esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Definition
### Ontology Evolution and Scalability  
In real-world scenarios, IoT systems are dynamic, with new devices, sensors, and metrics frequently introduced. This dynamic nature requires the ontology \(O\) to evolve without disrupting the existing system. Furthermore, the architectural design will have to enable efficient scalability, supporting growing numbers of edge servers and IoT devices with no loss in performance and interoperability.  
**Formal Definition**: Given an initial ontology \(\mathcal{O}\), the task is to iteratively update \(\mathcal{O}\) with new attributes or schemas \(\Delta\mathcal{O}\) while ensuring backward compatibility as follows:  
\[O_{\text{new}}=O+\Delta\mathcal{O},\quad\text{where }\Delta\mathcal{O}=\left\{o_{1},o_{2}, \ldots,o_{k}\right\} \tag{3}\]  
The system must maintain the integrity of the current mappings \(M\) while propagating \(O_{\text{new}}\) to all \(m\) edge servers.  
An ontology in the context of our work is formally represented as a triplet \(\mathcal{O}=(C,R,A)\), where the following holds:  
* \(C\) is the set of concepts (or classes) that define the entities in the domain.
* \(R\) is the set of relationships between these concepts, specifying how they interact.
* \(A\) is the set of axioms and constraints that govern the structure and integrity of the ontology.  
For example, in the case of IoT interoperability, an ontology can represent device types (e.g., sensors and actuators) as classes in \(C\), their interactions (e.g., \"measures\" and \"controlledBy\") as relations in \(R\), and data consistency rules (e.g., \"temperature readings must be in \"C or \"F\") as axioms in \(A\).
## 5 The Experiment and Analysis  
To prove the effectiveness of KMVMD-PGMCKD, bearing rolling element fault signals from Case Western Reserve University and bearing rolling element fault signals from QPZZ-\(\  
The bold values in Table 1 are the kurtosis values used to compute the mean value. According to the data, the IMF with a larger kurtosis value is selected for signal reconstruction when \(k=6\), which are IMF3, IMF4, and IMF6. The power spectrum of the reconstructed signal is given in Figure 2.  
It can be clearly found that 1/3 harmonic of fault frequency (46.88 Hz) and rotating frequency (29.2 Hz) are obvious in Figure 2, which are better than the original signal without processing. However, since the fault frequency is not found directly in Figure 2, in order to obtain a better result, the reconstructed signal will be further processed by PGMCKD.  
Firstly, the calculated theoretical value of \(T\) is 85 according to Equation (10), so the search range of \(T\) is set [83; 87], the step size is 1, the search range of \(L\) is set [100; 500], its step size is 5. Based on the evaluation criterion of power spectrum entropy, the optimized parameter by the grid search method is [\(T\), \(L\)] = [87; 460]. The power spectrum of the deconvoluted signal by PGMCKD is given in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**k**} & \multirow{2}{*}{**Kurtosis**} & \multirow{2}{*}{**IMF1**} & \multirow{2}{*}{**IMF2**} & \multirow{2}{*}{**IMF3**} & \multirow{2}{*}{**IMF4**} & \multirow{2}{*}{**IMF5**} & \multirow{2}{*}{**IMF6**} & \multirow{2}{*}{**IMF7**} & \multirow{2}{*}{**Kurtosis**} \\  & & & & & & & & & **Mean** \\ \hline
2 & 3.0346 & **3.0406** & & & & & & 3.0406 \\
3 & **3.0427** & **3.1279** & 3.0361 & & & & & 3.0853 \\
4 & 2.5209 & 2.118 & **3.1205** & **3.0365** & & & & 3.0785 \\
5 & 2.519 & 2.1155 & **3.0974** & **3.4117** & **2.7499** & & & 3.0863 \\
6 & 2.3611 & 2.9584 & **3.1004** & **3.4003** & 2.7476 & **3.1516** & & **3.2174** \\
7 & 2.3452 & 3.0009 & 1.9672 & **3.0901** & **3.4083** & 2.7464 & **3.1055** & 3.2013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kurtosis and kurtosis mean values of each IMFs with different \(k\).  
Figure 3: Power spectrum of element fault signal by KMVMD-PGMCKD.  
Figure 2: Power spectrum of reconstructed signal.  
From Figure 3, it can be clearly found that 1/5 harmonic of the rotating frequency (5.859 Hz), the fault frequency \(f_{0}\) (140.6 Hz), and its second harmonic (281.3 Hz) are obvious. It can be seen that the KMVMD-PGMCKD can accurately extract fault features of the bearing rolling element signal.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Research Challenges and Future Work
#### 6.2.3 Hybrid System  
Future applications will need to support positioning in both indoor and outdoor environments, ideally using the same user terminal, such as a mobile device. GPS services are already widely used for outdoor positioning, while VLP (visible light positioning) systems are well suited for indoor services. Hybrid systems that combine VLC-based technologies with WiFi, Bluetooth, cellular networks, and others to provide seamless and reliable location services in all environments would also be very practical. The advantage of hybrid systems is that they allow for a seamless transition between indoor and outdoor environments. For example, users can use GPS to navigate outdoors, and, when they enter a building, the system automatically switches to VLP or WiFi positioning, ensuring that users always have access to highly accurate location services.  
In addition, by combining different positioning technologies, the hybrid system can significantly improve positioning accuracy and robustness and adapt to complex environmental changes and different usage scenarios, providing a better user experience and a wider range of applications. In this way, the hybrid system not only improves the reliability and accuracy of location services but also provides a solid foundation for future smart cities, intelligent transportation, and other IoT applications.
# An FPGA Implementation of a Convolutional Auto-Encoder
## 3 Experimental Results and Discussion
According to the device utilization summary given by the design suite, the utilization of the main hardware resources is shown in Table 1. With the numbers of network layers and weight parameters set at 13 and 68,227, respectively, we can see that the main resources, such as LUT and FF, take a very small percentage. Our CPU platform is Intel i7-6700K CPU (Intel, Santa Clara, CA, USA) at 4 GHz with a 16 GB RAM. The GPU platform is NVIDIA GTX TITAN Xp (NVIDIA, Santa Clara, CA, USA). A 150 MHz system clock is used for the framework on FPGA. In the experiment, \"time.clock()\" instruction of Keras is used to obtain the computing time while running on either CPU or GPU. The computing time of FPGA is calculated by counting the number of clock cycles consumed from input to output. As for the power consumptions of GPU, CPU, and FPGA, they are acquired from the \"nvidia-smi\" command provided by NVIDIA, the electro-instruments, and the power analyzer integrated in Vivado2016.2, respectively. A performance comparison of FPGA, CPU, and GPU is shown in Table 2.  
As the experimental results show, in terms of compression speed, it consumes 15.73 ms running on FPGA and 115.29 ms running on CPU. In terms of the computing performance per watt, it is 26.49 GOPS/W running on FPGA, which is far beyond that of CPU and GPU.
## 5 Results and Discussion
### AUC-ROC Performance  
All models achieved perfect AUC-ROC scores (1.0000), indicating optimal discrimination between wildfire and non-wildfire instances (Figure 8). This metric confirms the models' exceptional ability to rank positive instances higher than negative ones, regardless of the specific classification threshold selected.
## 2 Basic Theory
### Experimental Analyses
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{2}{c}{**Attribute Index**} \\ \cline{2-3}  & **RS** & **NRS** \\ \hline Wine & [13, 4, 10, 3, 1, 12, 8, 11, 2] & [13, 10, 6, 7, 12] \\ Iono & [1, 5, 6, 12, 32, 29, 8, 33, 7, 20, 17, 33, 34] & [1, 5, 19, 30, 16, 31, 6, 3] \\ Sonar & [54, 19, 45, 35, 27, 23, 20, 29, 24, 16] & [44, 12, 21, 31, 24, 1] \\ Glass & [7, 3, 8, 5, 2, 4, 1, 9] & [8, 4, 7, 5, 9, 2, 3, 1, 6] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Attribute Reduction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} \\ \cline{2-6}  & & **CART** & **KNN** & **SVM** & **DOA-SVM** \\ \hline Wine & RS & 90.33 & 94.41 & 94.01 & 95.07 \\ Wine & NRS & 91.33 & 96.08 & 96.48 & 97.32 \\  & Raw & 90.11 & 94.93 & 97.18 & 97.89 \\  & RS & 85.6 & 84.32 & 86.33 & 87.18 \\ Iono & NRS & 90.51 & 86.32 & 91.03 & 92.06 \\  & Raw & 87.71 & 85.18 & 89.69 & 90.96 \\  & RS & 70.62 & 69.67 & 75.18 & 84.33 \\ Sonar & NRS & 69.48 & 72.04 & 71.68 & 74.88 \\  & Raw & 71.7 & 78.8 & 75.3 & 75.9 \\  & RS & 59.91 & 55.60 & 59.64 & 61.7 \\ Glass & NRS & 66.00 & 63.08 & 66.61 & 69.59 \\  & Raw & 68.00 & 63.08 & 67.33 & 69.01 \\  & Average Value & 78.44 & 78.63 & 80.87 & 82.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification algorithm accuracy. significantly better than the other three classification algorithms, and this result highlights the superiority of the DOA-SVM model proposed in this paper.
# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 5 Results and Discussion
### Comparison of Performances with Baselines
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SemEval 2015**} & \multicolumn{3}{c}{**SemEval 2016**} \\ \hline \multirow{2}{*}{**Methods**} & **BERT** & **DistilBERT** & **RoBERTa** & **BERT** & **DistilBERT** & **RoBERTa** \\  & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** & **Avg \(\pm\) Std** \\  & **(Best)** & **(Best)** & **(Best)** & **(Best)** & **(Best)** & **(Best)** \\ \hline No-DA & 80.29 \(\pm\) 0.41 & 78.22 \(\pm\) 0.12 & 81.48 \(\pm\) 0.44 & 83.91 \(\pm\) 0.57 & 85.58 \(\pm\) 0.12 & 85.60 \(\pm\) 0.33 \\  & (80.72) & (78.39) & (81.94) & (84.49) & (85.76) & (85.95) \\ EDA & 80.22 \(\pm\) 0.85 & 78.28 \(\pm\) 0.42 & 81.71 \(\pm\) 0.47 & 84.51 \(\pm\) 0.35 & 83.14 \(\pm\) 0.16 & 85.29 \(\pm\) 0.46 \\  & (81.08) & (78.72) & (82.19) & (84.88) & (83.30) & (85.77) \\ EDA-w2v & 80.45 \(\pm\) 0.74 & 79.67 \(\pm\) 0.55 & 82.16 \(\pm\) 0.12 & 85.53 \(\pm\) 0.88 & 83.12 \(\pm\) 0.67 & 88.3 \(\pm\) 0.59 \\  & (81.19) & (80.24) & (82.28) & (86.49) & (83.82) & (88.97) \\ IG & 80.34 \(\pm\) 0.98 & 79.81 \(\pm\) 0.14 & 81.64 \(\pm\) 0.64 & 85.89 \(\pm\) 0.84 & 83.08 \(\pm\) 0.21 & 86.87 \(\pm\) 0.97 \\  & (81.35) & (79.97) & (82.29) & (86.78) & (83.30) & (87.79) \\ IG-w2v & 80.68 \(\pm\) 0.81 & 81.40 \(\pm\) 0.46 & 81.59 \(\pm\) 0.74 & 86.92 \(\pm\) 1.32 & 83.16 \(\pm\) 0.42 & 88.87 \(\pm\) 0.79 \\  & (81.40) & (81.88) & (82.36) & (88.21) & (83.61) & (89.69) \\ \hline TAWC & 81.96 \(\pm\) 0.48 & 81.13 \(\pm\) 0.77 & 83.58 \(\pm\) 0.48 & 88.97 \(\pm\) 0.85 & 86.14 \(\pm\) 0.32 & 89.67 \(\pm\) 0.61 \\  & (82.58) & (81.90) & (83.92) & (90.15) & (86.46) & (90.37) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average accuracies across both datasets. ensures that relevant synonyms are used to replace augmented sentences. In addition, the model tends to generalize well due to the synthetic samples managing the balance class of the training set.
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 4 Discussion
However, our study only included three typical machine-learning models, more models with different complexities could be analyzed by future studies to further validate our findings.
# Efficiency Estimation of Roller Chain Power Transmission System
###### Abstract  
In the present study, a novel approach to estimating the efficiency of roller chain power transmission systems is proposed based on sliding friction losses and damping force. The dynamics model is taken into account between chain links with lateral offset owing to the derailleur system. Frictional losses were calculated according to Coulomb's law of friction, and the damping force was dependent on the damping coefficient. The effects of rotational speed, load, derailleur system, and damping coefficient on transmission efficiency were analyzed. The test stand of the roller chain power transmission system was set up to verify the estimated efficiency, and the results showed a good correlation, demonstrating the validity of the chain power transmission efficiency estimation.  
roller chain; chain efficiency; sliding friction; lateral offset; damping force +
Footnote †: journal: Applied Sciences
# PID Control Assessment Using L-Moment Ratio Diagrams
## 2 Applied methods
### L-Moment Ratio Diagrams  
The notion of L-moments was introduced by [PERSON] [24]. As L-moments extend the background idea of moments, it was natural to introduce the LMRD as an extension of the idea of MRDs. The LMRD is a popular tool in extreme value analysis (EVA). They allow identifying the proper distribution for empirical observations. The plot that relates L-kurtosis \(\tau_{4}\) to L-skewness \(\tau_{3}\) denoted as LMRD(\(\tau_{3},\tau_{4}\)) is the most common variant. By analogy to MRDs, we may compare any empirical data against various theoretical distribution functions [34].  
Similarly to MRDs (\(\gamma_{2}\), \(\gamma_{3}\)), there are two LMRD variants and the LMRD(\(\tau_{2}\), \(\tau_{3}\)) is also used. This research suggests also using the third type of diagram, i.e., the LMRD(\(l_{2}\), \(\tau_{4}\)). It is shown that its properties are more useful in the control engineering context.  
The idea behind using LMRDs rests on the fact that there exists an assumption that properly controlled variables should meet Gaussian properties. This means that the process is linear, symmetric, stationary, all the disturbances are properly decouples, and there are no extraordinary human interventions, like manual control mode. In such a case, one expects that the associated points on the LMRD(\(\tau_{3},\tau_{4}\)) diagram will be close to the point \((0,0.1226)\), and in LMRD(\(\tau_{2}\), \(\tau_{3}\)), this is as close as possible to the origin \((0,0)\) and \(\tau_{3}\approx 0\).
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 1 Introduction
Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulations prove that, compared with existing schemes, the proposed scheme can obtain more accurate sensor node location in less time. The main contributions of this paper are twofold:  
* To improve location accuracy, we design an SVR based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. This method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. * To shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. Compared with existing schemes, the proposed scheme only needs one trajectory of the mobile anchor node to locate sensor nodes, which significantly shortens the location time. The remainder of this paper is organized as follows. Section 2 discusses the related work. In Section 3, we describe the details of the proposed localization scheme. Performance evaluations by simulation are presented in Section 4, followed by the conclusion and future work in Section 5.
## 3 Digital Implementation
### Neuron Implementation  
In this study, the Izhikevich neuron model is used to realize the neural network. The digital implementation is shown in Figure 3. In Figure 3a, the blocks \"Reg\" and \"ADD\" represent the register and adder, and \"SUB\" stands for the subtractor. The block \"MUL\" represents the multiplier which is realized based on the CORDIC algorithm in this study. For the concrete implementation of an iteration of the CORDIC algorithm refer to Figure 3b, where \"MUX\" is the multiplexer. The input \(X_{i}\), \(Y_{i}\), \(Z_{i}\) are the input values of the iteration, and \(X_{i+1}\), \(Y_{i+1}\), \(Z_{i+1}\) are the updated results of the \(i+1\) iteration. The variable is the iteration parameter of the iteration, and the specific values are based on the functions that the CORDIC algorithm implements. The CORDIC algorithms with different iterations can meet different precision requirements. The CORDIC algorithm can achieve more precision by increasing the iteration number. In this study, the CORDIC algorithm uses 21 steps of iteration.
## 4 Experimental Stations
### 2D X-ray Detectors  
#### 4.3.1 Overview of Current Detector Developments for XFELs  
Several dedicated detection systems are under development for photon science at XFELs. Importantly, single photon counting detectors (like PILATUS [188], EIGER [189] or Medipix [190]) are not suitable for the high photon fluxes at XFELs, and the use of charge-integrating detection systems becomes crucial. Each of the following new detection systems for XFELs is tailored to meet the specific requirements of their respective light source. These detector systems comprise the Cornell-SLAC Pixel Array Detector (CSPAD) [191] and the new ePix family [192] for the SLAC Linac Coherent Light Source, the Adaptive Gain Integrating Pixel Detector (AGIPD) [193], the Large Pixel Detector (LPD) [194] and the DEPFET Sensor with Signal Compression (DSSC) [195] for the European X-ray Free Electron Laser and the Silicon-On-Insulator PHoton Imaging Array Sensor (SOPHIAS) [196] for the SACLA FEL.  
None of these detection systems is capable (Table 4) of meeting the specific needs of the Aramis beam line and its user stations Alvra and Bernina. In particular, no detector system can provide at the same time single photon resolution at 2 keV photon energy and a dynamic range suitable for crystallography experiments. Therefore, a dedicated, state-of-the-art application-specific integrated circuit (ASIC) and detector system named JUNGFRAU (adJUstiNg Gain detector FoR the Aramis User station) has been developed in house [161].
## 3 Challenges
### Resource Constraints  
As in WSNs, sensor nodes are usually low-cost, low-power, small devices that are equipped with only limited data processing capability, transmission rate, battery energy, and memory. For example, the MICAz more from Crossbow is based on the Atmel ATmega128L 8-bit microcontroller that provides only up to 8 MHz clock frequency, 128-KB flash program memory and 4-KB EEPROM; the transmit data rate is limited to 250 Kbps. Due to the limitation on transmission power, the available bandwidth and the radio range of the wireless channel are often limited. In particular, energy conservation is critically important for extending the lifetime of the network, because it is often infeasible or undesirable to recharge or replace the batteries attached to sensor nodes once they are deployed. Actuator nodes typically have stronger computation and communication capabilities and more energy budget relative to sensors. Resource constraints apply to both sensors and actuators, notwithstanding.  
In the presence of resource constraints, the network QoS may suffer from the unavailability of computing and/or communication resources. For instance, a number of nodes that want to transmit messages over the same WSAN have to compete for the limited bandwidth that the network is able to provide. As a consequence, some data transmissions will possibly experience large delays, resulting in low level of QoS. Due to the limited memory size, data packets may be dropped before the nodessuccessfully send them to the destination. Therefore, it is of critical importance to use the available resources in WSANs in a very efficient way.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetic Particle Imaging (MPI)  
MPI technology was developed more than 10 years ago and is still evolving in the biomedical field [178]. The main relevant features of this dynamic domain are represented by harmless, cost-effective, and high-precision detection when combined with computed tomography [179]. MPI is based on the creation and superimposition of a static selection field and an oscillatory drive field by evaluating the non-linear magnetization properties of MNPs. For this specific type of application, the SPIONs represent the most suitable class of MNPs [180].  
This technique acts as a true helper in diagnosing and treating various complex diseases, especially from the oncological and surgical field. One of the in vivo applications is represented by the quantitative analysis of MNPs by determining their concentration and distribution, which can improve imaging quality [181].  
By combining MPI with MRI, one can investigate the molecular basis of some treatment mechanisms (Vivotrax from Magnetic Insight company, Alameda, CA, USA) [182]. Further examples can also be found in Table 1. The clinical translation of MNP-based imaging techniques is underway, with ongoing research focusing on safety, efficacy, and regulatory approval [183]. Continued research and development in this field holds great potential for advancing diagnostics, therapeutics, and personalized medicine.
## 4 Confirmation of Conventional Parameters in the DIC for an Optimized Measurement
### Mesh Density  
In DIC, a field pattern is observed based on the CC computation of each subset. Hence, the DIC quality should be highly controlled by mesh density. Particularly, small-sized objects like cracks call for denser mesh to observe the crack region in images. Note here that in addition to the acquisition of a crack pattern, the mesh density should also be high enough to realize the crack thickness mesh-independent.  
Figure 11 describes volumetric stains in four mesh-density cases. First to be demonstrated, in the PhotoInfor, the standard mesh mode is achieved by dividing squares, and the mesh density is consequently determined by the distance between adjacent subsets. Clearly, case (a), characterized by a sparse mesh, is insufficient to effectively capture the crack pattern as it is barely observable, and the thickness is fully mesh-dependent (thickness equals mesh spacing). With an increase in density, case (b) is capable of depicting a more distinct pattern, encompassing the crack length and its overall distribution. Nevertheless, the thickness measurement is somewhat reliant on the mesh spacing, which is not entirely satisfactory. To address this, we conducted cases (c) and (d) with the aim of optimization for a more thorough comparison. The results indicate that computations with mesh spacings of 5 pixels and 2 pixels each yield satisfactory outcomes. Furthermore, to take into consideration the analysis speed, a mesh density of five pixels is suggested in this problem with respect to the DIC reorganization.  
Figure 10: Comparison of volume strain diagrams varied with four selected subset radii. (**a**) Subset radius is set to 2 pixels. (**b**) Subset radius is set to 6 pixels. (**c**) Subset radius is set to 10 pixels. (**d**) subset radius is set to 14 pixels.  
According to the discussion in the former, we aim to propose a recommended guideline for mesh placement in DIC studies. In this scenario, the average width/thickness of the cracks generated is gauged to be approximately 3 pixels. Hence, to be concluded from Figure 11, the mesh density (spacing) should be controlled within twice the pattern size. Nevertheless, employing a high-density mesh across the entire field can lead to substantial computational demands, a cost that, while significant, is unavoidable. Thus, localized treatment with high-density mesh could be an intelligent way. Fortunately, PhotoInfor provides an alternative solution to create mesh imported from third-party software, through which mesh density can be customized for any desired effect.