esa-sceva/satcom-chunk-collection huggingface dataset:
# UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
## 5 Technical Validation  
### Experimentation  
SMT and NMT systems on both Korean-English and Korean-Vietnamese parallel corpora were implemented to evaluate the performance of UPC on MT systems. Our SMT implementation was based on Moses [1], while the NMT implementation was based on OpenNMT [4]. The Moses MT used statistical models, and the N-gram of the language model was set to four [17; 44]. OpenNMT systems utilized an encoder-decoder model with the following typical attributes: recurrent neural networks = 2 \(\times\) 512, word-embedding dimension = 512, and input feed = 13 epochs [45; 46]. We conducted those SMT and NMT systems for bidirectional translation (i.e., Korean-to-English and English-to-Korean). We used BLEU and TER to measure translation qualities.  
In each language pair, 5000 sentence pairs were selected for making the test set, and the rest were utilized for training. These test and training sets were tokenized, and those of alphabetical characters were lowercased by the [PERSON] tools.  
Our systems are thoroughly demonstrated as follows:  
* **Baseline**: Utilizes the initial Korean, English, and Vietnamese sentences in the UPC, shown in Table 8. Korean texts were denoted \"initial\", but they were normalized and tokenized using the [PERSON] tokenizer. English and Vietnamese texts were also normalized and tokenized using the [PERSON] tokenizer and converted to lowercase.
* **Word-sense Ann.**: Utilizes the Korean sentences after using UTagger (i.e., Korean morphological analysis and word-sense annotation), the English and Vietnamese sentences are the same forms of the baseline systems.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 3 Experiments
### Simulations  
First, the all-around view Compton camera was implemented and the measurement data of gamma rays were obtained using the library called Geant4 [28; 29]. Figure 8  
Figure 7: Loop closure of measurement path: the measurement path generation is completed when the loop is closed.  
illustrates the all-around view Compton camera implemented in Geant4. The camera consists of a 12 \(\times\) 4 \(\times\) 30 array of CdTe detectors whose width, height, and depth are 8, 12.5, and 2.21 mm, respectively. The width, height, and depth of the Compton camera with a cover are 138.6, 60, and 140 mm, respectively. Geant4 can simulate radiation tracks when interacting with matter based on the Monte Carlo method. The reliability of the library has been comprehensively validated and used in several studies [30]. Regarding the gamma-ray measurement using Geant4, we can obtain the data that present the position of the element interacting with the gamma ray and deposited energy at each element. Using the obtained data, a simple back-projection was performed to reconstruct the distribution of radiation sources using the previous measurements. The next measurement point was then determined via PCA. After all the measurements were completed, the final distribution of the radiation sources was reconstructed, and the positions of the radiation sources were localized. The simulation experiment is illustrated in Figure 9.
# Ising-Based Kernel Clustering +
## 4 Evaluation
### Experiments on Real Datasets  
For further analysis, this section conducts experiments using practical real-world datasets. The Yale Face Database (YALE) [30] and the Japanese Female Facial Expression Dataset (JAFFE) [31] are used for the experiments, and their details are shown in Table 1. Furthermore, several facial image examples from YALE and JAFFE are presented in Figures 15 and 16, respectively.  
The clustering performance is evaluated using Normalized Mutual Information (NMI). NMI is a normalization of the Mutual Information score to scale the results between 0 (no mutual information) and 1 (perfect correlation).  
First, an experiment is conducted to investigate an appropriate parameter \(\sigma\) because the performance of the proposed method depends on the parameter \(\sigma\). Next, the results are compared with state-of-the-art clustering methods. When performing clustering for complex image data, such as facial images, it is not always possible to convert the data structure to a linearly separable one with a single kernel. Therefore, multi-view methods using multiple kernels tend to show high performance [32]. [PERSON] et al. [33] proposed Structured Graph Learning with Multiple Kernel (SGMK), which extends Structured Graph Learning to multiple kernel clustering and showed that SGMK outperforms many clustering methods, such as Spectral Clustering [34; 35], Multiple Kernel K-means [36; 37], and structure learning approaches [38; 39]. Therefore, this paper compares the proposed method with SGMK from the viewpoint of NMI.  
Figure 17 is the NMI of the proposed method for YALE and JAFFE datasets. The results show that the NMI performance of the proposed method did not change significantly when \(\sigma\) is varied for both the YALE and JAFFE datasets. This suggests that using a single Gaussian kernel to transform the data structure is not sufficient for facial image clustering and that using multiple kernels to select the most appropriate kernel is more effective than adjusting the parameters of the Gaussian kernel.  
Table 2 shows that the Ising-based kernel clustering has lower NMIs than SGMK. This is because SGMK uses multiple kernels for data transformation and performs clustering based on the most suitable kernel, while Ising-based kernel clustering uses only a single Gaussian kernel.  
Multi-view clustering methods that use multiple kernels to transform input data into linearly separable high-dimensional data are one of the most promising ways to perform kernel clustering for practical real-world data. While Ising-based kernel clustering has the potential to obtain the ground state for a given objective function of kernel clustering, selecting the most appropriate kernel is required before optimization. Thus, developing an algorithm for Ising-based kernel clustering that uses multiple kernels will be discussed in the future.
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 6 Results and Discussion
### Scenario II  
This section studies the performance of the proposed method. This system has an intelligent controller mechanism that distributes the network load and provides alternative routes instantaneously for the incoming packets. The simulation parameters used in this scenario are identical to scenario I. The inter-event time is 4 ms, the buffer capacity is 2 Mbits, and the service time of the processor is 20 ms. In addition, to verify the robustness of the proposed four switches mechanism, this scenario has been compared with another IEC 61850 switch (SDS-3008 series) [24] and also with scenario I. The parameter specifications of the SDS-3008 switch are set as follows: the buffer capacity size is 3 Mbits (247 packets/sec), inter-event time set to 4 ms and the service time set to 15 ms.  
In this methodology, the maximum drop packets occurred in this scenario is 1267 packets at the time 1 s with traffic generated 2000 packets/sec, and the minimum drop packets occurred at time 200 ms with 51 packets dropped. Table 5 shows the percentage of the drop packets with different times ranging from 200 ms to 1 s. Figure 14 depicts the number of the drop packets with different time simulations in the four switches methodology. It is clear that the performance of the four switches technique shows an outstanding functionality, as compared with other techniques, shown here in Figure 15.  
As we can see from Figure 15, the number of the drop packets at the worst case of time 1 s with 2000 packets/sec generated is 1267 by utilizing four switches, whereas, the number of the dropped packet at the same time of one buffer, two buffers and SDS-3008 switch is 1822, 1622 and 1591, respectively. By providing alternative paths instantaneously to the coming packets with a short period of time, it minimizes the waiting time of the packets in the buffers that have a direct influence on the system behavior and increases its reliability and security. The control technique in this scenario works to check all the availability of the paths within a short time, and by controlling the paths' gates, the controller guides the packets to the optimal path to process it. This technique helps to minimize the drop packets significantly and increases the switch functionality. Figure 16 shows how the percentage of drop packets significantly minimized when utilizing the proposed methodology.  
Figure 14: Number of drop packets versus simulation time for four-switch architecture system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Time** & \begin{tabular}{c} **Number of Generated** \\ **Packets from 8 Ports** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Drop** \\ **Packets** \\ \end{tabular} &
\begin{tabular}{c} **Percentage of** \\ **Drop Packets** \\ \end{tabular} \\ \hline
200 ms & 400 & 51 & 12.75\% \\
400 ms & 800 & 371 & 46.375\% \\
600 ms & 1200 & 671 & 55.92\% \\
800 ms & 1600 & 971 & 60.6875\% \\
1 s & 2000 & 1267 & 63.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage of drop packets in four switches.
## 4 Detection and Mitigation Mechanism
### Idbn
#### 4.1.2 Intrusion Detection Based on IDBN  
The intrusion detection based on IDBN makes use of the DBN model to detect unknown targeted attack types of heavy-duty robots. The DBN model includes multi-layer RBM for feature extraction and the contrastive divergence (CD) learning method to repeatedly optimize the network weights. It can achieve good learning ability and adaptability to detect unknown samples by training known samples.  
In the DBN model, the processed data are used as the input data of the first visible layer of the RBM. The CD algorithm is used to carry out the layer-by-layer training on the RBM. Meanwhile, the RBM output values of the previous layer are used as the input values of next layer until the completion of multilayer RBM training [33].  
The process of IDBN training and detection is as follows:  
* A fixed amount of training data \(min\_batch\) was sampled randomly and inputted to the DBN at each time;
* The network weight would be updated once for each \(min\_batch\) number of training. This process would continue until all samples have been trained;
* After the multi-layer RBM training was completed, the BP neural network fine-tuned the parameters obtained by training the RBM through the back-propagation process;
* Through the repeated fine-tuning, the optimal value of related parameters could be obtained. These parameters include the connection weight matrix \(w\), the bias of visible unit \(a\) and the bias of hidden unit \(b\);
* Test data and corresponding attribute labels were entered in the trained DBN;
* The actual classification result of each test data was obtained by forward propagation calculation;
* The result was compared with the input attribute label to obtain the correct detection rate of the test sample.  
During this process, the data dimension of the test sample was the same as the training data.
## 3 Modified Equations Derived in the Framework of the Multi-Scale Method
### Frequency Down-Conversion in the Framework of the Long Pulse Duration Approximation
 \tag{15}\]  
The maximal LFW intensity is defined by the roots of the equation \(f(p_{1})=0\). As neither parameter \(A\) nor parameter \(B\) depend on \(\gamma\) and \(\Delta_{21}k\), the maximal LFW intensity depends only on the incident intensities of interacting waves. Obviously the nonlinear coupling coefficient and phase mismatching \(\Delta_{21}k\) influence only on a distance on which the maximal LFW intensity achieves. Since we want to choose the intensity of incident LFW that is as small as possible, the function \(p_{1}(z)\) should achieve its minimum in section \(z=0\). Therefore, its derivative must be equal to zero. As follows from the first Equation (12), this means either \(\varphi_{0}=0\) or \(\varphi_{0}=\pi\). Under this assumption, we solve the equation \(f(p_{1})=0\) for various values \(|A_{10}|^{2}\), \(|A_{20}|^{2}\) and depict the results of the computation on the plane (\(|A_{10}|^{2}\), \(|A_{20}|^{2}\)) (Figure 1). Thus, one can see that there are two regimes of the frequency conversion: low-efficient and high-efficient. In the first one, the conversion efficiency is lower than 20%; in the second one, the conversion efficiency is much higher and can be higher than 90%. We could also see in Figure 1 that if the intensity of incident IFW equals zero then the intensity of incident LFW must be greater or equal than 0.08: \(|A_{10}|^{2}\geq 0.08\) to achieve the high-efficiency mode of the frequency conversion. Thus, in accordance with data from Table 1, the following incident intensities of the LFW and HFW are enough for the high-efficient amplification of the pulse with the 4 \(\upmu\)m wavelength: 0.4976 GW/cm\({}^{2}\) and 5.7224 GW/cm\({}^{2}\). If the intensity of incident LFW is lower, then the amplification efficiency decreases significantly. However, with the increasing incident of the IFW intensity, the incident LFW intensity that is required to achieve the high-efficiency mode of the frequency conversion decreases. Moreover, if the incident IFW intensity is equal to \(|A_{20}|^{2}=0.4\), then the high-efficiency mode occurs even for a zero-value of the incident LFW intensity. The IFW and HFW incident intensities, corresponding to these dimensionless values, can be the following: 2.488 GW/cm\({}^{2}\) and 6.22 GW/cm\({}^{2}\). It should be noted that the computer simulation results obtained by solving the original problem mostly support this analysis provided on the basis of the modified equations. In particular, two different modes of the frequency conversion also occur if we describe the laser pulse interactions in the framework of the original problem (1).
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 5 Conclusions and Future Work  
In this work, we used NLP methods to summarize EHR progress notes from ICU patients and to extract diagnoses and problems, with the ultimate goal of predicting ICU patients' lengths of stay. Our study showcased the effectiveness of combining extractive and abstractive summarization techniques. Moreover, the generated summaries and extracted diagnoses proved valuable for predicting ICU LOS, offering potential benefits for ICU management, clinical decision-making, and patient care in real-world clinical settings.  
Moving forward, exploring the integration of additional clinical variables and refining the summarization techniques could enhance the predictive accuracy and utility of EHR-driven LOS predictions. While our LOS classification methods show significant improvements and robustness over the benchmark methods, especially in terms of precision and recall, they still need further enhancement. Future work could focus on refining these models, potentially through additional feature engineering and model optimization. This could include developing temporal features that capture trends over time, such as the progression of vital signs or lab results instead of categorizing them to \"low\", \"normal\", and \"high\" as we did in this work. Furthermore, model optimization techniques, like implementing feature selection methods, including recursive feature elimination (RFE), can help identify the most important features from the dataset and enhance the models' predictive performance by reducing the dataset's dimensionality. Additionally, gathering feedback from healthcare professionals regarding the generated summaries and LOS predictions can provide valuable insights into their usefulness, readability, and accuracy.
## 4 Experiments and Discussion
### Baselines  
In the experiments, we compared MFDGCN against the following eight baseline methods. HA [19]: The average value of all historical records in a certain period as the predicted value. VAR [20]: Used in the analysis of multivariate time series models, it can better reflect the fluctuation of traffic flow and reduce uncertainty. SVR [21]: Curve fitting using SVM and regression task analysis. DCRNN [8]: Traffic prediction using the encoder-decode framework and diffusion graph convolution. STGCN [9]: Designing neural networks with convolutional layers for spatio-temporal prediction with fewer parameters and faster training. STSGCN [11]: Design of multiple modules with different time periods to effectively capture heterogeneity in the local spatio-temporal graph. GWnet [10]: Stack spatio-temporal layers for better short-term predictions. GMAN [12]: Multi-attention mechanism modeling to solve the problem of poor long-term traffic prediction.  
Figure 7: PeMS_BAY data set for traffic prediction. Black points mean location of sensors, each sensor measures traffic data every 30 s. PeMS_BAY constructed a road network based on the measured data.
## 3 Results and Discussion
### CO Diffusion Law
#### 3.2.4 Effect of \(L_{d}\) on the CO Diffusion  
Figure 17 shows the CO concentration profiles at each point within the model for \(L_{d}\) values of 12 m, 16 m, and 20 m, respectively. From the lateral view, the appearance of CO concentration and the first concentration wave peak (black lines) are detected at almost the same moment for different \(L_{d}\) values. At the open end of the model, the moment of CO emergence is delayed with the increase in \(L_{d}\). This is due to the objective effect of the model length, which implies that the larger the \(L_{d}\) value, the longer the time required for all CO to be discharged from the model.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Process Framework  
The proposed CNN-LSTM-SAM-Attention framework for landslide prediction integrates four modules: CNN (feature extraction), SAM (spatial attention), LSTM (temporal modeling), and Classification. The overall workflow is depicted in Figure 1. Each module is detailed in subsequent subsections.  
Figure 1: The process framework diagram of the CNN-LSTM-SAM-Attention model.
# Ru(terpy)-Based Conducting Polymer in Electrochemical Biosensing of Epinephrine
###### Abstract  
A heteroleptic [Ru(terpy)]2\({}^{+}\) (terpy = 2.2\({}^{\prime}\).6\({}^{\prime}\).2\({}^{\prime}\)-terpyridine) complex was electrochemically polymerized to give the corresponding metal-containing conducting polymer on gold and glassy carbon electrodes. The polymerization of the Ru(II) complex was allowed by a terthiophene functionalization on one of the two terpy coordinating fragments, whereas the presence of -COOH substituents on the second terpy ligand enabled the film to immobilize a tyrosinase enzyme by cross-linking with glutaraldehyde. Then, the Ru(terpy) conducting polymer worked as a transducer as well as an immobilizing agent in the design of amperometric biosensors for the determination of epinephrine. The electrochemical behavior of enzymatic sensors containing Ru(terpy)-based conducting polymers was investigated by differential pulse voltammetry and chronoamperometry. Analytical performances and kinetic parameters were calculated, suggesting a potential application of the reported biosensors in the determination of epinephrine in pharmaceutical products.  
Ru(II) complex; terpyridine ligands; biosensors; tyrosinase; epinephrine
## 4 Results
### Data Preprocessing
#### 4.2.1 Standardization  
The features in the data have different units, and the distribution ranges are different. Thus, using original features will cause some machine learning algorithms to only focus on the features with larger values that cannot accurately train the model. Therefore, the feature distribution is converted to the same range through a standardized method so that all features have the same influence when the model is learning. The commonly used methods are z-score standardization and maximum and minimum standardization. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Features Expression** & **Description** \\ \hline Age & Age & \\ Sex & Sex & Female: 0, Male: 1 \\ Chest Pain Type (Cp) & Types of chest pain & Typical angina: 0, Atypical angina: 1, \\ Resting Blood Pressure (trestbps) & Blood pressure at rest & Nonangina: 2, Asymptomatic: 3 \\ Serum Cholesterol (chol) & Blood pressure at rest & (mmHg) \\ Fasting Blood Sugar (fbs) & Serum cholesterol content & (mg/dL) \\ Resting Electrocardiographic & Amount of glucose in the blood on an & (\textgreater{}120 mg/dL, false: 0, true: 1) \\ Results (restecg) & ECG information at rest & Normal: 0, Abnormal ST-T fluctuation: 1, \\ Maximum Heart Rate Achieved (thalach) & Maximum number of heart beats & Left ventricular hypertrophy: 2 \\ Exercise-Induced Angina (exang) & Dose angina occur during exercise & No: 0, Yes: 1 \\ Old Peak & exercise that is significantly different & \\ Peak Exercise Slope (slope) & Slope of the ST phase at peak exercise & Rise: 1, Flat 2, Fall: 3 \\ Number of Major Vessels Colored Using & Number of major blood vessels that can & \\ Fluoroscopy (ca) & be inspected using fluorescence & \\  & Information about the distribution of & \\ Thallium Scan (thal) & blood in the blood vessel through & Normal: 3, Inherent defect: 6, \\  & thallium test to check whether the blood & Repairable defect: 7 \\  & vessel is defective & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data characteristics.  
study uses maximum and minimum standardization to scale all unique values to between 0-1. The formula is as follows:  
\[x_{new}=\frac{x_{ori}-x_{min}}{x_{max}-x_{min}} \tag{27}\]  
where \(x_{ori}\) denotes the original feature value; \(x_{min}\) represents the minimum feature value; and \(x_{max}\) denotes the maximum feature value. The calculated \(x_{new}\) is the new feature value between 0 and 1 after standardization.
## 4 A Generalized Framework for Tactile Internet in the beyond 5G Era
### Network Domain  
This section is typically located between the master and control/slave domains and serves as a two-way communication medium, connecting the operator to the tele-operator (remote) environment. It is made up of routers and gateways, which provide a medium for two-way communication. The master domain sends the haptic input command signals. Thus, the input signals are routed through routers, switches, gateways, base stations, access points, a database server, and an AI-enabled tactile support engine before reaching the slave section [59; 63].