esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Phantom Acquisition
#### 2.3.3 Data Acquisition  
Data were acquired using the Verasonics Vantage 256 research ultrasound scanner (Verasonics Inc., Redmond, WA, USA) and P4-2v phased array transducer. This is a small phased array transducer with 0.3 mm element pitch and 64 elements. Three studies were conducted:  
1. The imaging target was the ATS 549 tissue mimicking phantom (CIRS, Norfolk, VA, USA), and the transducer was attached to the translation stage as described above. The transducer was swept over 5 cm in 0.033 mm steps, alternating between plane wave transmission and left/right diverging transmissions (virtual source and 11 elements with f-number \(-\)0.75) in that order to mimic a continuous sweep with spacing of 0.1 mm between repetitions.
2. The transducer was used with the linear slide device on the ATS 549 phantom. The phantom was positioned such that point targets were located below lesion targets, and both were visible in the image. The transducer was coupled with gel, and the device was held steady on the phantom surface by hand. The transducer was swept along the track covering approximately 5 cm in 1 s, although the temporal dynamics and extent varied from sweep to sweep. During the sweep, transmissions were alternated as described in Figure 2b at an overall pulse repetition frequency of 500 Hz.
3. The manual sweep was repeated with an ex vivo tissue sample between the transducer and an ATS 539 phantom. The phantom was positioned so that lesion targets were located below point targets, and both were visible in the image. Three different store-bought meats were used to study whether the intervening layer affected motion tracking. Pork chop (12 mm thick), beef loin (10 mm thick) and chicken breast (9-19 mm thick) were used as varying models. Samples were prepared to be wider than the slide device so that they rest entirely on top of the sample. The degassed samples were placed on top of plastic wrap, and all layers were coupled with gel. The same sweep sequence was used as above.  
Figure 3: Mechanical slide fixture allowing 1-D movement along the array dimension. (**a**) Dimensioned diagram (units mm). (**b**) Rendering. (**c**) 3-D printed device.  
In each case, channel radio frequency (RF) data were stored for offline processing. For each, a reference image (i.e., no sweep) was also acquired using steered plane waves from \(-45\) to \(45\) degrees.
## 4 Results and Discussion
### Shell vs. Solid Models  
In this section, the results obtained from the shell model are compared with those obtained from the solid model. Figure 9 shows that the shell model matches the solid for the lower relative density portion of the plot, but the two models rapidly diverge above 40% relative density. At these higher relative densities, the solid model more accurately matches the expected result by approaching 100% of the neat stiffness as it approaches 100% relative density. As such, the solid model results have been used for all comparisons to experimental data and for curve fitting. This additional accuracy does come at the cost of increased complexity, and computation time. In addition, to simulate different relative densities using solid models requires that the thickening process be performed to generate a new mesh for each relative density to be analyzed, where the shell-model's relative density can be adjusted by simply altering the shell-thickness parameter. For these reasons, it would be best to use shell models in any instance where their accuracy is sufficient such as on the low end of the relative density range.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Data Sources
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Station Name**} & \multicolumn{2}{c}{**Geographical Locations**} & \multicolumn{2}{c}{**Annual Mean**} & \multicolumn{2}{c}{**Annual Mean**} \\  & **Latitude** & **Longitude** & **Rainfall (mm)** & **Temperature (\({}^{\circ}\)C)** \\ \hline Chau Doc & \(10^{\circ}42^{\prime}12.7^{\prime\prime}\) N & \(105^{\circ}07^{\prime\prime}58.7^{\prime\prime}\) E & 1360 & 27.0 \\ Cao Lanh & \(10^{\circ}28^{\prime}16.6^{\prime\prime}\) N & \(105^{\circ}38^{\prime}42.1^{\prime\prime}\) E & 1356 & 27.0 \\ Moc Hoa & \(10^{\circ}45^{\prime}12.6^{\prime\prime}\) N & \(105^{\circ}56^{\prime}00.5^{\prime\prime}\) E & 1564 & 27.3 \\ Can Tho & \(10^{\circ}01^{\prime}33.9^{\prime\prime}\) N & \(105^{\circ}46^{\prime}07.8^{\prime\prime}\) E & 1544 & 26.6 \\ My Tho & \(10^{\circ}21^{\prime}03.3^{\prime\prime}\) N & \(106^{\circ}23^{\prime}53.9^{\prime\prime}\) E & 1349 & 26.7 \\ Cang Long & \(9^{\circ}59^{\prime}33.7^{\prime\prime}\) N & \(106^{\circ}12^{\prime}11.3^{\prime\prime}\) E & 1672 & 26.8 \\ Ba Tri & \(10^{\circ}02^{\prime}03.6^{\prime\prime}\) N & \(106^{\circ}35^{\prime}37.3^{\prime\prime}\) E & 1473 & 26.8 \\ Soc Trang & \(9^{\circ}36^{\prime}05.2^{\prime\prime}\) N & \(105^{\circ}58^{\prime}24.9^{\prime\prime}\) E & 1859 & 26.8 \\ Bac Lieu & \(9^{\circ}17^{\prime}43.5^{\prime\prime}\) N & \(105^{\circ}42^{\prime}50.1^{\prime\prime}\) E & 1712 & 26.8 \\ Ca Mau & \(9^{\circ}10^{\prime}28.5^{\prime\prime}\) N & \(105^{\circ}10^{\prime}41.5^{\prime\prime}\) E & 2366 & 26.7 \\ Rach Gia & \(10^{\circ}00^{\prime}44.5^{\prime\prime}\) N & \(105^{\circ}0^{\prime}37.7^{\prime\prime}\) E & 2057 & 27.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of meteorological stations in the Mekong Delta.
## 5 Morse Code Decoding
### Accuracy Computation  
To quantify the accuracy of our recognition algorithm, the Levenshtein distance (edit distance) is used by counting the minimum number of edit operations required to transform one string into the other. The modification operations include: (a) Insert a character; (b) delete one character; and (c) replace one character with another. Let us denote \(R=[r_{1}\;r_{2}\;\dots\;r_{i}]\) and \(D=[d_{1}\;d_{2}\;\dots\;d_{j}]\) as the raw data string and the decoded data string, respectively. \(m[i,j]\) represents the edit distance between the first \(i\) characters of \(R\) and the first \(j\) characters of \(D\), and is expressed as [29]  
\[m(i,j)=\left\{\begin{array}{cl}0&i=0,j=0\\ j&i=0,j>0\\ i&i>0,j=0\\ \min(m(i-1,j)+1,m(i,j-1)+1,m(i-1,j-1)+flag)&i>0,j>0\end{array}\right., \tag{7}\]  
where the flag variable is the indicator function equal to 0 when \(R[i]=D[j]\), and equal to 1 otherwise. Then, we have the recognition accuracy,  
\[A=1\;-\;\frac{\left|m(i,j)\right|}{\max(|R|,|D|)}, \tag{8}\]  
where \(|R|\) and \(|D|\) represent the length of the raw and decoded data string, respectively. In addition, the procedure of this dynamic programming algorithm to derive the edit distance is shown in Algorithm 1:  
```
1:procedureLevenshtein Distance (\(R\), \(D\))
2:int \(m[i,j]=0\)
3:for each \(i\leftarrow\)1 to \(|R|\)do
4:\(m[i,0]=i\)
5:endfor
6:for each \(j\leftarrow\)1 to \(|D|\)do
7:\(m[0,j]=j\)
8:endfor
9:for each \(i\leftarrow\)1 to \(|R|\)do
10:for each \(j\leftarrow\)1 to \(|D|\)do
11:\(t\leftarrow\) if (\(R[i]=D[j]\)) then 0 else if 1
12:\(m[i,j]=\min\left\{m[i-1,j-1]+t,m[i-1,j]+1,m[i,j-1]+1\right\}\)
13:endfor
14:endfor
15:return \(m[|R|,|D|]\)
16:endprocedure
```  
**Algorithm 1** String similarity algorithm
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model
### TCN-TPA Network Training and Prediction Layers  
Short-term power load forecasting is a multivariate time series task because short-term power load forecasting is not only related to historical load but also to variables such as meteorological factors. For this multivariate time series forecasting task, this paper will use the TCN-TPA network model for short-term power load forecasting. Firstly, the TCN network can not only control the memory length of the model by adjusting the expansion rate and changing the convolutional kernel but also the TCN network has the ability to process data in a massively parallel way, which makes the training and validation time of the prediction model shorter. Secondly, using the temporal attention mechanism not only allows assigning different weights to the input information to highlight the key influencing factors but also breaks away from the typical attention mechanism's tendency to select time steps that are more relevant to time series prediction. The temporal attention mechanism can better capture temporal information and span multiple time steps so that temporal patterns can be extracted for each load component and focus on relevant meteorological factors to improve prediction precision.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Discussion  
IoT-based smart patient monitoring systems are receiving attention, especially in the context of dealing with a large number of patients and those with acute heart failure, where continuous monitoring is needed. This study presents an IoT-based monitoring system along with a deep CNN model for heart failure detection. Experiments are performed to analyze the effectiveness of the proposed CNN within the context of other machine learning, deep learning, and transfer learning models such as VGG-16 and AlexNet. Experimental results show the best results from the proposed CNN model compared to other employed models. The classification accuracy of the CNN model is 0.9368, which is better than other models.  
The proposed model outperforms all machine learning and deep learning models. The accuracy, precision, recall, and F1 score performance of the proposed model is high. In order to validate the proposed CNN, 10-fold cross-validation is applied, which also shows its efficacy. Moreover, a comparison of the proposed approach is carried out with existing works regarding different features such as the number of features used, the uses of AI approaches, and IoT and patient monthly record-keeping, and results are presented in Table 12. These comparisons indicate that the proposed system is better than the existing ones.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Training Time** \\ \hline Proposed approach & 24 min \\ VGG-16 & 29 min \\ AlexNet & 32 min \\ \hline \hline \end{tabular}
\end{table}
Table 10: Training time of classifiers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold Number** & _Accuracy_ & _Precision_ & _Recall_ & _F1-Score_ \\ \hline F1 & 0.951 & 0.961 & 0.912 & 0.922 \\ F2 & 0.921 & 0.970 & 0.934 & 0.962 \\ F3 & 0.931 & 0.932 & 0.932 & 0.943 \\ F4 & 0.981 & 0.970 & 0.994 & 0.953 \\ F5 & 0.940 & 0.934 & 0.984 & 0.916 \\ F6 & 0.961 & 0.962 & 0.974 & 0.923 \\ F7 & 0.942 & 0.970 & 0.961 & 0.914 \\ F8 & 0.941 & 0.941 & 0.954 & 0.973 \\ F9 & 0.920 & 0.942 & 0.945 & 0.981 \\ F10 & 0.974 & 0.954 & 0.975 & 0.994 \\ \hline
**Average** & **0.9462** & **0.9398** & **0.9565** & **0.9481** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results for 10-fold cross-validation of CNN model.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 5 Discussion
### Challenges and Limitations
* **Efficiency improvements:** The overall efficiency of the system can be raised by continuing research into more effective electrolysis technologies, such as high-temperature electrolysis and sophisticated proton exchange membrane (PEM) electrolyzers. Hydrogen-based energy storage can also become more viable through creating reversible fuel cells and increasing fuel cell efficiency. Some of the highlighted potential solutions for addressing the barriers hindering the widespread adoption of these green hydrogen technologies have been proven to mitigate the challenges. While some are just hypothetical solutions that could be implemented and improved with time, ongoing research and development are steadily improving the viability of these integrated systems.
## 3 Case Study
### Analysis of Influence Intensity
#### 3.3.2 Influence Intensity Analysis from the Perspective of Representative Stations  
Babaoshan station and Military Museum station were selected as a representative ordinary station and transfer station, respectively, for comparative analysis. The variation in the influence intensity at these two stations is illustrated in Figures 6 and 7, respectively.  
As shown in Figure 6, the influence intensity at Babaoshan station increased from 7:30 a.m. to 8:00 a.m. After the disruption was resolved, the influence intensity gradually decreased until 8:45 a.m. The variation in the influence intensity at Military Museum station was more severe and complex, presenting an \"M\" shape, as shown in Figure 7. The maximum influence intensity at Military Museum station on line 1 and line 9 occurred at around 8:00-8:15 a.m. and 8:45-9:00 a.m., respectively. The influence intensity gradually decreased after 9:00 a.m.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Polarization Aspects  
In the context of radio signals, polarization refers to the orientation of the electric-field component with respect to the magnetic-field component of the electromagnetic wave as it propagates through space. It can be, for example, linear (i.e., electric and magnetic fields are perpendicular to each other and each of them oscillates in a single plane), circular (both electric and magnetic fields rotate in a circular motion with respect to the wave propagation direction, and they are perpendicular to each other), or elliptical (similar to the circular orientation, but with elliptical motions instead of circular ones). Traditionally, the effects of polarization have been ignored in the context of positioning and sensing, which has also frequently been the case in the context of wireless communication research. However, polarization can affect resolution and accuracy in sensing and positioning, and mismatched polarizations can lead to significant signal loss and reduced communication efficiency. For example, a proper alignment of polarization between the transmitter and receiver can reduce interference and improve the SNR; also, using different polarizations on targets (i.e., polarization diversity) can enhance target detection and resolution. The potential benefits of integrating the polarization dimension into localization applications has recently been studied in [64]. The open research questions include how to avoid ambiguities that arise when the nuisance parameters (i.e., those parameters not relevant in the JPSAC solution) are jointly modeled or searched for in a combined manner, how to deal with challenging NLOS and 3D scenarios that increase the estimation complexity, how to deal with the complexity of polarimetric data, and how to carry out an accurate calibration of polarimetric sensors.
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 3 Random Forest Assisted Deep Neural Network Model
### Conventional Random Forest Approach
The key enhancements are as follows:  
* _Incorporation of sequential dependencies:_ Long-term and short-term dependencies within the 251-point voltage signal are modeled using LSTM and MGU. These components capture the temporal structure of the data and provide RF with enriched features that reflect sequential patterns and dependencies. * _Optimization with MGU for training and inference speed:_ Instead of relying solely on LSTM layers, MGU is utilized to optimize training and inference speed. MGU reduces computational complexity while maintaining the ability to model sequential dependencies effectively, making the system more efficient for real-time applications. * _Extraction of nonlinear relationships:_ A 1D CNN is employed to extract local patterns and nonlinear relationships from the voltage signal. These patterns, often inaccessible to RF, are passed as high-quality features to improve its regression performance. * _Prioritization of critical patterns:_ Multihead Attention highlights the most influential regions within the sequential data, enabling RF to focus on the key segments that are more predictive of the target force value. * _Mitigation of low target variance:_ The enriched features derived from deep learning components introduce additional context and variability, allowing for RF to perform better even when the target variable exhibits low variance. * _Integration of contextual information:_ The effects of constant parameters such as pressure and rotational speed are implicitly captured by the deep learning layers. These parameters are then incorporated into RF's feature space, reducing the need for explicit feature engineering. * _Enhancing interpretability through feature integration:_ By combining the outputs of CNN, LSTM, MGU, and Multihead Attention, the method produces a comprehensive feature set that bridges the gap between raw sequential data and RF's decision-making process. * _Improved efficiency in feature engineering:_ The reliance on manual feature extraction is significantly reduced by leveraging automated feature generation from deep learning architectures, thus streamlining the process and minimizing errors.
# The 3D Product Model Research Evolution and Future Trends: A Systematic Literature Review
## 4 Discussion
### The 3D Product Model Research Evolution
#### 4.1.4 1990s  
While product design continued to be the focus of the 3D product model research in the 1990s, the emergence of new technologies enabled research to begin on virtual assembly and using the 3D product model in virtual environments. The research on the virtual assembly of 3D products included the implementation of force feedback systems from the virtual environment to the physical environment at the Institute of Technology in Japan, combining human movement in the assembly of 3D products in the virtual environment with ergonomics research at Caterpillar and using the virtual environment for virtual assembly training at Motorola [75; 110; 279]. The virtual environment and 3D product model were also used at Iowa State University's Virtual Reality Applications Center to identify the impact that part shapes have on the stresses on that part and make modifications in real time [222]. Technology and research also emerged in the 1990s to not only apply the virtual environment to the physical world but also reverse engineer the physical world into the 3D virtual environment. Scanning technologies and coordinate measuring machines (CMM) were used to capture key data points of physical objects and translate them into 3D models [66; 118].
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 3 Experiments and Evaluation
### Experiment Settings  
The design and the effectiveness of the proposed framework are evaluated on real-world vulnerability data sets, aiming to examine whether the generated embeddings are effective for vulnerable function classification. Experiments are conducted on the three open-source projects: FFmpeg, LibTIFF, and OpenSSL. To simulate the scenario that there are usually insufficient labeled vulnerable functions available, we partition the data sets into two parts: 33% training and 67% test, as shown in Table 2. We keep the ratio of training and test partition unchanged in all our experiments. To ensure the validity of reported results, we partition the data set of each project three times to generate three different training and test sets for each run. The final results we obtain are the average of the results from three runs.  
The data imbalance issue is addressed using the cost-sensitive learning. In practice, the number of vulnerable functions is significantly smaller than the non-vulnerable ones in real-world projects, which may bias the learning of neural models. Therefore, during the training phase in Stage 1 (shown in Figure 1), cost-sensitive learning is applied by assigning different weights to vulnerable and non-vulnerable classes, so the loss function is adjusted to balance two imbalanced classes. In this paper, the following equation is used to obtain the weights of each class:  
\[class\_weight=\frac{total\_samples}{n\_classes*one\_class\_samples} \tag{1}\]  
where the value of \(n\_classes\) is 2, denoting the two classes which are vulnerable functions and non-vulnerable ones. The \(one\_class\_samples\) refers to the number of samples in one class. During the training phase, the misclassification cost of the samples in the vulnerable class is higher than that of non-vulnerable ones, enabling the neural network to overcome the data imbalance issue.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Source**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**\# of Functions Used/Collected**} \\ \cline{3-4}  & & **Vulnerable** & **Non-Vulnerable** \\ \hline \multirow{3}{*}{Open-source projects} & FFmpeg & 213 & 5701 \\ \cline{2-4}  & LibTIFF & 96 & 731 \\ \cline{2-4}  & OpenSSL & 143 & 7068 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data set used in the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **\# of Vulnerable** & **\# of Non-Vulnerable** & **\# of Vulnerable** & **\# of Non-Vulnerable** \\  & **Functions** & **Functions** & **Functions** & **Functions** \\ \hline FFmpeg & 70 & 1881 & 143 & 3820 \\ \hline LibTIFF & 32 & 241 & 64 & 490 \\ \hline OpenSSL & 47 & 2332 & 96 & 4736 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The training/test partitions of the data sets. We set 33% of the training samples to simulate the case where there is a limited number of labeled vulnerability data.