esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background  
### The Hard-Voting and Soft-Voting Method  
The voting method is the most widely used and important classifier-fusion method. Suppose that the combined classifier consists of \(T\) single classifiers \(h_{i}\) (_opposite_ = 1, 2,..., \(T\)). The learner \(h_{i}\) will predict the sample label from the label set \(\{c_{1},\,c_{2},\,\ldots,\,c_{N}\}\). The fusion method needs to combine the decision results of \(T\) classifiers and output the final label. Suppose that the output of classifier \(h_{i}\) for the input sample \(x\) is a \(Q\)-dimensional vector \((h^{1}{}_{i}(x),\,h^{2}{}_{i}(x),\,\ldots,\,h^{N}{}_{i}(x))^{T}\), where \(h^{j}{}_{i}(x)\) represents the output of classifier \(h_{i}\) that belong to the \(j\)th class. The value of \(h^{j}{}_{i}(x)\) has different types, such as the binary label and the probability label.  
The voting method using the binary label is also called hard voting. The binary labels can be combined through the majority voting or the weighted voting method ([PERSON] and [PERSON], 2018).  
* The majority voting method can be described as follows: \[H(x)=\operatorname*{arg\,max}_{j}(\sum_{i=1}^{T}h^{j}{}_{i}(x)),h^{j}{}_{i}(x) \in\{0,1\}\] (1)
* The majority voting method is suitable for all base learners with the same performance. However, in practical applications, the performance of the base learners is different, which requires different weights. Therefore, weighted voting came into being. \[H(x)=\operatorname*{arg\,max}_{j}(\sum_{i=1}^{T}w_{i}\times h^{j}{}_{i}(x)),h^{ j}{}_{i}(x)\in\{0,1\}\] (2) where \(w_{i}\in[0,\,1]\) indicates the weight of classifier \(h_{i}\) and a large weight means stronger classification performance.  
If the output of a classifier is the class posterior probability, the base classifiers can be combined by the soft-voting method. There are two appropriate types of weight coefficients in the soft-voting method, including the classifier level and the class level [13].
* The classifier-level weight represents the weight for each base classifier. The final output can be described as follows: \[H(x)=\underset{j}{\text{arg max}}(\sum_{i=1}^{T}w_{i}\times h^{j}_{i}(x)),\,h^{j }_{i}(x)\in[0,1]\] (3)
* The class-level weight represents the weight of each base classifier for an output class. This method considers that the classifier has different prediction performance with different output classes. The final output can be described as follows: \[H(x)=\underset{j}{\text{arg max}}(\sum_{i=1}^{T}w_{i}^{j}\times h^{j}_{i}(x)), \,h^{j}_{i}(x)\in[0,1]\] (4) where \(w_{i}^{j}\in[0,\,1]\) represents the weight of class \(j\) for classifier \(h_{i}\).
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 4 Multiple Damage Location Detection  
### Multiple Damage Dataset  
In the multiple damage location detection, stiffness of the damaged column was reduced by 20%. The multiple damage dataset includes five categories: \"c0\", \"c1c6\", \"c1c11\", \"c6c11\", and \"c1c6c11\". \"c0\" means that there are no damaged columns; \"c1c6\" means that the damaged columns are column 1 and column 6; \"c1c11\" means that the damaged columns are column 1 and column 11; \"c6c11\" means that the damaged columns are column 6 and column 11; \"c1c6c11\" means that the damaged columns are column 1, column 6, and column 11. The data in the dataset were obtained from the finite element model. The specific parameters in the finite element model were set according to the damage types mentioned above. Displacement of each node in the finite element model under environmental excitation was collected. Meanwhile, the displacement of each node was converted into interlayer displacement, i.e., the interlayer displacement of 12 nodes was obtained. The acquisition frequency was set to 100 Hz. Each sample contains 50 data points, which is 0.5 s of acquisition time. Each sample in the dataset is a matrix of 12 \(\times\) 50. Among them, 12 represents 12 nodes and 50 represents 50 data points. The number of samples is 399 in each category, and the dataset contains a total of 1995 samples. After these samples are randomly sorted, 1500 samples were used for training and verification, 495 samples were used as test set.
## 5 On the Robustness of the Proposed Feedback Power Control Strategies  
In this section, we investigate the robustness of the proposed feedback power control strategies against possible non-idealities. In particular, we analyze the performance in the presence of (i) mutual interference between the transmitting nodes (i.e., when the multiple access links are not perfectly orthogonal) and (ii) noisy feedback channels. Without loss of generality, a simple illustrative scenario with \(n=2\) correlated sources is considered, using either DD LDPC coding or SCCCing. However, the obtained conclusions hold also for scenarios with more than two sources.  
Figure 6: Average BER, as a function of the average SNR at the sources, considering: (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. Both balanced SNR and unbalanced SNR power control strategies are considered.
# Regional Geomagnetic Field Modeling Based on Associated Legendre Polynomials
## 3 Data and Modeling Results
### Model Accuracy Evaluation Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **N** & **Max Error/nT** & **Min Error/nT** & **MAE/nT** & **RMSE/nT** \\ \hline R\(-\)ALPOLM & 83 & 32.60 & \(1.57\times 10^{-4}\) & 1.16 & 3.21 \\ \hline LPM & 76 & 48.12 & \(1.55\times 10^{-3}\) & 1.67 & 5.00 \\ \hline TPM & 42 & 58.23 & \(8.54\times 10^{-3}\) & 8.51 & 12.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the three model projections. Notably, the R\(-\)ALPOLM and LPM demonstrate significant advantages in the depiction of detailed features, as their 2D images not only align closely with the overall color distribution trends of the original data but also exhibit higher similarity in local detail characteristics. In contrast, although the TPM model maintains basic consistency with the original data in terms of overall color distribution trends, it shows obvious deficiencies in the expression of detailed features, with multiple regions displaying inconsistent color distributions compared to the original data, indicating its weaker capability in capturing local geomagnetic anomaly features. To further visually illustrate the differences in fitting accuracy among the three models, Figure 8 presents the planar residual distribution and residual histograms of the predicted results from each model. As shown in Figure 8, the R\(-\)ALPOLM model exhibits the best modeling performance, with residuals primarily concentrated within the range of \(\pm\)2 nT. Moreover, the residual values in the central modeling region are generally small, indicating high fitting accuracy. Larger residuals are mainly distributed in the four corner regions of the modeling area, and their distribution pattern aligns with the anomaly point distribution characteristics described in Section 3.2. The LPM model also performs well overall, with residuals similarly concentrated within \(\pm\)2 nT. However, compared to the R\(-\)ALPOLM model, its high residuals are more dispersed, appearing not only in the corner regions but also in the central modeling area, suggesting slightly inferior fitting capability for local geomagnetic anomalies compared to the R\(-\)ALPOLM model. In contrast, the TPM model exhibits significantly larger residuals, with high residuals widely distributed throughout the entire modeling region, indicating substantially lower modeling accuracy compared to the R\(-\)ALPOLM and LPM models. Figure 7: Two\(-\)dimensional planar data plots of the regional geomagnetic anomaly field: **(a)** The original data. **(b)** The TPM model. **(c)** The LPM model. **(d)** The R\(-\)ALPOLM model.The figure illustrates the comparison between the original data and the models constructed by TPM, LPM, and R\(-\)ALPOLM.
## 3 Fibre-Optic-Based Smart Geosynthetics  
### Characteristics and Principles of Optical Fibres  
Fibre optics or optical fibres (OFs) are tiny strands of \(\varnothing\)-0.2 mm glass [25]. OFs are light-transmitting cylindrical dielectric waveguides consisting of high-purity, low-loss optical materials--most commonly silica, although other plastic and polymer materials are also commercially available [26]. Owing to dopants, the refractive index of the central axis or core (approximately 1.46 for silica) is slightly higher than those of the cladding and the surrounding material [27]. Because internal optical waves moving almost parallel to the axis reach the interface at an angle above that required for total reflection according to [PERSON]'s law, the optical waves remain restricted to the core. However, light escapes when the fibre is bent through a large local radius [28]. A plastic covering (i.e., buffer) protects the OFs from scratches, and numerous OFs are frequently bundled with high-strength fibres such as Kevlar to prepare durable optical cables that can endure industrial handling and applications [29]. Typically, conventional acrylate-protected and nonbuffered OFs exhibit exterior diameters of 250 and 125 \(\upmu\)m, respectively [30]. OFs are classified as single- and multi-mode exhibiting core diameters of approximately 10 and 30-100 \(\upmu\)m, respectively [31]. Single-mode OFs offer lower optical attenuation because of the smaller difference between the core and cladding refractive indexes, which requires lower dopant concentrations. However, dispersed distinct modes travelling at various velocities distort multi-mode fibre group signals. In multi-mode fibres, the larger core enables easier alignment with the optical sources and connectors, which is highly advantageous for light-guide applications [32]. However, multi-mode fibres may only be used for intensity-based sensing applications. Optical power losses are very small, approximately 0.03 dB/km in multi-mode fibres. However, although OF power losses are irrelevant for sensing applications, 1550 nm is still the preferred window because the required opto-electronic components are more readily available [33].  
Various increasingly complex theories have been proposed to explain OF light refraction characteristics. The simplest and most basic model, known as ray optics, posits that light follows the shortest path possible (Fermat's principle) [34]. [PERSON] proposed a scalar wave model to express light diffraction [35]. [PERSON] explained polarisation by considering light as electromagnetic (vectorial) waves [36]. Quantum optics refers to more complex Schrodinger-equation-based models and determines the energy exchanged between matter and radiation [37; 38].
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 5 Experimental Results and Analysis  
To validate the proposed method in this paper, different experiments are conducted on the vehicle platform, which is shown in Figure 11. The vehicle platform consists of chassis dynamometer, experimental passenger vehicle, and related instruments. The passenger vehicle is equipped with the proposed energy management controller, the IBS, which is designed by Hella (Lippstadt, Germany). The intelligent alternator is developed by Bosch (Stuttgart, Germany). Two current sensors and two voltage sensors are employed to measure the current and voltage of the battery and the intelligent alternator. These real-time data are collected by DAQ devices from National Instruments (Texas, TX, USA), and Vector CANoe (Vector, Stuttgart, Germany) is used to monitor the information in CAN and LIN buses.  
Figure 11: Vehicle platform.  
Figure 10: Working process of the strategy.  
Two driving cycles are applied for the experiments on the platform. The Economic Commission for Europe (ECE15) [9] and New European Driving Cycle (NEDC) [11] are shown in and Figure 12. Under ECE15, the experiment is carried out with constant electrical load power. The other experiment is carried out with changing load power under the NEDC.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 3 D-Q Transformation  
To change the variables of a 3-phase induction motor to variables on the orthogonal axes consisting of D, Q, and N axes is called coordinate transformation.  
The D-axis is where the magnetic flux of the motor is typically generated; the Q-axis is symmetrical to the D-axis and is the reference axis. It is located before the D-axis in the rotating direction when the magnetic or other physical quantity of the induction motor rotates in the forward direction. Furthermore, the D-axis becomes the axis of the current that generates a torque. The N-axis is orthogonal to the D-axis and Q-axis in space and has a zero value in the motor.  
In Figure 2a, when the D-Q orthogonal axes are fixed (do not rotate), the condition is called a stationary reference frame; in Figure 2b, when the axes rotate, it is called a rotating reference frame. The stationary reference frame is also called a stator reference frame. The rotating reference frame is a reference frame of which the axes rotate at the angular velocity of \(\omega\), and its name and expression vary by the type of rotational angular velocity. For a representative example, a reference frame that rotates in synchronization with the rotor system is called synchronous reference frame, which is assumed with the PVA method.  
In the process of transforming 3-phase to 2-phase, a synchronous reference frame can be obtained by the transformation of a stationary reference frame, as shown in Figure 2.
## 3 Test Results and Discussions
### Micro-Morphology Inside the ECC Matrix  
The compressive strength and strain rate of the ECC are closely associated with the internal structure of the matrix. Nanomaterials commonly influence the hydration process of cement, thereby affecting the density of the hydration matrix. Previous studies, utilizing various testing methods, have shown that CNs can enhance cement hydration and increase the density of the cement matrix [39; 40; 41]. Previous SEM-EDS results from our team showed that when a CN is added to the mortar, it will reduce the calcium-silicon ratio of the hydrated product and increase the density of the hydrated product [36; 38]. In this study, scanning electron microscopy (SEM, Tescan-SEM4000 Pro, Xian, China) was employed to observe the microstructure of the ECC matrix. Figure 7a-c display the microscopic morphology of the control sample group (without a CN), where a noticeable unhydrated region is present in the matrix. A substantial amount of spherical silica fume (SF) and a minor quantity of ettringite are observed within the matrix. In contrast, the hydrated morphology of the 0.15% CN-ECC sample, depicted in Figure 7d-f, exhibits significant differences. Here, the matrix structure becomes denser, with more silica fume consumed by the hydration reaction and the formation of increased amounts of ettringite (AFT) and C-S-H. The SEM results indicate that the incorporation of a CN alters the structure of the hydrated matrix, promoting the formation of additional hydration products, which in turn enhances the overall properties of the ECC.  
Figure 6: Tension results of ECC after sulfate corrosion. (**a**–**c**) Strain rate; (**d**) loss rate.
###### Abstract  
Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm--time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.  
photoacoustic imaging; bone structure; convolution neural network (CNN); attention 2020 accepted: 12 November 2020; Published: 15 November 2020
## 3 Proposed Heterogeneous Algorithm for Efficient-Path Detection
### Deployment of Ant-Colony-Optimization Algorithm for Vehicle Management  
Ant-colony optimization is a heuristic optimization algorithm inspired by the behavior of ant colonies and is intended to approximate solutions for optimized problems. The algorithm simulates the behavior of ants as they search for food, where the ants leave pheromone trails that other ants can follow to find food. The algorithm uses this concept to find the best path through a problem space.  
The general process of ant-colony optimization involves several steps. First, the algorithm starts by randomly placing a certain number of artificial ants on the problem space. Each ant is initialized with a random starting point and a set of rules for moving through the problem environment. Then, the ants select the next move based on a combination of heuristics and pheromone information. As they move, they construct solutions to the problem by building a path. They also deposit pheromones on the edges (routes). The number of pheromones deposited is determined by the quality of the solution the ant has found. After all the ants have found their solutions, the algorithm selects the best solution. Moreover, the pheromones on the edges evaporate over time, simulating the fact that pheromones tend to dissipate over time in nature. Finally, the algorithm continues to repeat these steps, with the ants updating their solutions based on changing pheromone  
Figure 3: PT algorithm to handle entrance-and-exit processes.  
levels, until a satisfactory solution is found or a maximum number of iterations is reached. Figure 4 illustrates the vehicle-management process.  
In our case, the ACO algorithm optimizes the resultant path from the pheromone-termite algorithm to create the most efficient path that starts from the source vehicle and ends at the destination vehicle. The ant randomly pursues the food and returns to the colony. The ants spot its path using pheromones that can help other ants follow the same path. The chosen path has the highest number of pheromones that lead to an immediate search for food. Therefore, the value is identified based on the pheromones used by the ants.  
In our proposed algorithm, food is assigned a value of (0, 1). If there is no vehicle entering the road, then the status is assigned a value of (0), which denotes food for ants. In the case of maximum (congested) vehicles on the road, then the status is (1), which denotes no food. In another perception, the ants apply the pheromones for path detection when  
Figure 4: (**a**) Process of ant-colony optimization, (**b**) vehicle management following the ACO process to select the optimal route.  
they travel for food. Nevertheless, these pheromones disappear over time if a path is not in use. This situation demonstrates that the ants have started trailing alternate paths in search of food.
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 3 Simulations and Results
### Parameter Selection  
Two discretizing parameters, \(\Delta\)p\({}_{1}\) and \(\Delta\)p\({}_{2}\), have a significant impact on the identification rate, especially for star images with position noise. When there are position errors  
\begin{table}
\begin{tabular}{c c} \hline
**Field of View** & **25\({}^{\circ}\times\) 25\({}^{\circ}\)** \\ \hline Resolution & 1024 \(\times\) 1024 \\ Pixel size & 15 \(\mu\)m \\ Focal Length & 34.64 mm \\ Minimum Sensitivity & 5.5 Mv \\ \hline \end{tabular}
\end{table}
Table 1: Key parameters of sensor star.  
Figure 10: Extend the core cluster.  
with a large deviation in an image, it is reasonable to set \(\Delta\)p\({}_{1}\) and \(\Delta\)p\({}_{2}\) to bigger values for more tolerance for noise. However, too many redundant guide stars may be matched as candidates to severely decrease the effectiveness of the voting process. However, when \(\Delta\)p\({}_{1}\) and \(\Delta\)p\({}_{2}\) are set to small values, there is a greater possibility that the correct candidates of the voting unit are eliminated.  
The relationship between \(pv1\) and \(pv2\) and the identification rate as the standard deviation of position noise increasing from 0 to 2.0 pixels is illustrated in Figure 11, while the standard deviation of the magnitude noise remains at 0.3 Mv. It can be seen that when position errors increase by more than 1.0 pixel, the algorithm performance degrades differently for different parameters groups, particularly for parameter group 1 and group 3, in which \(\Delta\)p\({}_{1}\) and \(\Delta\)p\({}_{2}\) are set to 0.05 and 0.05 and 0.015 and 0.015, respectively.  
It is important to note that the algorithm achieves a higher identification rate of more than 97% with 2.0 pixels of noise in parameter group 2, where \(\Delta\)p\({}_{1}=0.025\), \(\Delta\)p\({}_{2}=0.025\). Furthermore, the average run times are 16.24 ms, 18.36 ms, and 21.87 ms for parameter group 1, group 2, and group 3, respectively. Considering both the robustness to position noise and run-time performance of the algorithm, we selected parameter group 2 as the discretizing unit parameters in the paper.
## Animal Studies
### Animal Models of Multiple Sclerosis
#### 2.1.2 Toxin-Induced Demyelination  
Toxin-induced demyelination is different to virally induced demyelination in that it does not attempt to imitate MS, but mimics the processes of demyelination and remyelination of cells. This model is induced in animals by either injecting the toxin lysolecithin, a phospholipase A\({}_{2}\) activator or by adding the toxin curprizone, a copper ion chelator, to the animal's food [27; 32]. Similarly to what can be found in the plaques of humans with MS, lesion sites of animals injected with lysolecithin are infiltrated with T cells, B cells and macrophages followed by spontaneous remyelination [47]. This model can be advantageous in inducing a chronic demyelinating disease, as it is characterized by degeneration of oligodendrocytes rather than by a direct attack on the myelin sheath [50].  
Building from these earlier approaches, a new model has recently been suggested; the cuprizone autoimmune encephalomyelitisitis (CAE) model. This model resembles the experimental autoimmune encephalomyelitisitis (EAE) model (described below) in both its histopathology and how it is developed. The CAE model is induced by feeding cuprizone to mice during a two-week period, followed by an injection of complete Freund's adjuvant and pertussis toxin. After an incubation period, significant demyelination can be observed along with clear radiological abnormalities in the corpus callosum [42; 43]. This creates a model that can be used to observe demyelination and remyelination progression at different time points and with gradual effects in different brain areas, and specific manipulations have recently been suggested for the development of this model depending on the exact aims and focus of each study using it [51]. Taking into account the deregulation of the endocannabinoid system in MS and its important role in neuroprotection against demyelination, this model can prove very useful for testing the effects of cannabinoid compounds on myelin repair following a demyelinating insult [52; 53].