esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 6 Echocardiography
### Classification  
[PERSON] et al. [35] applied a deep learning approach to the classification of echocardiographic views and segmentation of LV hypertrophy. Their data-efficient, semisupervised model was a combination of a CNN, GANs, and UNet. This research work can provide support to doctors and cannot be used as a replacement for the clinician. Moreover, a limited dataset was used due to patients' privacy and consent issues. Ejection fraction (EF)is a measurement which determines the amount of blood the LV pumps out with each heartbeat, and it can be calculated through echo. [PERSON] et al. (2018) proposed a pipeline in which the echocardiography video sequences and metadata of a patient were given as input to a 3D CNN. They built a custom 3D CNN with 3D convolutions, asymmetric kernels, and residual learning blocks integrated into the 3D convolutional layers. The deep learning model consists of three main blocks, and its input consists of thirty sequential frames of echocardiography sequence. The initial layers are used for obtaining a smaller representation of the data, which is used as input in the middle layers. The middle layers, which use small asymmetric filters, are responsible for most of the computation. Finally, the top layers converge the activations into a vector that is fed to a softmax classifier. Their research aimed to classify a person's health based on EF. Their accuracy is 78%, which may not be sufficiently high for practical use. Deep learning techniques are also used for viewpoint classification from echocardiography videos ([PERSON] et al., 2018; [PERSON] et al., 2018). A fused approach was introduced, wherein two CNNs were trained; spatial and temporal information was combined at the end for the final classification score. The spatial CNN network processes the original echo video images to automatically extract the spatial features, while for the temporal CNN network, the optical flow approach is applied to obtain acceleration and velocity images. The method gave 96.3% accuracy, which was superior to other state-of-the-art approaches. The A5C had the worst classification score due to a limited dataset for that view. Another research work was carried out by [PERSON] et al. (2018) for the classification of normal and abnormal echocardiography videos. They proposed a two-stream deep network which extracted the optical flow and spatial context for the classification of echocardiography videos. A CNN extracts the spatial features, and each LSTM with attention learns the temporal features. For the output, the features from these two streams are fused. They achieved an accuracy of 91% for the dataset of 170 videos.  
For classification tasks in echocardiography videos, CNN and LSTM seem popular among researchers. The evident limitation is difficulty in classifying look-alike cardiac views. The reason behind this is the lack of training data for some classes and the noisy nature of the echocardiography modality.
## 5 Post-Evaluation Analysis
### Query-by-Example Spoken Term Detection
#### 5.2.3 System Analysis for Single and Multi-Word Queries  
An analysis for single and multi-word queries in the QbE STD task was carried out and results are presented in Tables 29 and 30 for MAVIR and RTVE data, respectively. They show better performance for multi-word queries than single-word queries, since multi-word queries are typically longer than single-word queries. This produces less FAs so that the final performance improves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**INL**} & \multicolumn{2}{c}{**OOL**} \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0142 & \(-\)0.1196 & 0.0000 & 0.0000 \\ \hline E2E & 0.0000 & \(-\)378.6430 & 0.0000 & \(-\)510.4187 \\ \hline E2E + ZNORM & 0.0000 & \(-\)39.2731 & 0.0000 & \(-\)33.2913 \\ \hline \hline \end{tabular}
\end{table}
Table 26: System results of the QbE STD task for MAVIR test data for in-vocabulary (INV) and out-of-vocabulary (OOV) queries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**INL**} & \multicolumn{2}{c}{**OOL**} \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0164 & 0.0164 & 0.0000 & 0.0000 \\ \hline E2E & 0.0388 & \(-\)32.6405 & 0.0246 & \(-\)37.1246 \\ \hline E2E + ZNORM & 0.0000 & \(-\)106.8322 & 0.0000 & \(-\)97.8272 \\ \hline \hline \end{tabular}
\end{table}
Table 28: System results of the QbE STD task for SPARL20 test data for in-vocabulary (INV) and out-of-vocabulary (OOV) queries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**INL**} & \multicolumn{2}{c}{**OOL**} \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0164 & 0.0164 & 0.0000 & 0.0000 \\ \hline E2E & 0.0388 & \(-\)32.6405 & 0.0246 & \(-\)37.1246 \\ \hline E2E + ZNORM & 0.0000 & \(-\)106.8322 & 0.0000 & \(-\)97.8272 \\ \hline \hline \end{tabular}
\end{table}
Table 29: System results of the QbE STD task for MAVIR test data for single-word (SING) and multi-word (MULTI) queries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SING**} & \multicolumn{2}{c}{**MULTI**} \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0033 & \(-\)0.1224 & 0.1667 & 0.1667 \\ \hline E2E & 0.0000 & \(-\)382.1081 & 0.0833 & \(-\)584.4417 \\ \hline E2E + ZNORM & 0.0000 & \(-\)38.6250 & 0.0000 & \(-\)38.1104 \\ \hline \hline \end{tabular}
\end{table}
Table 27: System results of the QbE STD task for RTVE test data for in-vocabulary (INV) and out-of-vocabulary (OOV) queries.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### Scenarios Description
#### 5.2.3 Terms and Key Performance Indicators  
The following key performance indicators were defined to evaluate the performance impact of the PEAR algorithm.  
Overconsumption: The PEAR performance in terms of energy balancing will be characterized by the overconsumption metric. It represents the average amount of energy consumption of a field device exceeding the energy profile constraints. The cluster overconsumption  
Figure 7: The ISA100.11a certified hardware devices employed in the experimental setup at CDS premises (Cluj-Napoca, Romania)—two out of three test batches.  
sumption represents the total energy consumption exceeding the energy profile considering all devices within a cluster. The overconsumption metric is expressed in mWh and indicates, on a field devices or cluster basis, the efficiency of the energy resources usage in terms of energy balancing, predictability, and cluster lifetime.  
Sink nodes: The sink nodes are the field devices placed on lower topology layers and routing data packets from multiple child devices.  
Energy margin: The analysis of a network with one cluster reveals that the average energy consumption is 66.12 mWh/day with a standard deviation of 5.26 mWh/day corresponding to a coefficient of variation of 7.41%, regardless of the energy profiles being set or the number of topology changes. When describing the PEAR performance, the term energy margin will be used to indicate the difference between the energy profile and the average field device consumption. The energy margin value will not be used to indicate the PEAR performance but rather as a tool for interpreting the results.  
Success rate: The success rate represents the percentage of the field devices within a cluster respecting the energy profile.  
PEAR stability: The PEAR stability state is achieved when all possible energy balancing topology changes have been exhausted. On the other hand, the PEAR instability state is characterized by a cyclic loop of topology changes that do not result in lower overconsumption jumps or an overall better energy balancing.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 2 RFI Classification and Formulation
### Interference Formulation  
SAR systems operate by transmitting electromagnetic waves of specific wavelengths and then recording the energy reflected after interacting with distant objects. In a specific receiving time window, the received echo can be expressed as a linear mixture of the target echo, interference, and noise--namely [24]:  
\[x(k)=s(k)+i(k)+n(k), \tag{1}\]  
where \(k\) denotes fast time samples, and \(s(k)\), \(t(k)\), and \(n(k)\) denote the target echo, RFI, and thermal noise, respectively.  
Being extensively researched, the mathematical model of RFI signals usually can be divided into three categories: namely, the narrow-band signal, pulse signal, and wide-band signal. Several signals are selected below as representative RFI signals that are easily encountered in practice.  
Continuous-Wave Interference:  
\[i(k)=\sum_{l=1}^{L}A_{l}(k)\cdot\exp(j2\pi f_{l}k+\phi_{l}), \tag{2}\]  
Chirp-Modulated Interference:  
\[i(k)=\sum_{l=1}^{L}A_{l}(k)\cdot\exp[j(2\pi f_{l}k+\pi g_{l}k^{2})], \tag{3}\]  
Figure 2: A rough classification of SAR interference [26].  
Sinusoidal-Modulated Interference:  
\[i(k)=\sum_{l=1}^{L}A_{l}(k)\cdot\exp[jg_{l}\sin(2\pi f_{l}k+\phi_{l})], \tag{4}\]  
where \(A_{t}(k)\), \(f_{l}(k)\), \(g_{l}\), and \(\phi_{l}\) denote the amplitude, frequency, modulation factor, and initial phase, respectively. \(L\) represents the number of interfering components.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### Numerical Simulation Techniques  
Numerical methods and computational techniques have become essential tools for engineers and researchers to tackle previously difficult technical and scientific challenges [94]. Much of the research on understanding and predicting rockbursts has been conducted using numerical modeling. According to [PERSON] and [PERSON] [102; 103], contemporary simulation methodologies in rock mechanics can be divided into three categories: continuum approaches, discontinuum approaches, and hybrid approaches. Spalling is a significant factor in rock faces subjected to dynamic loads from blasting or rockbursts. The use of numerical methods to study rock mechanics problems has been common since the 1960s and was employed in the 1970s to study rockburst difficulties. Numerical simulation is an effective technique for assessing rockburst hazards and improving mining design. The finite difference method (FDM) and the discrete element method (DEM) are commonly used for numerical modeling of rockbursts. The discontinuum approach represents large displacements and nonlinear constitutive behaviors in rock masses and joints [104; 105; 106; 107; 108; 109]. The primary damage area of rockbursts is focused at the excavation face and linear material behavior is demonstrated in the far-field zone without cracks [103]. [PERSON] [90] used the FLAC/PFC coupled numerical model to study excavation-induced acoustic emission in large-scale underground excavations, while [PERSON] [110] suggested a coupled numerical technique for investigating the influence of excavation on ground motion. Figure 5 shows the evolution of various numerical approaches used in simulating rockbursts.  
Current numerical simulations for rockbursts can be divided into two groups based on their goal: understanding the mechanisms of rockbursts and engineering applications. Advanced simulation methods such as DEM, PEM, and PD are needed for understanding the mechanisms of rockbursts, but their higher calculation costs may limit their use for engineering purposes. Nominal techniques for modeling rockbursts often perform better on discontinuity difficulties, such as crack propagation and branching, but struggle to identify rock blocks created by fissures. The simulation should be able to estimate the kinetic energy of each block to evaluate the overall severity of the rockburst. Simulating structural rockbursts can be more challenging than strainburst simulation. FDEM has been used to study the interaction between existing discontinuities and new cracks during fault-slip rupture. A 3D simulation is necessary to achieve an accurate prediction of rock mass strength.
# Soil Spectral Behavior Related to Its Load-Bearing Capacity Based on Moisture Content
## 2 Soil Type Used and the Measuring Techniques
### Spectral Behavior Measurements (Color Test)  
The spectrophotometer CM-700d (see Figure 3a,b) was used to measure the soil's color (soil spectral behavior). The color of the sandy loam soil was determined by the spectrophotometer at each moisture content mentioned in Section 2.1 (1.05%, 3.34%, 4.82%, 6.05%, and 7.63%) before the Bevameter test.  
The spectrophotometer sends electromagnetic waves in the visible range (400-700 nm; light visible band), and the color of the soil was determined based on the reflected wavelength (spectral reflectance %). Three measurements were taken by the spectrophotometer for the soil at each moisture content, and the average of these was considered as the result.
# Explainable Security Requirements Classification Through Transformer Models
## 3 The Method
### Explainability  
As AI systems reach a higher degree of sophistication, concern has been raised about how their internal processes work. To address this, the idea of explainable AI is gaining importance. AI explainability refers to the methods and techniques used to make the operations and outputs of AI systems understandable to humans. It aims to explain the inferences made by AI models; the advancement enhances trust, accountability, and transparency. Large Language Models, on the other hand, are trained on immensely scaled datasets composed of complex neural architectures and have the ability to comprehend and produce human languages efficiently. Explainability, however, is challenged by the complexity of their architecture and the volume of data they work with. There are several important considerations as to why it is vital to comprehend how transformers operate. The first point pertains to the user's trust in the models and their outputs. The second point suggests that the model can assist in detecting and correcting the imbalance in the training data, thus preventing any biases.  
In this work, we used attention visualization, which is a method to examine the behavior of attention mechanisms located in neural networks, especially in models such as Transformers and LLMs. Attention mechanisms allow models to weigh the importance of different input elements when making predictions or generating outputs. The attention enables models to focus on some words or tokens within the input sequence being processed to generate the response or make the prediction. These mechanisms can be described graphically and are valuable for understanding the various operations within the system by detailing the parts or areas of the input that are acceptable for a given task. The most common illustrations employed when demonstrating attention focus include heat maps, which depict the level of attention focused on individual input tokens. For example, certain words may be highlighted in a sentence to indicate that they received more attention from the model when predicting the next word or generating a response.
## 2 Materials and Methods
### System Features and Selected Use Cases
#### 2.3.2 Testing and Validation of a New Design  
The design prototype can be tested through a series of simulations involving static (parked car) and dynamic (simplified driving) scenarios, which are also part of core component 2. The user is tasked to interact with the cockpit in different ways and contexts to assess various performance, security, and comfort metrics through the different scenarios (needed by core components 5 and 6). In this phase, the IVI software is run either on an emulator or on the real hardware (via core component 3). The integration of real-world hardware components with the VR environment is a critical aspect of the V-Cockpit project, as this integration allows designers to test and validate the functionality of physical controls within the virtual prototype. Sensors and physiological monitoring devices are incorporated to capture user feedback, providing valuable data on user comfort and experience. Such data are at the core of the analysis and feedback provided through the following step.
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
###### Abstract
Second, since the KNPA traffic accident data does not include information on the destination or destination point, we extract this information from the _\"Report\"_ field, which records the accident details. Since the pattern of writing a report is generally fixed, we use Named Entity Recognition (NER) to extract the destination. Third, we convert the destination to latitude and longitude coordinates through geocoding. Finally, we estimate the directionality (Northhbound or Southbound) of the vehicle involved in the traffic accident by calculating the angle between the accident location and the destination. In the experimental results, we collected 5181 data items reported to the police in the Chungcheongnam-do region, which had a high incidence of traffic accidents, and estimated the directionality for all incidents. We also visualized the direction-specific occurrence of traffic accidents in each Administrative District (AD) using a cumulative histogram on a map. Analyzing the histograms by AD, it was found that in Chungcheongnam-do, accidents occurred more frequently in the Southbound direction. The remainder of this paper is organized as follows. Section 2 explains the analysis of the KNPA's traffic accident dataset. Section 3 describes the proposed method and each procedure in detail. The experimental results are described in Section 4, and the discussion and conclusions follow.
## 4 Results and Discussion
### Muting of All Vehicles Except Target
#### 4.1.1 Muting of All Vehicles Except Target Line  
As a logical continuation of these findings, it can be assumed that if gradually more information is added, this should incrementally improve the model performance. To test this, all vehicles from other lines were muted to include only those running under the same line name and direction. This includes vehicles which will run on earlier schedules than the target vehicle, but also those that follow the target vehicle on later journeys. The results are shown in Figure 10. This modification was compared to the results of the full network-based model and is shown in Section 4.1.3. For both directions, ETA prediction performance was reduced if the model was limited to a single line. However, both directions showed that the muted-line-based model outperformed the full network model. Interestingly, the muted model reached its best performance very quickly, whereas the full model converged slower. These results will be put into context in Section 4.1.3.
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Determination of Parameter Values in the Model
#### 3.3.3 Determination of the Value of Parameter \"\(d\)\"  
There should be distance \"\(d\)\" between the front car and the following car. In order to ensure that the distance after stopping is not severely distressing, the value of \"\(d\)\" can be based on people's individual responses. Generally, the value of \"\(d\)\" is 2-5 m. The value is affected by the driver's experience, the conditions on the road and the psychological resilience the driver has that day. In this paper, it is defined that the value of \"\(d\)\" is 2 m for ascending roads, 3 m for flat roads and 5 m for downhill roads.
## 4 Results and Discussion
### Simulation Results
In addition, when the fin angle was reduced from 90\({}^{\circ}\) to 60\({}^{\circ}\), the pressure drop decreased as the fin angle became smaller for the same flow rate. However, when the fin angle further decreased from 60\({}^{\circ}\) to 30\({}^{\circ}\), the pressure drop increased as the fin angle became smaller. That is, the pressure drop underwent a process of becoming smaller and then larger as the fin angle decreased. In a word, there is a contradiction among the three objective functions: heat transfer, oil pressure drop, and oil outlet temperature. The effects of fin angle, oil flow rate, and water flow rate on the objective function are interrelated.
## 4 Micro-Vibration Suppression and Image Stabilization Principle
### The First-Order Vibration Suppression
Taking \(\alpha=0^{\circ}\) as an example, the disturbance suppression curve of a BVIL with the structural parameters listed in Table 1 can be extracted from the Lissajous ellipse of the vector \(TF_{l}+TF_{r}\), as shown in Figure 9. It is not difficult to see that disturbances above 5 Hz will be suppressed significantly, but there is a peak around 2 Hz. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline \(L\) & 0.8 & m \\ \(k\) & 15,800 & N/m \\ \(c\) & 280 & Ns/m \\ \(m_{pe}\) & 100 & kg \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the BVIL. Figure 9: Disturbance suppression curve of the BVIL.