esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Novel Taxonomy for SSNs
### Low Level Virtualization
#### 3.1.1. LLV Based on Operating System Virtualization Solutions  
Multitasking OSs take the responsibility of virtualization technology as part of its tasks, it may use one or both of the two available programming models: event- driven and thread based. This section will provide a list of examples of them briefly. * **TinyOS**[30]: is an embedded, component-based operating system, it is tiny because it uses fewer than 400 bytes, it supports an event driven programming model. TinyOS consists of a set of reusable system components along with a task scheduler, which represents a specific set of services, which are specified by interfaces, and each interface defines how the component directly interacts with other components, also the component is considered as an independent computational entity. Thus, it allows for the application developers to develop custom components according to the applied applications and links them to TinyOS's library, these components are written in neC, which is a dialect of C language optimized for the memory limits of sensor networks, and it has three main abstractions commands, events and tasks. Commands are calls to perform a certain service, events are generated as a reaction to the service implementation and the tasks are produced as functions posted to TinyOS scheduler from commands or events. It may not necessarily be the most viable for SSNs, because it is mainly event-driven. TinyOS features are presented in [29], like reducing the code size by 60%, the timer component causes reduction in CPU utilization by 38%, and the interrupt and task switching also take less time when compared to SenSmart [30]. However, due to the actual added value of this system to the field of SSNs the following OSs evaluated their performance, referring to it as [31; 32]. * **SenSmart**[31]: is one of the most recent multitasking OSs for the resource constrained sensor platform, as implemented on MICA2/MICAz motes. It is based on the event-driven programming model, and thus follows a sense-and-send workflow model. This further supports its use in SSNs. The executable image of multiple application programs are written at the base station using neC, where the compiler creates the binary code and memory usage information, and then the rewriter converts them into a normalized code program, which can cooperate with the kernel then links them together with the kernel runtime, and then publish it to the sensor node, as shown in Figure 8.  
Figure 8. SenSmart Application development process. Figure 7. Low Level Virtualization (LLV) of SSNs.
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 4 Experiments
### Experimental Results
#### 4.2.3 Experimental Results on CelebA  
**Performance comparison**. Like the AwA2 dataset, the teacher CBM joint model achieves the highest task (y) performance on the CelebA dataset. Employing this joint model as the teacher, the proposed VICO-KD method enhances the student model's performance compared to the Vanilla-KD approach. However, the performance gain is less significant than that observed on the AwA2 dataset, indicating that the efficacy of the proposed method may vary depending on the dataset and task complexity.  
**Visual concept transfer for domain knowledge with different correlation characteristics**. We conducted experiments to validate the effectiveness of visual concept transfer in VICO-KD through concept correlation analysis. Logistic regression analysis categorized features into two groups: features with weak correlations ('Narrow Eyes', 'Bangs', 'Pale  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**AwA2**} & \multicolumn{2}{c}{**CelebA**} \\ \cline{2-5}
**Model** & **Task (y)** & **Concept (c)** & **Task (y)** & **Concept (c)** \\ \hline
**Teacher CBM** & & & & \\ Independent & 0.876 \(\pm\) 0.004 & 0.944 \(\pm\) 0.007 & 0.681 \(\pm\) 0.002 & 0.921 \(\pm\) 0.004 \\ Sequential & 0.886 \(\pm\) 0.004 & 0.945 \(\pm\) 0.003 & 0.699 \(\pm\) 0.015 & 0.919 \(\pm\) 0.007 \\ Joint & 0.889 \(\pm\) 0.005 & 0.948 \(\pm\) 0.005 & 0.743 \(\pm\) 0.017 & 0.933 \(\pm\) 0.010 \\ \hline
**Student CBM** & & & & \\ Vanilla-KD (Joint) & 0.833 \(\pm\) 0.021 & 0.935 \(\pm\) 0.001 & 0.680 \(\pm\) 0.004 & 0.878 \(\pm\) 0.003 \\ VICO-KD (Joint) & 0.862 \(\pm\) 0.006 & 0.943 \(\pm\) 0.001 & 0.683 \(\pm\) 0.004 & 0.887 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on AwA2 and CelebA.  
We performed visual concept transfer experiments by assigning concept sets with different correlation characteristics to the teacher and student models. Table 3 shows that VICO-KD outperforms Vanilla-KD across all teacher-student pair scenarios. Notably, the performance improvements in the THSE and TESH scenarios demonstrate VICO-KD's ability to effectively transfer visual cues even when concepts have weak correlations. This suggests that VICO-KD complements the relationships between weakly correlated concepts through visual features, enabling richer feature representations.
## 5 SMT-Based BMC of \(\mathsf{ELTLKD_{q}}\) Properties
### SMT Encoding of Transition Relation of FTTCS with Two Trains
#### 5.5.2. Global Action Transition Relation
Similarly, the action transitions for train \(T_{2}\) are defined as follows: \(\mathcal{T}_{T_{2}}(\overline{\mathbf{w}},\overline{a},\overline{\mathbf{w}}^{ \prime})=\) \[\big{(}s_{2}=\mathtt{away}\wedge C=0\wedge\overline{a}=\mathtt{ start}_{2}\wedge s_{2}^{\prime}=\mathtt{try}\wedge x_{2}^{\prime}=0\wedge s_{1}^{ \prime}=s_{1}\wedge C^{\prime}=2\big{)}\vee\] \[\big{(}s_{2}=\mathtt{try}\wedge x_{2}<\Delta\wedge\overline{a}= \mathtt{approach}_{2}\wedge s_{2}^{\prime}=\mathtt{wait}\wedge x_{2}^{\prime}=0 \wedge s_{1}^{\prime}=s_{1}\wedge C^{\prime}=2\big{)}\vee\] \[\big{(}s_{2}=\mathtt{wait}\wedge x_{2}>\delta\wedge\overline{a}= \mathtt{in}_{2}\wedge s_{2}^{\prime}=\mathtt{tunnel}\wedge x_{2}^{\prime}=x_{ 2}\wedge s_{1}^{\prime}=s_{1}\wedge C^{\prime}=2\big{)}\vee\] \[\big{(}s_{2}=\mathtt{tunnel}\wedge\overline{a}=\mathtt{out}_{2} \wedge s_{2}^{\prime}=\mathtt{away}\wedge x_{2}^{\prime}=x_{2}\wedge s_{1}^{ \prime}=s_{1}\wedge C^{\prime}=0\big{)}\]
* Train \(T_{2}\) starts: \[(s_{2}=\mathtt{away}\wedge C=0\wedge\overline{a}=\mathtt{start}_{2})\Rightarrow( \overline{\mathbf{w}}_{T_{1}}^{\prime}=\overline{\mathbf{w}}_{T_{1}},\overline {\mathbf{w}}_{T_{2}}^{\prime}=(\mathtt{try},0),C^{\prime}=2)\] This transition occurs when \(T_{2}\) starts, moving to the \"try\" state, resetting its clock, and updating the controller to 2.
## 2 Problem Statement
### Problem Statement  
Suppose the UAV operation period is \([0,T]\). The UAV's initial position is \(p(0)\) and the initial energy is \(Q(0)\). Let \(\mathcal{D}\) denote a given bounded and connected destination set. Let \(T^{*}\) denote the time instant at which the UAV arrives at \(\mathcal{D}\). In other words, \(T^{*}\) is the time needed to complete the flight. During the period \([0,T^{*}]\), the UAV transmits confidential data to the intended ground node if the eavesdropping can be avoided. The path planning problem under investigation is to jointly optimize three objectives: minimizing \(T^{*}\), maximizing the final residual energy \(Q(T^{*})\) and maximizing the total time the UAV can securely transmit data by finding a sequence of control inputs \(v(t),\omega(t),u(t)\). Formally, the problem is formulated as follows:  
\[\min_{v(t),\omega(t),u(t)}T^{*} \tag{14}\]  
\[\max_{v(t),\omega(t),u(t)}Q(T^{*}) \tag{15}\]  
\[\max_{v(t),\omega(t),u(t)}\int_{0}^{T^{*}}I_{0}(t)dt \tag{16}\]  
subject to  
\[Q^{min}\leq Q(t)\leq Q^{max},\ \forall t\in[0,T], \tag{17}\]  
\[(x(T),y(T))\in\mathcal{D}, \tag{18}\]  
Equations (3), (12) and (13) hold, while (7) does not hold for any prism at any time.  
Different from the classic path planning problem which targets on a collision-free path for a mobile robot to safely and quickly arrive at the destination [35], the problem of interest also aims at maximizing the residual energy of the solar-powered UAV and total time duration in which the UAV can securely communicate with the intended node.
## 1 Introduction
### Contribution of the Present Work  
The aim of implementing an autoencoder (AE) model is to reconstruct the input data. However, the literature has shown that hybrid models yield superior performance. For this reason, this work implements LSTM and Multihead Attention (MA) layers in the AE architecture, specifically in the encoding and decoding layers, to capture more complex temporal relationships.  
Specifically, we propose an architecture for fault prediction in WT converters using SCADA data, with the objective of improving reliability and configurability through fault prediction. This study implements an unsupervised learning approach using an LSTM-AE with multihead attention (LSTM-MA-AE), which incorporates temporal features from SCADA data.  
This model is evaluated on a real dataset from a wind farm located in a complex terrain in southern Ecuador. We evaluate it with different architecture configurations to measure its performance in anomaly detection.  
Therefore, the main contributions of this research are:  
* Development of a DL model that integrates autoencoders, LSTM networks, and MA for fault detection in WT converters.
* Converter fault detection using SCADA data from a wind farm in complex terrain.
* A system of prediction of converter failures in real WT operating conditions using unsupervised learning.
# Fast Prediction of Characteristics in Wound Rotor Synchronous Condenser Using Subdomain Modeling
## 4 Conclusions  
Overall, the proposed mathematical method offers a fast and effective approach for characterizing WRSC properties, including flux density, induced voltage, inductance, and compounding curves. Under low rotor and stator current conditions, the subdomain method aligns well with 2-D FEM results. However, as rotor and stator currents increase, saturation can induce errors of up to 10% in the flux density and induced voltage results. The unity-power-factor compounding curves were accurate across varying conditions. These results were obtained because of the unity power factor and suppression of demagnetization by the d-axis stator current, which weakens flux linkage and limits saturation.  
Compared to 2-D FEM simulations, the mathematical method cut computation time from 66 to 33 s, facilitating faster optimizations in the WRSC design process. The reduced time can lead to exponential efficiency gains, enabling designers to employ optimization techniques more effectively.  
Future research based on these findings could explore:  
* Nonlinear magnetic material characteristics;
* Deriving leading and lagging compounding curves for power factor;
* Optimization techniques for a more efficient WRSC design;
* Experimental verification of the subdomain method on a test bench.  
Conceptualization, M.-D.N., T.-S.K., K.-H.S., G.-H.J. and J.-Y.C.; methodology, M.-D.N.; validation, M.-D.N.; writing--original draft preparation, M.-D.N. and T.-S.K.; writing--review and editing, [PERSON]-H.S., [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-H.S. and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (20223A10100040, Development of design technology for converting retired thermal plant into synchronous condenser plant).  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.  
Figure 5: Compounding curves of the calculation and the FEM under different conditions of maximum stator voltage.
## 5 Discussion
### Limitations of STPC
#### 5.1.3 Initial Cost and Development Overhead  
While STPCs can reduce line installation and transformer costs in certain cases, the initial hardware and development costs may be higher due to the need for high-performance power semiconductors, control circuitry, and thermal management. These costs are especially significant at the early deployment stage before mass production.
# Research on Multi-Physical Field Coupling Simulation of Local Electrochemical Machining
## 4 Experimental Verification
### Experimental Result  
Figure 16a,b show the results of local electrochemical machining when \(L\) = 3.3 mm and 3.7 mm, respectively. For \(L\) = 3.3 mm, there will be a ring of protrusion around the machined convex and there is an obvious joint mark. For \(L\) = 3.7 mm, there is no obvious joint mark. Figure 17 shows the contour comparison of the experimental contour measured by a three-dimensional measuring machine, multi-physical field coupling simulation, and pure electric field simulation. It can be seen from the enlarged view at the joint area that there is a large error between the pure electric field simulation contour and the experimental contour, and the maximum error is 0.28 mm and 0.32 mm, respectively. The contours of the multi-physical field coupling simulation results are basically consistent with the experimental contours, and there are slight deviations (<0.06 mm). Considering the errors of cathode fabrication and experimental installation, the correctness of the multi-physical field coupling simulation results can be verified.  
Figures 18 and 19 are the convex comparison before and after local electrochemical machining with GOM scanning, respectively. The legend represents the distance from the center wheelbase. From the 3D view, it can be seen that the sidewall taper of the convex improved significantly. From the front view, the convex sidewall taper changes from \(-8^{\circ}\) to 0.15\({}^{\circ}\), and as you can see from the side view, the convex sidewall taper decreases from 14.5\({}^{\circ}\) to 0.25\({}^{\circ}\), reaching the target's sidewall taper requirements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Material of anode workpiece & GH4169 \\ Material of cathode tool & Stainless steel 304 \\ Electrolyte & 20\%NaNO\({}_{3}\) \\ Pressure of electrolyte (MPa) & 0.4 \\ Applied voltage (V) & 20 \\ Feed speed (mm/min) & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 16: Local electrochemical machining experimental results: (**a**) \(L\) = 3.3 mm, (**b**) \(L\) = 3.7 mm.  
Figure 17: Comparison of simulated contour and test contour: (**a**) \(L\) = 3.3 mm, (**b**) \(L\) = 3.7 mm.  
Figure 18: The original convex of the GOM scan: (**a**) 3D view, (**b**) front view, and (**c**) lateral view.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 3 Materials and Methods
### FCM-Based Feature Selection  
In order to extract the most significant features using FCM, a causal relationship graph is constructed with the features as the nodes of the graph. The presence of a specific feature is indicated by a positive value in the state vector and the absence of the feature is indicated by 0. A correlation matrix is used to initialize the weight matrix of the fuzzy cognitive map. During FCM learning process in each iteration different combinations of features in the state vector is evaluated. The feature set that provides the best level of classification accuracy for the test data is chosen at each iteration and permanently added to the subsequent state vector. Classification accuracy (CA) is computed as a proportion of the accurately classified test instances:  
\[\text{Accuracy}=\frac{\text{Successfully classified test cases}}{\text{Total number of test cases}}*100 \tag{5}\]  
After the feature has been selected and added to the state vector, the same test is applied for the combinations of remaining features with the selected feature affixed at a particular position. As long as the target performance level is met or all features have been selected, these iterations are continued. Let F = {f1, f2, f3, f4,..., fn} be the set of all features in the dataset, S = {\(\left\lvert\mathcal{O}\right\rvert\) be initial set of selected features, A = {A1, A2, A3,..., An} be the state vector, and M be the weight matrix for the FCM. In each iteration, a different set of feature values in the state vector A are set to 1 and others are set as 0, for example, A = {01001010001}. The objective function for the FCM model is the maximization of the classification accuracy. The FCM model is trained using different combinations of the state vector A and the classification accuracies are computed based on Equation (5). The feature set A that gives the highest classification accuracy is selected and added to the selected feature set S. The iterations are continued until either the desired classification accuracy has been achieved or all the features in F have been selected at least once.
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy (FTIR) Characterization  
The absorption, emission, and photoconductivity spectrum of solids, liquids, and gases are obtained using the Fourier-transform infrared spectroscopy (FTIR) technique. Between 4000 and 400 cm\({}^{-1}\), the range is captured. The curves plotted between transmittance vs. wave number were used to identify the different functional groups in the various foams using a Nicolet NEXUS-670 FTIR instrument. Figure 4 compares the FTIR graph of non-reinforced flexible bio-based polyurethane foam (A0) and the best sample (A13), nanoparticle-reinforced flexible bio-based polyurethane foam. Here, the presence of  
Figure 3: FESEM characterization of (**a**) Open cellular pores of bio-based flexible polyurethane foam, (**b**) Graphite nanoplates, (**c**) Zirconium oxide nanoparticles, (**d**) Bamboo charcoal nanoparticles, (**e**) Nanoparticle-reinforced flexible bio-based polyurethane foam.  
carboxylic acid OH stretch is confirmed in non-reinforced foam around 3000-2800 cm\({}^{-1}\) from stretching the peaks. N-H stretches of amines are present in the range of around 3300 cm\({}^{-1}\)[53]. The asymmetric and symmetric bending vibrations of the HO-H produce peaks at 1648 cm\({}^{-1}\) and 1381 cm\({}^{-1}\). The carbonyl peak was found at 1724 cm\({}^{-1}\), with the isocyanate peak (-NCO) disappearing at around 2270-2250 cm\({}^{-1}\). There was no peak of hydroxyl at 3450 cm\({}^{-1}\), indicating that all isocyanates contained in the pre-polymer constituents were fully used. While comparing the graph in Figure 4, the addition of nanoparticles to the bio-based polyurethane foam is readily seen. The medium-intensity sp2-hybridized C=C bonding in the 1680-1600 cm\({}^{-1}\) range validates GNP. Three strong absorption bands are seen at wave numbers of 3380, 1650, and 550 cm\({}^{-1}\) in tetragonal ZrO\({}_{2}\) (Figure 4, blue line). The tension vibration of the hydroxyl groups on the ZrO\({}_{2}\) surface produced two bands at 3380 and 1650 cm\({}^{-1}\). The specimen contains other nanoparticles besides the GNP, as seen in the FTIR graph. The results are ambiguous, and there is a lot of noise due to the presence of alkanes and alkynes in the diverse compound mixture, indicating the presence of BC and GNP in the composite.
## 5 Hardware System Design
### Temporal Parallelization
Total latency of stereo-matching with \(n_{T}\) number of target micro-images, without the pipelining is \(N*n_{T}\) clock cycles, and with pipelining it equals to \(N~{}+~{}ii(n_{T}-1)\) clock cycles. Thereby, gain obtained from temporal parallelization is restricted by the value of _ii._ In stereo-matching, to save computation, a condition is added which ensures that matching only occurs with \(I_{ti}\) that are placed at higher distance (\(l_{n}>1.73\)) if a match is found at the shorter distance. Thus, in a case, when no match is found at the shortest distance, search stops and the next reference pixel is selected. This however, results in inter-iteration (loop-carried) dependency that consequently increases _ii._  
Figure 9: Micro-images grid including reference and target micro-images placed at different baseline distances.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Relevant 4G LTE Performance Metrics  
The performance of a 4G LTE MNO, such as the Etisalat LTE network, can be monitored via a plethora of metrics. However, while some metrics directly impact the end user's experience and the QoS, some are not as valuable in determining the performance and possible optimization of the network. For better optimization, a few of the most important metrics are considered in this study, and a handful of them are optimized. The KPIs considered are presented in Sections 3.2.1-3.2.5.
## 2 Navigation Sensors and Techniques for Drones
### Vision-Based Sensors  
The two most popular forms of vision-based navigation are visual odometry (VO) and SLAM. VO is the process of estimating the egomotion of the drone using the camera attached to it [7]. SLAM uses a camera to build a map of its surroundings, and determine its location [8]. Although these methodologies are effective in localization, they require a lot of computational load and an onboard vision sensor. Furthermore, although they provide position estimates, they are local coordinates and cannot provide absolute positions unless there is a reference point.
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
###### Abstract  
A tightly coupled fluid-structure interaction (FSI) methodology is developed for aeroelasticity analysis in the time domain. The preconditioned Navier-Stokes equations for all Mach numbers are employed and the structural equations are tightly coupled with the fluid equations by discretizing their time derivative term in the same pseudo time-stepping method. A modified mesh deformation method based on reduced control points radial basis functions (RBF) is utilized, and a RBF based mapping algorithm is introduced for data exchange on the interaction interface. To evaluate the methodology, the flutter boundary and the limit cycle oscillation of Isogai wing and the flutter boundary of AGARD 445.6 wing are analyzed and validated.  
CFD/CSD coupling; flutter boundary; computational aeroelasticity +
Footnote †: journal: Applied Sciences