esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A comprehensive mathematical model that includes methodology for assessing rotor transient stability was proposed in [19]. The model is validated using both simulation and experimental data, showing good agreement with the measured results. The authors of [20] evaluate the performance of induction motor control models for specific conditions. The study [21] presents a design optimization approach for an induction electric motor aimed for automotive applications. The models are compared on the base of their accuracy, computation time, and ease of use. The results show that the optimized design improves the performance of the motor in terms of torque and efficiency. The authors of [22] discuss the design of methods enabling the identification of the standstill parameters of electric induction motors with a focus on their stability. The authors highlight the importance of detailed consideration of key features in the design process and provide examples of how particular models comparison can be used to optimize the performance of electric machines. The article [23] presents a design and optimization approach for electric motors using core loss model. The results show that the approach can be used to improve the performance of electric motors in terms of efficiency and torque. A comprehensive overview of the available software tools for electric motor design and simulation aimed at effective control is given in [24]. The effects of piezoelectric stator mounting position angle, stator-rotor contact materials, and preload on motor torque are analyzed by simulation and presented in [25], identifying efficient modeling techniques for electric motor analysis, including torque modelling by vector synthesis. The disadvantages of the existing CAD model tools are the limited possibilities in terms of the functions used, which do not allow for a comprehensive study of the model in terms of mechanical, electrical, electromagnetic and temperature parameters. Despite the advancements in electric motor CAD modeling, there are still certain shortcomings that need to be addressed. One of the main limitations is the accuracy of the models, particularly for complex geometries and nonlinear materials. Another challenge is the computational cost of the models, which can be prohibitively expensive for large-scale systems. In addition, the lack of standardization in the development and validation of CAD models makes it difficult to compare results from different studies. Finally, the availability of accurate and reliable input data, such as material properties and boundary conditions, can also be a challenge in some cases. The aim of the study is to establish the dependencies between the characteristics of an IM developed on the basis of a model approach. In order to achieve this objective the following tasks have to be completed:  
1. The development of a mathematical model of an IM. 2. The development of a CAD model of an IM. 3. Carrying out a performance study of the developed IM. 4. Carrying out a finite element analysis of the CAD model of an IM.
Thus, there is a need for comprehensive review papers that analyze the current MIMO waveforms, channel estimation, and equalizations techniques. Therefore, the current paper gives a detailed overview of the recent advantages of MIMO systems in general and classifies the MIMO-OFDM waveforms, channel estimators, and equalizers. Figure 1 demonstrates the different research axes that will be discussed in the paper, in addition to the logical organization of the paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Details Paper** & **Special Focus/General Vision of** & **MIMO-SDR System** \\  & **Reference** & **the Paper** & **Research Axes** \\ \hline
2019 & [PERSON], et al. [8] & MIMO systems in presence of channel and hardware impairments & - MIMO waveform \\  & & & - & MIMO hardware impairments \\  & & & - & MIMO time varying \\  & & & channels \\ \hline
2019 & [PERSON], et al. [9] & Channel estimation algorithms for 5G candidate waveforms & - & MIMO channel estimation algorithms \\ \hline
2019 & [PERSON], et al. [10] & 5G massive MIMO localization & - & MIMO channel \\  & & & - & parameter estimation \\  & & & - & MIMO localization \\  & & & techniques \\ \hline
2015 & [PERSON], et al. [11] & Large-scale MIMO Systems & - & Channel model \\  & & & - & Main applications \\  & & & - & Physical layer and \\  & & & network technologies \\ \hline
2015 & [PERSON] and [PERSON] [12] & MIMO Detection & - & Co-channel interference \\  & & & - & MIMO detection \\  & & & - & MIMO channel models \\ \hline
2008 & [PERSON] and & & & \\  & [PERSON] [13] & MIMO channel modelling \\ \hline
2002 & [PERSON] and [PERSON] [14] & Models for MIMO propagation channels \\ \hline
2018 & [PERSON], et al. [15] & Massive MIMO linear precoding techniques for single- and multi-cell systems \\ \hline
2008 & [PERSON], & & \\  & [PERSON] and & Overview of MIMO testbed & - & MIMO developed \\  & [PERSON] [1] & technology & & testbeds \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The rest of the paper is organized as follows: Section 2 gives an overview of the recent radio communication trends, namely SDR, CR, and MIMO. Section 3 describes the recent advances in the MIMO research field and the state of art concerning MIMO-OFDM systems. Finally, Section 4 summarizes the existing challenges and defines the novel research directions.
# A Stock Prediction Method Based on Heterogeneous Bidirectional LSTM
## 2 Related Work
### LSTM (Long Short-Term Memory) Neural Networks  
LSTM, proposed by [PERSON] and [PERSON] [11] in 1997, is a variant of RNN neural networks. It was introduced because training standard Recurrent Neural Networks (RNNs) to handle problems with long-term temporal dependencies presents numerous challenges. Recent studies (e.g., [PERSON] and [PERSON], 2018 [15]; [PERSON] & [PERSON], 2018 [16]; [PERSON] & [PERSON], 2017 [17]; [PERSON], 2018 [18]; [PERSON], 2018 [19]) have utilized deep learning methods to predict financial time series data, involving techniques such as LSTM and BiLSTM, with these models achieving notable predictive performance.  
The significance of LSTM neural networks lies in the gate mechanisms present in each of their units: the forget gate, input gate, and output gate. These three gates play different roles in processing data information. The forget gate determines which information from the input should be discarded, the input gate decides which data information will be modified, and the output gate determines which data information will be passed to the next neural unit. The specific structure of an LSTM neural unit is shown in Figure 1:The relevant gating mechanism equations are as follows:  
\[f_{t}=\sigma(W_{xf}\cdot x_{t}+W_{hf}\cdot h_{t-1}+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{xi}\cdot x_{t}+W_{hi}\cdot h_{t-1}+b_{i}) \tag{2}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\xi_{t} \tag{3}\]  
\[\dot{c}_{t}=tanh(W_{xc}\cdot x_{t}+W_{hc}\odot h_{t-1}+b_{c}) \tag{4}\]  
\[o_{t}=\sigma(W_{xo}\cdot x_{t}+W_{ho}\cdot h_{t-1}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\odot tanh(c_{t}) \tag{6}\]  
Among these, Equation (1) is the calculation formula for the forget gate \(f_{t}\), Equation (2) represents the calculation formula for the input gate \(i_{t}\), Equations (3) and (4) represent the calculation formulas for the cell state \(c_{t}\), indicating the long-term memory transmission state of data information, Equation (5) represents the calculation formula for the output gate \(o_{t}\), and Equation (6) \(h_{t}\) represents the hidden state output information flowing to the next unit. In Equations (1)-(6), \(\sigma\) represents the sigmoid activation function, \(\odot\) is the Hadamard product, and \(x_{t}\) represents the input vector of the current unit. \(W_{x}\). and \(W_{f}\). represent the weight matrices for the input vector \(x_{t}\) and the previous hidden state \(h_{t-1}\), respectively. \(b\). represents the bias vectors.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### The Effectiveness of the Method  
To evaluate the effectiveness of our proposed method in modern network environments, we not only tested it on the CIFAR10 dataset but also extended the experiments to more complex image classification tasks, such as the _mini_ImageNet dataset. On _mini_ImageNet, we used the EfficientNet model. Within the EfficientNet family, ranging from B0 to B7, there is a progressive increase in model parameters, which is accompanied by a corresponding enhancement in accuracy. Nevertheless, due to constraints related to device capabilities and graphics card memory resources, we ultimately opted for the EfficientNet-B0 model.  
In addition, to further validate the performance of our method, we also compared it with two defense methods that utilize diffusion models. We adhere to the fundamental experimental setup and utilize the PyTorch implementations provided by [PERSON] et al. ([[https://github.com/wzekai99/DM-Improves-AT](https://github.com/wzekai99/DM-Improves-AT)]([https://github.com/wzekai99/DM-Improves-AT](https://github.com/wzekai99/DM-Improves-AT)), accessed on 1 September 2024) and DiffPure ([[https://github.com/NVlabs/DiffPure](https://github.com/NVlabs/DiffPure)]([https://github.com/NVlabs/DiffPure](https://github.com/NVlabs/DiffPure)), accessed on 1 September 2024). The classification model employed is WideResNet [51]. Given the constraints of our device's performance and the available graphics card memory, we limited ourselves to generating denoised images using 1M generated data. We trained a WRN-28-10 model on the CIFAR10 dataset with 1M generated data. For optimization, we used the SGD optimizer with Nesterov momentum, setting the momentum factor to 0.9 and the weight decay to \(5\times 10^{-4}\). We implemented a cyclic learning rate schedule with cosine annealing, starting with an initial learning rate of 0.1. The dropout rate was set at 0.3. To evaluate performance, we considered two metrics: clean accuracy and robust accuracy. Clean accuracy evaluates defense methods on clean data and measures them across the entire test set, while robust accuracy evaluates the model's resistance to AutoAttack.  
Table 7 shows the testing accuracy under clean images and AutoAttack on the CIFAR-10 and ImageNet datasets. The experimental results show that our method has comparable accuracy on clean data to existing advanced methods, while significantly improving robustness on adversarial samples. In particular, on the ImageNet dataset, our method shows a significantly smaller decrease in accuracy compared to other comparison methods when subjected to AutoAttack attacks. This indicates that our method not only performs well on small-scale datasets but also demonstrates strong robustness on large-scale datasets and in the face of complex attacks.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Diagnostics Techniques
#### 2.2.1 EC-Based Methods  
These methods include _eddy current_ inspection and _magnetic flux leakage_ (MFL). The method based on eddy current measures the material response to an induced electromagnetic field: the presence of a surface or near-surface defect produces a distributed electromagnetic field that can be measured. This method is contactless, but very sensitive to probe lift-off from the surface of test specimens [20]. The method based on MFL consists of magnetizing the object to be tested and in scanning its surface by means of a flux-sensitive sensor [21]. In the presence of a defect, the magnetic flux (usually contained into the magnetized test object) leaks into air [22]. This method is able to detect superficial or near-superficial transverse fissures (like RCF) but is not suited to longitudinal fissures, and its performance is influenced by the selected scanning speed [23].  
The inspection speeds of the systems based on the two methods described above are limited (75 km/h for EC testing, 35 km/h for MFL) and smaller than those characterizing revenue service trains [5].  
Figure 6: Three-dimensional representation of bolt-hole cracks.  
Figure 7: Thermite weld joining two rails with different profiles (50E5, UNI-50, on the right and 60E1, UNI-60, on the left).  
The limitations of EC inspection of rails are analyzed in Ref. [24]. Some of the most recent advances in EC-based testing of rails are illustrated in Refs. [25; 26]. Finally, it is worth mentioning that a recent implementation of the MFL technology can be found in Ref. [27].
## Materials and Methods
### Data Analysis
#### 2.3.3 Independent Component Analysis  
Independent component analysis is a method of extracting independent features (or independent signal sources) from data. In ICA, a basic assumption is that the observed data can be viewed as a linear superposition of multiple different independent components (or signal sources). Assuming that  
Figure 2: The FNC matrix of three tasks performed by a subject. (**a**) The FNC matrix of a single target tracking task with word from word-scene pairs. (**b**) The FNC matrix of a multiple target tracking task with lure word. (**c**) The FNC matrix of a multiple target tracking task with word from word-scene pairs. It should be noted that these three pictures only represent the functional connectivity of a certain task, but not the overall situation.  
\(G=\left[g_{1},g_{2},\ldots g_{n}\right]^{T}\) is the observed signal, according to the previous assumption, there are independent signal sources \(S=\left[s_{1},s_{2},\ldots,s_{m}\right]^{T}\) that satisfy:  
\[G=AS \tag{2}\]  
where A is a matrix of \(n\times m\) (usually full rank), and the goal of ICA is to estimate a decomposition matrix B such that:  
\[\hat{S}=BG \tag{3}\]  
where \(\hat{S}\) is the best estimate of independent signal source \(S\).  
For fMRI signals, spatial ICA is often used to extract spatial features, which can extract time-series data of non-overlapping brain regions in space. In this study, the GIG-ICA method in the group ICA toolbox [34] (GIFT, [[http://mialab.mrn.org/software/gift](http://mialab.mrn.org/software/gift)]([http://mialab.mrn.org/software/gift](http://mialab.mrn.org/software/gift))) is used to analyze the pre-processed fMRI data of all subjects. Thirty-nine independent components are obtained at first. After manually removing artifacts unrelated to the experiment and signals from unrelated brain regions, 24 spatial independent components are finally obtained (shown Figure 3), as which greatly reduced the computational cost compared to using whole brain fMRI data directly.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 2 Background
### Software Solutions for Reliability Enhancement of NNs  
In the literature, several methodologies have proposed the enhancement of the system's reliability by tacking hardware fault impacts on NNs through the adaptation of the components of the software [14; 49; 50], hardware [51; 52; 53], or a combination of both (e.g., employing software-hardware co-design) [13; 54].  
Although the approaches that involve hardware design adaptation seek to increase system robustness resorting to well-known approaches [51; 55; 56; 57] regardless of the application, it has significant drawbacks, such as the need for modifications during the design phase, which can be impractical for system designers. Additionally, these hardware changes can increase power consumption and costs, limiting their use in embedded systems.  
In contrast, software-based reliability modifications are more efficient, cost-effective, and easier for system designers to implement, due to their inherent flexibility and scalability.  
HTs that are software-based at the application level mainly involve the modification of the NN topology [13; 33] or the modification of the training process [13; 32] while avoiding area and hardware implementation cost overhead. The state of the art includes the HTs based on the range restriction [14; 32; 58] where activation functions, such as ReLU, Swish, or Tanh functions, included in the most common NN architectures, are bounded within a specific interval. In addition, a few studies have explored the possibility of performing a neural architectural search based on reliability-oriented metrics for evaluating NN topology [49]. Finally, taking reliability into account during the training process to make the  
Figure 3: Effect of the fault propagation up to the application level.  
model more resilient at inference time has been addressed as well [33; 59]. These techniques fall under the category of fault-aware training HTs, which aim at minimizing the error that a fault can induce at the application level during the training phase to improve the robustness of the NN.  
The following subsections describe the implementation detail of the state-of-the-art HTs (i.e., Adaptive Clipper, Ranger, Swap ReLU6, median filter and fine-grain TMR).
# Factors That Affect Liquefaction-Induced Lateral Spreading in Large Subduction Earthquakes
## 3 Validation of the Numerical Methodology
### Validation with Historical Cases
#### 3.2.2 Model Input Ground Motion  
The 2010 Maule Mw8.8 earthquake caused extensive damage to ports and bridges ([PERSON] et al. (2012) [45] and [PERSON] et al. (2012) [46]). Liquefaction-induced lateral spreading significantly damaged the Lo Rojas fishermen port in Coronel, Bio-Bio Region. As [PERSON] et al. (2017) [22] indicated, only strong motions recorded in rock stations are adequate for the numerical analyses. In this case, recordings in rock stations were obtained at the RAP (Rapel), COV (Convento), and USM (Santa Maria University). According to the USGS coseismic slip model ([PERSON] et al. (2011) [47]), the 3-D distance to the Rapel Station and the interplate fault is 31 km. That distance is very similar to the one at the Lo Rojas site, which is approximately 32 km. For that reason, [PERSON] et al. (2017) [22] selected the Rapel (RAP) ground motion. We used the same criterion to select the station to simulate the lateral spreading in the Lo Rojas site in this study. Figure 16 shows the chosen record (both directions) with a significant duration of approximately 34 s and a PGA of 0.2 g.  
The 1964 Alaska Mw 9.2 earthquake caused ground failures and collapsing structures from lateral spreading, and the associated tsunami caused about 130 deaths ([PERSON] and [PERSON] (1995) [2]).  
Figure 16: (**a**) North-South (NS) and East-West (EW) acceleration time histories for the Rapel station, (**b**) spectral acceleration (5% damping) of the NS and EW components for the Rapel station.  
Figure 15: Soil layout at (**a**) the Lo Rojas site, and (**b**) the Matanuska site  
According to [PERSON] et al. (2008) [40], no strong motion instruments were operative when that destructive seismic event occurred, so no direct measurement of near field ground motions are available. Consequently, we used a simulated ground motion at the Anchorage site shared by [PERSON] et al. (2008) [40] to reproduce the lateral spreading case. Figure 17 shows the simulated record (both directions) with a significant duration of approximately 152 s and a PGA of 0.25 g.
###### Abstract  
The purpose of this in vitro study was to assess the trueness of a dental implant scanned using an intraarol high-frequency ultrasound prototype and compared with conventional optical scanners. An acrylic resin cast containing a dental implant at position 11 was scanned with a fringe projection 3D sensor for use as a reference dataset. The same cast was scanned 10 times for each group. Ultrasound scanning was performed with a high-frequency probe (42 MHz, aperture diameter of 4 mm and focus length of 8 mm), and 3D images were reconstructed based on the depth of each surface point echo. Optical scans were performed in a laboratory and with an intraarol scanner. A region of interest consisting of the dental implant site was segmented and matched to the reference dataset. Trueness was defined as the closeness between experimental data and the reference surface. Statistical analysis was performed with one-way ANOVA and post-hoc tests with a significance level of \(p=0.05\). No statistical difference was found among the evaluated scanners. The mean deviation error was 57.40 \(\pm\) 17.44 \(\upmu\)m for the ultrasound scanner, 75.40 \(\pm\) 41.43 \(\upmu\)m for the laboratory scanner and 38.55 \(\pm\) 24.34 \(\upmu\)m for the intraarol scanner. The high-frequency ultrasound scanner showed similar trueness to optical scanners for digital implant impression.  
Keywords: dental impression technique; dental implants; ultrasonography +
Footnote †: journal: Applied Sciences
## 5 Discussion
### SANAE: Scintillation Events
It is well-known that, when large-scale gradients (of the order of kilometers or tens of kilometers) occur in the high latitude ionosphere, GDI generates irregularities in cascade down to scales of meters or even below (e.g., Reference [75]). Therefore, irregularities causing amplitude scintillation in the L-band Fresnel scale can be co-located with decameter irregularities causing HF backscatter (see also Reference [22]). This observation further confirms the presence of small-scale ionospheric irregularities.