R_{s}=\begin{bmatrix}R_{s}&0\\ 0&R_{s}\end{bmatrix}\quad R_{r}=\begin{bmatrix}R_{r}&0\\ 0&R_{r}\end{bmatrix}\quad j=\begin{bmatrix}0&-1\\ 1&0\end{bmatrix} \tag{5}\]  
The flux linkages are depicted by Equations (6) and (7).  
\[\mathbf{\psi}_{\text{sa}\beta} =L_{s}\mathbf{i}_{\text{sa}\beta}+L_{m}\mathbf{i}_{\text{ra}\beta} \tag{6}\] \[\mathbf{\psi}_{\text{ra}\beta} =L_{r}\mathbf{i}_{\text{ra}\beta}+L_{m}\mathbf{i}_{\text{sa}\beta} \tag{7}\]  
where \(L_{s}\), \(L_{r}\) and \(L_{m}\) represent stator, rotor, and mutual inductance, respectively.
## 4 Experimental Results
### Image Acquisition
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Total No. of Images**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-7}  & **Benign** & **Malignant** & **Benign** & **Malignant** & **Benign** & **Malignant** \\ \hline \multirow{2}{*}{Mini-MIAS} & \multirow{2}{*}{206} & \multirow{2}{*}{110} & 162 & 88 & 44 & 22 \\ \cline{5-7}  & & & 44 & 22 & 162 & 88 \\ \hline \multirow{2}{*}{DDSM} & \multirow{2}{*}{479} & \multirow{2}{*}{502} & 319 & 335 & 160 & 167 \\ \cline{5-7}  & & & 160 & 167 & 319 & 335 \\ \hline \multirow{2}{*}{INbreast} & \multirow{2}{*}{127} & \multirow{2}{*}{73} & 101 & 58 & 26 & 15 \\ \cline{5-7}  & & & 26 & 15 & 101 & 58 \\ \hline \multirow{2}{*}{BCDR} & \multirow{2}{*}{426} & \multirow{2}{*}{310} & 284 & 207 & 142 & 103 \\ \cline{5-7}  & & & 142 & 103 & 284 & 207 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of used benign and malignant mammogram images. Figure 7: Mammogram samples: (**a**) INbreast database [51]; (**b**) BCDR database [52]. To assess the adequacy of the proposed classification method through identification, in particular, it should be determined whether the automated texture features extracted from the breast cancer case can likewise prompt great characterization results if a breast malignancy case indicates a malignant or benign case. All images have been segmented based on the segmentation method described in Section 3.2, whereas the DDSM dataset has been segmented using the cropping technique. The Mini-MIAS dataset used as an input for the segmentation method resulted in 206 out of 207 images successfully segmented from benign cases and 110 out of 115 images segmented correctly from the malignant cases. More so, INbreast and BCDR are segmented as whole images correctly. The proposed methodology was validated through a number of different experiments. The experiments were performed with MATLAB (2020b) with a Core-i7 processor, 32 GB RAM, and Windows 10 operating system.
###### Abstract  
The purpose of this study was to investigate the possible predictors of success of microimplant-assisted rapid palatal expansion (MARPE) in skeletally mature patients. Additionally, factors associated with the amount of maxillary expansion were analyzed. Factors associated with MARPE success were analyzed in 53 adult patients (27 males, 26 females, mean age 25.8 \(\pm\) 8.9 years, and range 18.0 to 56.6 years) who had a maxillary transverse deficiency greater than 2 mm and a cervical vertebral maturation stage of 6. Age at pretreatment (T1), sex, sutural bone density at T1, type of appliance, mode of microimplant fixation, and lateral cephalometric variables at T1 were considered for inclusion as predictors for MARPE success. In patients who showed successful maxillary skeletal expansion, the linear distances of maxillary widths were measured on cone-beam-computed-tomography images at T1 and after MARPE (T2), and factors associated with the amount of expansion were analyzed. In total, 41 of the 53 patients showed successful maxillary expansion. Age (\(p\) = 0.019), sex (\(p\) = 0.002), and A-N perp (\(p\) = 0.015) were significantly associated with the success of MARPE. The factors associated with the amount of maxillary skeletal expansion were SN-MP and midpalatal-suture density at T1. In conclusion, there is a greater chance of failure in male patients who are older and have maxillary retrusion. A greater amount of maxillary expansion can be expected in patients with a higher mandibular-plane angle and with lower midpalatal-suture density.  
microimplant-assisted rapid palatal expansion; maxillary transverse deficiency; success predictors
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Route Measurement and Scoring
#### 3.4.2 Residual Energy  
The residual energy of a node not only directly reflects the survival of the node, but also affects the lifetime of the entire network. Therefore, energy was selected as the second routing metric factor. The residual energy scoring (RE) is shown in Equation (9):  
\[RE=\frac{E_{residual}}{E_{initial}}\text{, }RE\in[0,1] \tag{9}\]  
where \(E_{residual}\) is the residual energy of the node at a certain moment, which is a variable. \(E_{initial}\) is the total energy of the node at the beginning, which is a constant. Therefore, \(RE\) must be between 0 and 1, as \(CD\) is. Clearly, the more energy there is left, the higher the score is.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Neural Network Model
#### 4.6.3 Feature Importance Methodology  
To understand the contribution of different features to shot accuracy prediction, we employed SHAP (SHapley Additive exPlanations) values, which provide a unified measure of feature importance.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 2 Materials and Methods
### Experimental Measurements
#### 2.4.2 Measurement of the Vibration Response  
The vibration response was measured with the machines fixed, as shown in Figure 3, with the accelerometers placed on the outer faces.  
The equipment employed for the measurements were as follows:  
* Acquisition system Pulse Bruel & Kjaer Front-end type 3560C and a laptop with Pulse Labshop software.
* ICP triaxial accelerometers of PCB 356A16 (available frequency range from 0.5 Hz to 5000 Hz).
## 3 Global Positioning System (GPS) L1 Receiver: 2D Pattern vs. 3D Pattern
### Received Signal Power Levels  
As a minimum of four signals are required for a standalone GPS navigation solution, Figures 6-9 display the first-, second-, third-, and fourth-highest received power levels of the GPS L1 C/A signals at the receiver position as a function of the altitude, during the full considered trajectory, when assuming a 0 dBi receiver antenna gain, when the GPS L1 transmitter antenna is assumed to have a 3D pattern (in blue) and a 2D pattern (in red). The highest received signal powers (Figure 6) are very similar for both assumed patterns, which is explained by the fact that the signal comes from the main lobe of the transmitter antenna pattern; the main lobe patterns are very similar for 2D and 3D patterns, as also shown in Figure 4, because the main lobe pattern is well specified and there is a very small gain variance for different azimuth angles. The second-highest received signal powers reflect more differences, mostly at the end of the trajectory, as most of these signals come from the side lobes, where the dependency of the transmitter's antenna gain with the azimuth is more evident; the differences are more notable in the case of the third-highest received power during the entire MTO.  
As with four signals we would be able to compute the navigation solution, the fourth received signal power is of highest interest for us. What we can notice from Figure 9 is that in the case of 3D pattern the fourth-highest received signal power is sometimes lower than \(-170\) dBm. On the other hand, this is never the case for the 2D pattern. Observing Figure 9 carefully, we can notice that the received powers are different; however, it is hard to have clear evidence, in which case the received powers are higher. For a better understanding of the effect of the two considered patterns, in the next section we will investigate the signals' availability.  
Figure 5: GPS L5 antenna gain pattern.  
Figure 8: Third-highest received power level of GPS L1 signals, during the considered MTO, for a 0 dBi receiver gain pattern.  
Figure 6: Highest received power level of GPS L1 signals, during the considered MTO, for a 0 dBi receiver gain pattern.  
Figure 7: Second-highest received power level of GPS L1 signals, during the considered MTO, for a 0 dBi receiver gain pattern.
## 5 LMS Channel Models
### Multi-State Markov Channel Model  
Other works in the literature show a satellite channel model approach based on a multi-state Markov chain [51]. In [52], the authors propose a channel model with more states but that is capable of limiting transition matrix size to save processing time and memory requirements for predicting fading properties and error probability performance for LMS. They have built their model based on typical measured data, using slow fading modeled as a lognormal distribution and fast fading modeled as a Rayleigh distribution. The proposed two-level, multi-state Markov model gives satisfactory predictions of first- and second-order statistics of propagation properties for satellite communications [62]. Other works exist in the literature that propose satellite channel models based on a Markov chain with a number \(n\) of states Figure 8. For example, in [26; 53], the authors develop and analyze a methodology to partition the received signal-to-noise ratio (SNR) into a finite number of states according to the time duration of each state. Then, their proposed finite-state Markov chain is composed of a number of states where each of them corresponds to different channel quality represented by the fading associated with the channel. In addition, in [54; 55], the authors describe in detail a satellite channel for a Digital Video Broadcasting-Return Channel Satellite (DVB-RCS) mobile system. They present a high-level satellite channel model based on the Markov approach. The proposal introduces the concept of windowed observations, and the idea in the paper is that of analyzing more packets in a certain window in order to evaluate the link degradation, computing an analysis of the packet error rate (PER) associated with a specific observation window. The analysis conducted by the authors does not consider only a two-state (good and bad) model but, introducing the concept of quality degree (QD), they analyze and compute the PER belonging to each window, discretizing the PER values in order to generate the correct number of states for the considered Markov model; see Figure 9. Therefore, the number of additional states of the classical GE model for augmenting the accuracy of the model depends on the specific operative conditions and, thus, also on the noise power level, satellite type, climatic conditions, and error correction rate.  
Figure 7: Six-state Markov channel model.  
In [56], the authors propose an analysis of the Ka-band satellite channel propagation characteristic and how meteorological factors affect it. They introduce, firstly, the principal component analysis method, and then a fuzzy clustering analysis in the channel study, providing a multi-state Markov model that proved its accuracy throughout simulation experiments.
## 5 Hydrodynamic Response of Floating Platform  
### Free-Decay Test  
Free-decay tests are methods generally used in a wave tank to determine the natural period of a floating system. The OC4 project was based on a 1/50 th-scale semi-submersible platform in the MARIN wave basin in 2011. This model was revised two years later, to provide more precise test data for the OC5 project [18]. Owing to the different properties of the mooring line (line stiffness) and rotating inertia forces in the two tests, the OC4 and OC5 data reveal good agreement in terms of translation motions (surge, sway, and heave) but a relatively large discrepancy in terms of rotating motions (roll, pitch, and yaw), as observed in Table 6. Six-DOF free decay tests were conducted to determine the hydrodynamic damping characteristics of the OC5 semi-submersible platform.  
The wave mode was set as still water, and the air density was zero. Only the platform was considered to simplify the simulation; however, the gross mass should also be considered. The platform was given a prescribed displacement and released to move freely from the initial position. This test considered only three rigid-body DOFs, that is, the surge, pitch, and heave motions.  
The results are presented in Figure 8, along with the simulation results from GNU and the wave basin test results from phase II of the OC5 project. The heave and surge time-domain responses for the platforms are in good agreement, as similar results were obtained in the heave and surge periods in the OC4 and OC5 projects. However, in the case of pitch, a relatively large discrepancy was observed in the time-domain response for the GNU simulation. As mentioned above, this effect may be owing to the different properties of the mooring line and rotating inertia forces of the platforms used in the two projects. Based on the natural period of the pitch, the pitch results were in good agreement with the OC5 test data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DOF** & **OC5 Natural Period** & **OC4 Natural Period** \\ \hline Surge & 107 s & 107 s \\ Sway & 112 s & 112 s \\ Heave & 17.5 s & 17.5 s \\ Roll & 32.8 s & 26.9 s \\ Pitch & 32.5 s & 26.8 s \\ Yaw & 80.8 s & 82.3 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Natural period in OC4 and OC5 project.  
Figure 7: Torque curve between fluid structure interaction (FSI) method and unsteady method in 11 m/s wind speed.
## 2 Materials and Methods
### Evaluation  
Evaluation Metrics  
A confusion matrix quantifies the performance of a classification model by detailing the distribution of correctly and incorrectly predicted samples across different classes. Table 12 shows a confusion matrix skeleton. The equations for true positive rate, false positive rate, precision, recall, and F1-score are given by Equations (8), (9) and (11)-(13).  
\[\text{True Positive Rate}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{False Positive Rate}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{9}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{10}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{12}\] \[\text{F1-Score}=\frac{2\times\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{13}\]  
The ROC area represents the area under the ROC curve, which plots the true positive rate (TPR) against the false positive rate (FPR) across thresholds. It quantifies classifier performance, with a higher area indicating better discrimination. When normalized, it equals the probability that a randomly chosen positive instance ranks higher than a negative one.
## 2 Materials and Methods
### Study Areas and Soil Samples  
France (mainland) and the Czech Republic were selected as the two study areas, being representative countries within the European Union for digital SOC mapping at a national scale due to their diverse geography, climate, and topography (Figure 2). France's varied landscapes, ranging from the fertile plains of the Loire Valley to the mountainous regions of the Alps and Pyrenees, provide a wide range of soil types and land uses, allowing for comprehensive organic carbon assessments across different ecological contexts. Conversely, the Czech Republic, characterized by its hilly terrain and continental climate, showcases a distinct range of soil types affected by specific agricultural and forestry practices.  
The LUCAS (Land Use/Cover Area Frame Statistical survey) 2018 SOC database is a comprehensive resource developed by the European Commission's Joint Research Centre, aimed at assessing soil quality across Europe [9]. This database provides critical information on the spatial distribution and quantity of SOC, which is fundamental for understanding soil health, as well as carbon sequestration potential and its implications for climate change. The soil samples from the entire LUCAS 2018 dataset at 0-20 cm soil depth measured in g kg\({}^{-1}\) were filtered according to the geographical coverage of two study areas. Outlier removal from the raw soil sampling datasets was performed using an interquartile range approach, having an interquartile range of 1.5 as the threshold below the first and above the third quartile for outlier removal.