esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
The same author team extended their study [30] by presenting a hybrid CNN-Random Forest approach proposed for classifying Polar Map images and clinical attributes into normal and abnormal classes, with ICA findings as the reference for CAD disease. The research involved 566 patient cases. The InceptionV3 pretrained model was used to predict the class of the input Polar Maps. The model's output was combined with 22 clinical factors, including gender and age, and fed into the Random Forest classifier for outcome prediction. With reference to the ICA results, the model achieved an accuracy of 78.44%, sensitivity of 77.36%, and specificity of 79.25%. Interestingly, the overall accuracy of the human cognitive process reached 79.15%, approximately 1% higher than the automatic model's accuracy (78.43%). Furthermore, the overall agreement rating between the human experts and the model was 86% (Cohen's Kappa = 72.24), indicating a strong level of agreement. The model was also tested on unseen data from a different SPECT scanner and achieved consistent results with an accuracy of 76.53%. These findings demonstrated the effectiveness of the hybrid approach for CAD diagnosis and its potential for practical clinical use. [PERSON] et al. [31] utilised polar maps and SPECT slices as the input data. The dataset consisted of 1413 heart SPECT images, which a nuclear expert labelled as either CAD or non-CAD. The performance of the CNN network, specifically using the VGG-16 architecture, was evaluated using a five-fold cross-validation procedure. The results revealed that SPECT images provided a better diagnosis than polar maps. The precision achieved using SPECT images was 86.14 \(\pm\) 2.14%, while polar maps achieved a precision of 82.57 \(\pm\) 2.33%. These findings demonstrated the potential superiority of SPECT images over polar maps in CAD diagnosis using the proposed CNN model.
_Appl. Sci._**2015**, \(5\), 1310-1322; doi:10.3390/app5041310  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Extremely Nonlinear Optics Using Shaped Pulses Spectrally Broadened in an Argon- or Sulfur Hexafluoride-Filled Hollow-Core Fiber  
**[PERSON] 1,*, [PERSON] 1,\({}^{\dagger}\) and [PERSON] 1,2**  
1 Institute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich-Schiller-University Jena, Max-Wien-Platz 1, 07743 Jena, Germany;  
E-Mails: [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (C.S.)  
2 Helmholtz Institute Jena, Helmholtzweg 4, 07743 Jena, Germany  
\({}^{\dagger}\) Present address: Chemistry Department, University of California, Berkeley, CA 94720, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-3641-9-47214; Fax: +49-3641-9-47232.  
Academic Editor: [PERSON]  
_Received: 23 September 2015 / Accepted: 16 November 2015 / Published: 20 November 2015_  
**Abstract:** In this contribution we present a comparison of the performance of spectrally broadened ultrashort pulses using a hollow-core fiber either filled with argon or sulfur hexafluoride (SF\({}_{6}\)) for demanding pulse-shaping experiments. The benefits of both gases for pulse-shaping are studied in the highly nonlinear process of high-harmonic generation. In this setup, temporally shaping the driving laser pulse leads to spectrally shaping of the output extreme ultraviolet (XUV) spectrum, where total yield and spectral selectivity in the XUV are the targets of the optimization approach. The effect of using sulfur hexafluoride for pulse-shaping the XUV yield can be doubled compared to pulse compression and pulse-shaping using argon and the spectral range for selective optimization of a single harmonic can be extended. The obtained results are of interest for extending the range of ultrafast science applications drawing on tailored XUV fields.  
**Keywords:** sulfur hexafluoride; nonlinear fiber optics; ultrashort laser pulses; pulse-shaping; atomic and molecular physics; high-harmonic generation
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 3 Results
### Material Modeling Results
#### 3.3.1 Random Particle Model  
Figure 17a illustrates the selection of geometric parameters of our RVE model. In this case, the particle volume fraction was assigned based on the air fraction previously extracted from the CT scan for specimen B2. The particle size distribution was set to follow a logarithmic trend, as this better captures the inherently random and unpredictable nature of aluminum foams.  
In addition to the volume fraction, two other key parameters were defined: the mean particle diameter and its standard deviation. Since the model assumes both phases as aluminum for simplification, we accounted for the presence of air by geometrically representing the particles as hollow spheres. This allowed us to preserve the influence of porosity, while the particles themselves act as thin interfacial shells with negligible material stiffness.  
Finally, the dimension ratio was introduced, representing the ratio between the particle diameter and the edge length of the cubic RVE domain. For example, with a particle diameter of 10 \(\upmu\)m and a size ratio of 4, the resulting RVE cube measures 40 \(\upmu\)m along each axis.  
After all input parameters were defined, the micromodel was generated and automatically discretized. Using the Ansys Material Designer (2025 R1) tool, the effective material properties were computed based on the defined input geometry and volume fractions. These results, shown in Figure 17b, represent the homogenized elastic behavior of the porous microstructure, as predicted by the Random Particle Method.
## 4 Test Results and Analysis
### Confinement Effect by Spiral Rebar Design (A12H-1 and 3-7)
#### 4.2.3 Calibration of the Design Equation  
According to Equations (8) and (17), the enhancement in strength by lateral confinement can be estimated as a function of \(f_{lat}\) and \(A_{core}\). The equation estimates the increase in strength due to the change in spiral diameter as 1974 kN between A12H-5 and A12H-7, and 5496 kN between A12H-3 and A12H-6, as shown in Table 4. However, the increases observed in the test results were 184 and 793 kN. That means that the effective \(A_{core}\) fraction was only 14.4% for A12H-5/-7 and 9.3% for A12H-3/-6.  
To calibrate the effective concrete core area (\(A_{core}\)), a coefficient, \(\kappa\), as a function of spiral/stirrup dimensions, was introduced in Equation (20). Because Equations (8) and (17) were verified for the minimum anchorage spacing [15,16], \(\kappa\) is 1.0 for the minimum anchorage spacing. Next, a revised equation for the spiral-dependent contribution of lateral confinement to the bearing strength was presented as Equation (21). The exponent of Equation (20) was determined to create \(\kappa\) in Equation (21), which is identical to the average increase (\(1.12=\frac{1.144+1.093}{2}\) ) from \(A_{core}\) for the minimum anchorage spacing.  
\[\kappa_{sp}=\left(\frac{D}{D^{\prime}}\right)^{0.12} \tag{20}\]  
\[P_{s}=4.1\kappa_{sp}f^{\prime}_{lat,sp}A^{\prime}_{core,sp} \tag{21}\]  
where \(D^{\prime}\), \(f^{\prime}_{lat,sp}\), \(A^{\prime}_{core,sp}\) are \(D\), \(f_{lat,sp}\), \(A_{core,sp}\) for the minimum anchorage spacing.
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 2 Level-5 Autonomous Driving--An Overview of the Relevant Concepts
### Autonomous Driving Usecase Groups  
The autonomous driving scenarios are categorized in the following use case groups [9], provided by the 3 rd Generation Partnership Project (3 GPP) standardization.  
* **Vehicle Platooning** facilitates a dynamic group of vehicles to drive with a shorter inter-vehicle gap (directly related to the communication latency).  
* **Advance Driving** facilitates collaboration among vehicles to perform complex nanouvres, i.e., speeding, breaking, overtaking, etc., in a safer manner.
* **Remote Driving** facilitates a remote driver to operate vehicles in areas/ situations where high reliability and short low latency is achievable.
* **Extended Sensors** facilitates holistic understanding of the complex environment by exchanging data (directly related to high data rate) gathered from all relevant road users (vehicles, RSUs, IoT enabled devices, etc.).
* **General Aspects** and **Vehicle QoS** facilitates the aforementioned use case groups in terms of communication coverage, service availability, service quality, and smoother user experience.  
These use case groups are expected to increase safety, traffic efficiency, collision avoidance, and reduce fuel consumptions. The recently commercialized 5G network and its variant standardized under 3 GPP release 16 is expected to provide the latency of 1 msec, ensuring the reliability of 99.999%, availability of 99.999%, and very high throughput of 10 Gbps [10].
## 3 Results and Discussion
### Weight Analysis of Influencing Factors
The influence weights of various factors on the denitration efficiency are calculated from the orthogonal test results and are shown in Figure 12.  
The experimental results show that discharge power in the DBD reactor has the greatest impact on the denitration efficiency, the weight coefficient of which is 56.96%. The simulated flue gas flow rate and mass fraction of the NaOH solution also have important effects, the weight coefficients of which are 18.02% and 11.54%, respectively. The spray density, packing type, and packing height have little influence on the denitration efficiency, the weights of which are 5.02%, 4.33%, and 4.16%, respectively.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme
### Performance Metrics  
To evaluate the performance of the model for fault detection, the receiver operating characteristics (ROC) curve and the resulting quantification metric area under the ROC curve (AUC) [6] are employed. The ROC curve is created by plotting the fault detection rate (FDR) on the Y-axis against false alarm rate (FAR) on the X-axis under different threshold levels. FDR is defined as the percentage of detected fault samples over the number of fault samples, and FAR is regarded as the percentage of detected fault samples over the number of normal samples [39]. Both the range of FDR and FAR fall in [0, 1].  
An illustration of the ROC curve and AUC value are shown in Figure 3. On the ROC plot, the points situated at the top left corner have a higher FDR value and a lower FAR value, and hence are regarded having better performance than other points on the ROC curve. The ROC curve and the AUC metric provide a relative trade-off between FDR and FAR. A good fault detector should yield a high FDR and a low FAR on an ROC curve and, accordingly, a large AUC value.
## 4 Design and Complexity of Proposed DL Architectures
### DL Architecture
\[\vec{f}_{l}^{t}=f_{ReLU}(\mathbf{W}_{f}\vec{x}_{l}^{t-1}+\mathbf{Z}_{f}\vec{x}_{l}^{t} +\mathbf{b}_{f}) \tag{14}\]  
\[\vec{i}_{l}^{t}=f_{ReLU}(\mathbf{W}_{f}\vec{x}_{l}^{t-1}+\mathbf{Z}_{i}\vec{x}_{l}^{t} +\mathbf{b}_{i}) \tag{15}\]  
\[\mathbf{o}_{l}^{t}=f_{ReLU}(\mathbf{W}_{o}\vec{x}_{l}^{t-1}+\mathbf{Z}_{o}\vec{x}_{l}^{t} +\mathbf{b}_{o}) \tag{16}\]  
\[\vec{c}_{l}^{t}=\vec{f}_{l}\odot\vec{c}_{l}^{t-1}+\vec{i}_{l}^{t}\odot\sigma_ {c}(\mathbf{W}_{c}\vec{x}_{l}^{t-1}+\mathbf{Z}_{c}\vec{x}_{l}^{t}+\mathbf{b}_{c}) \tag{17}\]  
\[\vec{x}_{L}^{t}=\mathbf{o}_{l}^{t}\odot\sigma_{z}\vec{c}_{l}^{t} \tag{18}\]  
where \(\vec{f}_{l}\), \(\vec{i}_{l}^{t}\), and \(\vec{o}_{l}^{t}\) represent, input and output gate vectors. Parameter matrices and vectors associated with these gates are denoted by \(\mathbf{W}\), \(\mathbf{Z}\), and \(\mathbf{b}\), respectively. The unit state of LSTM is denoted by \(\vec{c}_{l}^{t}\). Both \(\sigma_{c}\) and \(\sigma_{z}\) are hyperbolic tangents, and the Hadamard product is denoted by the \(\odot\) operator. In both FNN and RNN architectures, three hidden layers are utilized. In these three hidden layers, there are 256, 128, and 128 neurons, respectively. The proposed RNN architecture is depicted in Figure 5b. All of the proposed DL architectures are trained with the help of the adaptive moment optimizer (Adam). Similarly, a 1D convolution layer is selected as the hidden layer for CNN architecture. The output at the \(l\)th hidden layer in 1D CNN is given as [28]  
\[f_{ReLU}(s)=max(0,s) \tag{10}\]0  
where the kernel and the bias associated with the \(l\)th hidden layer for its \(i\)th neuron are denoted by \(\mathbf{W}_{l}^{ij}\) and \(\mathbf{b}_{l}^{i}\). Output of the \(j\)th neuron is represented by \(\mathbf{s}_{l}^{j}\). The proposed architecture of 1D CNN is shown in Figure 5c, and its parameters are listed in \[f_{ReLU}(s)=max(0,s) \tag{10}\]1  
\[f_{ReLU}(s)=max(0,s) \tag{10}\]2 Proposed DL architecture. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of hidden layers & 3 \\
1D convolution layer 1 & Filter Size = 3/Number of filters = 32 \\
1D convolution layer 2 & Filter Size = 6/Number of filters = 32 \\
1D convolution layer 3 & Filter Size = 9/Number of filters = 32 \\ Activation function & ReLU \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of parameters of the proposed CNN architecture.
## 4 Discussion
### Limitations and Future Work  
Although very good outcomes were obtained in this paper, there are a number of limitations that should be addressed in future work. First, only time feature values were considered for movement pattern recognition. Specifically, the frequency domain feature values [41] can also be used for the pattern recognition of hand movements. However,in this paper, only time-domain feature values have been extracted for downscaling and filtering. It may be interesting to see whether the addition of frequency domain feature values for pattern recognition can improve the accuracy of predictions or not in future studies. Another limitation is that this paper has only built offline learning algorithms. In future work, real-time predictive learning [43,44] may be more relevant as applied to prosthetic hand and exoskeleton control.  
Overall, the main work of this study is that considering sex differences in muscles would enhance the performance of hand movement pattern recognition systems compared to current movement recognition systems that use sEMG signals as input to have a real clinical and commercial impact.
## 2 Materials and Methods
### Analysis  
To investigate the responses of dryland vegetation to different drought intensities, we defined two categories of dry conditions based on \(\text{SPEI}_{\text{6-month}}\), extreme dry (\(\text{SPEI}\leq-2\)), and medium dry (\(-2<\text{SPEI}\leq-0.5\)). The R package, GeoRange, was implemented for monthly SPEI data to estimate the total area under extreme dry during the recent two decades (2000-2020). The Mann-Kendall trend test was utilized for time series of the total area and region-wide mean SPEI within the study area. Besides, the statistic (\(\text{SPEI}_{\text{1-month}}\leq-2\)) was conducted to generate the map of the pixel-wise extreme-dry month during November 2018 to April 2019.  
To further examine the response of dryland biomes to extreme drought, we selected four-pair regions of interest (labelled as ROI-1, ROI-2, ROI-3, and ROI-4) distributed from mesic northern to xeric southern areas. The selection criteria are: (1) same or highly similar biome types in each ROI; (2) the sub-regions within ROIs underwent different drought conditions (extreme dry, medium dry, respectively). ROI-1 are mainly covered by woodlands and open woodlands. ROI-2 and ROI-3 are covered by scattered shrubs and grasses. Vegetation in ROI-4 is dominated by Hummock grasses mixed with scattered shrubs. In addition, ROI-2 and ROI-4 were also under extreme dry conditions in the monsoon season of 2012-2013 and 2015-2016, respectively (Figure S1).  
With the purpose of detecting drought-related signals by eliminating seasonality, monthly anomalies (\(\Delta X\)) and standardized anomalies (\(X_{SA}\)) of all the aforementioned variables were calculated as a deviation from their corresponding multiyear (2007-2018) mean of each month. The Equations (4) and (5) are:  
\[\Delta X = X_{m}-\overline{X_{m}} \tag{4}\] \[X_{SA} = \frac{X_{mm}-\overline{X_{m}}}{\sigma_{m}} \tag{5}\]  
where \(n\) is the yearly temporal coverage from 2007 to 2018, \(X_{mm}\) is the monthly ranging from July to June, \(\overline{X_{m}}\) and \(\sigma_{m}\) are the mean and standard deviation of time series \(X\) at month \(m\).  
To quantify the relationship between satellite-based observation and tower-based measurement, the coefficient of determination (\(r^{2}\)) was calculated across five selected tower sites at different spatial (0.5\({}^{\circ}\), 0.05\({}^{\circ}\)) and temporal (monthly, 16-day) resolutions. All satellite datasets were extracted from a 3 \(\times\) 3 window centered on each flux tower site respectively. A \(t\)-test was utilized to examine the statistically significant level of the relationships (\(p\)-value).  
Data processing, analysis, and visualization were conducted in open-source R scientific computation environment (Version 3.6.2) and associated packages contributed by the R user community (Available online: [[http://cran.r-project.org](http://cran.r-project.org)]([http://cran.r-project.org](http://cran.r-project.org)) (accessed on 20 August 2020)).
## 4 Results  
### Patent Search Results  
Before performing technology network analysis, patent search result analysis was conducted to understand the initial status of SOA technology development. The SOA search involved 117 level-4 CPC classification numbers. Table 1 presents the distribution numbers of the top 10 level-4 CPC classification numbers.  
Table 1 indicates that most of the SOA technologies can be classified under the H01S3, H01S5, H04B10, G02B6, and H04J14 categories; Appendix A displays the definition of each CPC code. Under the CPC International Patent Classification definitions, H01S3 and H01S5 represent lasers and semiconductor lasers, respectively; H04B10 represents transmission systems employing electromagnetic waves other than radio-waves; G02B6 represents light guides; and H04J14 represents optical multiplex systems.  
The aforementioned analysis indicates that the SOA patents focused on lasers, semiconductors, transmission systems, light guides, and optical multiplex systems. The top 10 patentees were analyzed, and the results are presented in Table 2. Table 2 indicates that since 1990, the patentees with the longest average terms of patents are Lucent Technologies Inc., Murray Hill, US, Finisar Corporation, Sunnyvale, US, and Alcatel-Lucent Enterprise, Boulogne-Billancourt, FR. These companies have a long development history, and they began developing SOA technologies and filing related patents earlier than other companies did.
###### Abstract  
Measuring interpupillary distance and pupil height is a crucial step in the process of optometry. However, existing methods suffer from low accuracy, high cost, a lack of portability, and limited research on studying both parameters simultaneously. To overcome these challenges, we propose a method that combines ensemble regression trees (ERT) with the BlendMask algorithm to accurately measure both interpupillary distance and pupil height. First, we train an ERT-based face keypoint model to locate the pupils and calculate their center coordinates. Then, we develop an eyeglass dataset and train a BlendMask model to obtain the coordinates of the lowest point of the lenses. Finally, we calculate the numerical values of interpupillary distance and pupil height based on their respective definitions. The experimental results demonstrate that the proposed method can accurately measure interpupillary distance (IPD) and pupil height, and the calculated IPD and pupil height values are in good agreement with the measurements obtained by an auto-refracometer. By combining the advantages of the two models, our method overcomes the limitations of traditional methods with high measurement accuracy, low cost, and strong portability. Moreover, this method enables fast and automatic measurement, minimizing operation time, and reducing human errors. Therefore, it possesses broad prospects for application, particularly in the fields of eyeglass customization and vision inspection.  
interupillary distance; pupil height; optometry; ensemble regression trees; BlendMask
## 6 Results and Discussion  
To illustrate the performance of the proposed FT-CID model against the baseline IDS models, the experimental model evaluates the RPL-IIoT AMI dataset by varying the scenarios of the training data size and the number of communication rounds. To validate the results, we compared our FT-CID model with different algorithms in terms of accuracy, such as MLP, LR, and CNN, using the centralised setup to show significant improvements in intrusion detection accuracy. In this section, we also consider the influence of the communication rounds on intrusion detection performance.
## 2 Materials and Methods
### A Useful 'Equivalent' Problem: A Forced Infinite Elastic Plate
By taking the inverse of the first matrix in (17), we find that \(A\) and \(D\) are functions of \(\alpha\) given by  
\[A(\alpha) =\frac  
is the determinant of the matrix to be inverted in (17). The relation \(\mathcal{D}_{s}(\alpha)=0\) is known as the dispersion relation for symmetric Lamb waves. We may now use the expressions for \(A\) and \(D\) to find the transformed potentials and stresses. The symmetric part of the transformed potentials are given by  
\[\Phi(\alpha,y) =\frac{(\alpha^{2}-\beta_{t}^{2})\sin\Bigl{(}\frac{\beta_{t}}{2} \Bigr{)}P^{+}(\alpha)+2\mathrm{i}\alpha\beta_{t}\cos\Bigl{(}\frac{\beta_{t}}{2 }\Bigr{)}S^{+}(\alpha)}{\mathcal{D}_{s}(\alpha)}\cos(\beta_{t}y), \tag{22}\] \[\Psi(\alpha,y) =\frac{2\mathrm{i}\alpha\beta_{t}\sin\Bigl{(}\frac{\beta_{t}}{2} \Bigr{)}P^{+}(\alpha)+(\alpha^{2}-\beta_{t}^{2})\cos\Bigl{(}\frac{\beta_{t}}{2 }\Bigr{)}S^{+}(\alpha)}{\mathcal{D}_{s}(\alpha)}\sin(\beta_{t}y). \tag{23}\]  
From the potentials, we find the transformed stresses to be  
\[T_{xx}(\alpha,y) =\frac{F_{1}(\alpha,y)S^{+}(\alpha)+F_{2}(\alpha,y)P^{+}(\alpha) }{\mathcal{D}_{s}(\alpha)}, \tag{24}\] \[T_{xy}(\alpha,y) =\frac{G_{1}(\alpha,y)S^{+}(\alpha)+G_{2}(\alpha,y)P^{+}(\alpha) }{\mathcal{D}_{s}(\alpha)}, \tag{25}\]  
where \(\mathcal{D}_{s}\), \(F_{1}\), \(F_{2}\), \(G_{1}\), and \(G_{2}\) are analytic functions given by (21),  
\[F_{1}(\alpha,y) =2i\alpha\beta_{t}\biggl{(}\Bigl{(}\alpha^{2}-\beta_{t}^{2} \Bigr{)}\cos\biggl{(}\frac{\beta_{t}}{2}\Bigr{)}\cos(\beta_{t}y)-\cos\biggl{(} \frac{\beta_{t}}{2}\Bigr{)}\Bigl{(}K^{2}\alpha^{2}+(K^{2}-2)\beta_{t}^{2} \Bigr{)}\cos(\beta_{t}y)\biggr{)}, \tag{26}\] \[F_{2}(\alpha,y) =-\Bigl{(}\alpha^{2}-\beta_{t}^{2}\Bigr{)}\sin\biggl{(}\frac{ \beta_{t}}{2}\Bigr{)}\Bigl{(}K^{2}\alpha^{2}+(K^{2}-2)\beta_{t}^{2}\Bigr{)} \cos(\beta_{t}y)-4\alpha^{2}\beta_{t}\beta_{t}\sin\biggl{(}\frac{\beta_{t}}{2 }\Bigr{)}\cos(\beta_{t}y),\] (27) \[G_{1}(\alpha,y) =\Bigl{(}\alpha^{2}-\beta_{t}^{2}\Bigr{)}^{2}\cos\biggl{(}\frac{ \beta_{t}}{2}\biggr{)}\sin(\beta_{t}y)+4\alpha^{2}\beta_{t}\beta_{t}\cos \biggl{(}\frac{\beta_{t}}{2}\biggr{)}\sin(\beta_{t}y)\] (28) and \[G_{2}(\alpha,y) =\Bigl{(}\alpha^{2}-\beta_{t}^{2}\Bigr{)}\biggl{(}2i\alpha\beta_ {t}\sin\biggl{(}\frac{\beta_{t}}{2}\biggr{)}\sin(\beta_{t}y)-2i\alpha\beta_{t} \sin\biggl{(}\frac{\beta_{t}}{2}\biggr{)}\sin(\beta_{t}y)\biggr{)}. \tag{29}\]  
The solution for the Potentials (22) and (23), or stresses (24) and (25), fully describes the problem in terms of the Fourier transforms of the unknown functions \(s\), \(p\), \(q\), and \(r\). Whilst we do not know these functions exactly, we can approximate them from knowledge of their behaviour near a corner, which we will examine in Section 2.2.