esa-sceva/satcom-chunk-collection huggingface dataset:
# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 3 Results
### Comparison of Reconstruction and Segmentation Performance  
Example slices of predicted segmentations, overlaid on intermediate reconstruction outputs, are shown for all four teams alongside ground truth in Figure 7. For each reconstruction metric, and for each of the top 4 performing pipelines, weighted DSC was plotted against the reconstruction metric in Figure 8, with Pearson's correlation coefficients being calculated for each pair. The highest correlation coefficient in this study was between nRMSE and weighted DSC for the NYU-Knee AI submission, at 0.284, with all other correlation coefficients being substantially lower. This indicates that, at the absolute best, there was a weak correlation between segmentation and reconstruction metrics, and in most cases, there was a negligible or even slightly negative correlation between the two.  
Figure 6: Intermediate pipeline reconstruction outputs for each of the top 4 submissions in an example sagittal slice, as well as ground truth, with reconstruction metrics displayed for the volume including the visualized slice. For this volume, UglyBarnacle delivers the highest quality reconstruction, followed closely by NYU-Knee AI, recovering sharpness and many fine details lost to aliasing during 8\(\times\) Poisson undersampling. K-nirsh delivers an intermediate reconstruction that was poor by standard reconstruction metrics, but perceptually, made boundaries between tissues much more distinct and perhaps easier to segment. This is likely due to K-nirsh fine-tuning the reconstruction and segmentation networks in an end-to-end manner, unlike other top submissions.
# A Distributed Token Passing Protocol for Time Constrained Data Gathering in VANETs
## 3 Proposed Approach
### Proposed Protocol  
The proposed protocol consists of two main phases and an optional one, the latter executed only under specific conditions, as shown in Figure 2:  
1. _token processing_,
2. _neighbor discovery_,
3. _next Token Owner (TO) selection and token forwarding_.  
In the following, we characterize each phase in terms of exchanged messages and sequence diagram.
## 4 Experimental Setup  
Experimentation was performed with real signals coming from a PV generation plant located in central Spain, at a latitude of 39\({}^{\circ}\)36'N and a longitude of 02\({}^{\circ}\)05'W. Measurements were performed in a 100 kW installation that uses an Ingecon Sun 100 solar inverter. Herein, a set of polycrystalline silicon PV panels on the DC side of the PV inverter was located, which delivered a peak power of 125 kW. The PV panels are south facing and present a tilt angle \(\beta\) = 45\({}^{\circ}\). The global irradiance that reaches the PV panels was measured using a calibrated PV reference cell that poses the same orientation and tilt angle as the PV panels. The data from both sides of the PV inverter were acquired and collected using a proprietary FPGA-based (Field Programmable Gate Array) data acquisition system (DAS). The DAS can acquire data from seven simultaneous channels at 8000 samples per second (SPS) with a 16-bit resolution. The DAS on the DC side was designed for measuring voltages up to 1000 V and for working with any current clamp that delivers a \(\pm\)4 V output. On this side, only two channels are required: one for the voltage and another one for the current. The clamp used for the current measurement is the HOP 500-SB/SP1 by LEM. On the AC side, six channels of the DAS are required to measure the voltage and current of the three phases. Since the PV inverter operates in a low voltage grid, the DAS on this side was designed for measuring voltages up to 400 Vrms, and for using any current clamp with a \(\pm\)2 V output. The current was taken from a measurement transformer, so SCT-013-010 sensors by YHDC were used with the DAS. All the voltage signals were acquired using wires directly connected from the PV inverter to the DAS. The voltage and current waveforms were stored during an extended time using a standard 128 GB micro SD card.
## 2 A Hypothetical Example Application of the V-GSI Chart in a Tunnel
### Comparison of Measured Convergence and Modelled Displacement  
As tunnelling progressed, prisms were installed for convergence monitoring at specified intervals and at locations within the tunnel sidewalls and crown. Typical installation locations included one prism on each sidewall, and three prisms in the crown. Prisms were only installed once the support was installed for safety reasons. Of interest to our selected example are the three survey stations located near the tunnel crown. Convergence was tracked by assuming zero displacement at installation and relative movement over time at these stations.  
A query line was generated within the 3-D model, within the section plane shown in Figures 7-9 and across the crown of the excavation perimeter. The vertical displacement across this query line was graphed initially assuming no ground support, and then with ground support as described above (Figure 10).  
The approximate locations of the three survey stations located in the tunnel crown are also presented in Figure 10. As illustrated in Figure 10, the maximum downward vertical displacement at the station near the crown centerline is around 2.2 mm when no support is installed, and 2.1 mm when the support is installed. Left and right of the centerline, downward vertical displacements are approximately 1.8 mm and 2.1 mm, respectively, with support installed.  
Figure 10: Vertical displacement for a query line along the tunnel crown (top right) corresponding to the model described in Section 3.5 for three scenarios (no support installed, with support installed, and with support installed but where the stress field is defined by \(\sigma_{v}=\sigma_{H}=\sigma_{h}\)). Graph generated using data from software RS3 by Roccience Version 4.024, © 2022 Roccience Inc.
# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 3 Deep Learning Based MQA Approaches
### Hybrid Deep Neural Networks Based Models  
Different neural network models can be combined to make a very effective model. In this section, we discussed various hybrid deep models used by many researchers to improve the MQA task.  
A hybrid method was proposed by [PERSON], et al., where they addressed the problem of complex relationship between questions and answers, with the aim of enhancing the Chinese medical question-answer selection [65]. They combined single and hybrid models with CNN and GRU to benefit from the merits of different neural network architectures.  
Another hybrid method named Template-Representation-Based Convolutional Recurrent Neural Network (T-CRNN) was proposed by [PERSON], et al., to select an answer in the Complex Question Answering (CQA) framework. Firstly, they replaced the entity from the input question with the templates. A divide and conquer approach was employed to decompose the question, based on the replaced entities. Then the CNN, RNN and scoring is used to determine the correct answer [72].  
Similarly, [PERSON], et al. [73] proposed two types of question generation approach. The first one is a retrieval-based method that uses CNN; the second one is a generation-based method that uses RNN. In addition, they also show how the generated questions can be used to improve existing question answering systems.  
In [66], a CNN-LSTM attention model is proposed to predict user intent, and an unsupervised clustering method is applied to mine user intent taxonomy (see Figure 14). The CNN-LSTM attention model has a CNN encoder and a Bi-LSTM attention encoder. The two encoders can capture both global semantic expression and local phrase-level information from an original medical text query, which helps the intent prediction. Their model is described as follows.  
For CNN-Encoder, they implemented a convolutional layer with one dimensional convolution operation. After this, they applied a max-over-time pooling operation with the feature maps built by CNN filters.  
For a feature map \(c=[c_{1},c_{2},\dots,c_{n}],c\in\mathbb{R}\), \(\hat{c}=\max\{c_{i}\}\) is the maximum feature, which is selected to represent the intensity of this particular filter in a query.  
For Bi-LSTM Attention Encoder, an LSTM model process a vector sequence input \(X=[x_{1},x_{2},\dots,x_{n}]\) from beginning to end and calculates a hidden state for each time step as  
\[h_{i}=LSTM(h_{i-1},x_{i}) \tag{22}\]  
A Bi-LSTM model has two LSTMs (LSTM, LSTM) reading the same input sequence with different directions. They concatenate two hidden state \(\left[\overrightarrow{h_{i}},\overleftarrow{h_{i}}\right]\) as the final hidden state output \(h_{i}\) of input \(x_{i}\).  
Moreover, they applied the attention mechanism to extract important words to the intent of the query \(q\) to a sequence of hidden state \(H=[h_{1},h_{2},\dots,h_{n}]\). In this way, a query could be encoded into a vector \(v\), and each attention scalar \(\alpha_{i}\) can demonstrate the attention degree for the \(i\)-th word in query \(q\).
# Study on Noise Model of an Automotive Axial Fan Based on Aerodynamic Load Force
## 1 Introduction
The influencing factors of low-pressure axial flow fan noise are mainly divided into three aspects: thickness noise, constant-force noise, and non-constant-force noise; The aerodynamic force on the blade surface is highly unsteady and the uneven inlet interacts with the impeller producing a strong unsteady pulsation on the blade surface, thereby radiating noise, and the non-constant-force noise is the main discrete noise source of a low-pressure axial flow fan [14]. The experimental study in [15] shows that the influence of the Reynolds number (Re for short) on acoustic radiation is implicit in the unsteady force and that discrete sound plays a dominant role in the non-self-mode region with a low Re number, whereas the automotive axial flow fan is in the low Re number non-self-mode region. Due to the complexity of the load force on the fan blade, none of the above studies have obtained an accurate load force. In this paper, a new method based on previous studies is proposed and successfully applied to the axial flow fan of a vehicle. First, the relationships between the lift, resistance characteristics, and the angle of attack are established according to the BEM theory and the characteristics of the axial flow fan blades of the vehicle, and the constant force is obtained. Then, the relationship between unsteady lift and velocity using the unsteady thin-wing theory is established and the non-constant force is obtained. The constant and non-constant forces of each microelement are obtained using vector superposition to obtain the total load force. This method provides an important basis for the study of a high-precision noise mathematical model of a low Mach-number fan.
## 3 Objective Function to Tune the PSS Parameters  
To optimise the PSS parameters, the angle stability of the system was calculated by eigenvalue analysis, whilst a single objective function was considered based on the minimisation of the real part of eigenvalues. The eigenvalue having a bigger real value ensures better system stability. The eigenvalues can be taken from matrix (A) of the system of the linearised state space mathematical model with the command in MATLAB.  
\[\lambda_{k}=\text{eig}(\text{A}), \tag{48}\]  
where \(k=1,2,3\dots n;n\) shows the number of state variables in the given system and number of eigenvalues; eig() refers to the built-in function of MATLAB. An objective function is formulated to maximise the damping ratio to shift the eigenvalues towards a real axis to minimise the amplitude of oscillations, as shown in Figure 3:  
\[\text{Maximisation f}=\text{min }\left(\xi_{ij}\right), \tag{49}\]  
\[\xi_{i}=\frac{\sigma_{i}}{\sqrt{\sigma_{i}^{2}+\omega_{i}^{2}}},\]  
where \(\sigma_{i}\) is the real part, and \(\omega_{i}\) is the imaginary part of the system eigenvalues.  
Figure 3: Eigenvalue’s locations on the s-plane.  
The \(T_{1}\), \(T_{2}\) and \(K_{stb}\) parameters of the lead lag controller are required when optimising a subject to the following conditions.  
\(T_{1}\), \(\min\leq\ T_{1}\leq T_{1}\), \(\max\),  
\(T_{2}\), \(\min\leq\ T_{2}\leq T_{2}\), \(\max\),  
\(K_{stb}\), \(\min\leq\ K_{stb}\leq K_{stb}\), \(\max\).
## 5 Optimization and Scheduling Model of the Complementary System
### Inequality Constraints  
1. System power constraints: \[\begin{array}{l}\frac{P_{g}(i)\leq P_{g}(i,t)\leq\overline{P_{g}}(i)}{ \overline{P_{p}}(i)\leq P_{p}(i,t)\leq\overline{P_{p}}(i)}\\ \frac{P_{g}}{\leq P_{s}(t)\leq\overline{P_{s}}}\\ \frac{P_{w}}{\leq P_{w}(t)\leq\overline{P_{w}}}\end{array}\] (28) where \(\overline{P_{g}}(i)\), \(\underline{P_{g}}(i)\) are the upper and lower limits of generating power of the hydropower station, kW; \(\overline{P_{p}}(i)\), \(P_{p}(i)\) are the upper and lower limits of pumping power,kW; \(\overline{P_{s}}\), \(\underline{P_{s}}\) are the upper and lower limits of PV power, kW; \(\overline{P_{w}}\), \(\underline{P_{w}}\) are the upper and lower limits of wind power units, kW.
2. System power deviation constraint: \[\left|P_{s}(t)+P_{w}(t)+\sum_{i=1}^{I}(P_{g}(i,t)-P_{p}(i,t))-P_{l}(t)\right| \leq\beta P_{l}(t)\] (29) where \(\beta\) denotes the power deviation coefficient, whose value is usually set according to real-world operational requirements. This constraint indicates that the system is subject to energy transmission limitations.
3. Water flow constraints: \[\begin{array}{l}\underline{q_{g}}(i)\leq q_{g}(i,t)\leq\overline{q_{g}}(i) \\ \underline{q_{p}}(i)\leq q_{p}(i,t)\leq\overline{q_{p}}(i)\end{array}\] (30) where \(\overline{q_{g}}(i)\), \(q_{g}(i)\) are the upper and lower limits of the power generation flow, m\({}^{3}\)/s; \(\overline{q_{p}}(i)\), \(\underline{q_{p}}(i)\) are the upper and lower limits of the pumping water flow, m\({}^{3}\)/s.
4. Adjustable capacity constraints: \[\begin{array}{l}v_{c}(i,t)=v(i,t)-v_{d}(i,t)\\ \underline{v_{c}}(i,t)\leq v_{c}(i,t)\leq\overline{v_{c}}(i,t)\end{array}\] (31) where \(v_{c}(i,t)\) is the adjustable capacity of the \(i\)-th hydropower station, \(10^{4}\) m\({}^{3}\); \(v_{d}(i,t)\) is the minimum allowable storage capacity of the \(i\)-th hydropower station, \(10^{4}\) m\({}^{3}\); \(\overline{q_{p}}(i)\), \(\underline{q_{p}}(i)\) are the upper and lower limits of the adjustable capacity, \(10^{4}\) m\({}^{3}\).
# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 4 VM Placement Optimization Method
### Chaos-Elite NSGA-III
#### 4.1.2 Population Initialization Based on Chaotic Mapping  
Chaotic mapping is a dynamic system that is highly sensitive to initial conditions. Even slight changes in these conditions can result in significant differences in system behavior. This characteristic is beneficial for genetic algorithms as it enhances the diversity of individuals and ensures a broad coverage of the search space, thereby improving the algorithm's search efficiency and global search capability. It also aids in avoiding premature convergence to local optima and promotes the exploration of a wider solution space. In the traditional NSGA-III [23], the initial population is randomly generated, which, when handling large-scale tasks, is not conducive to determining the true optimal solution such as VM placement. Therefore, chaotic mapping is employed in NSGA-III to initialize the population. The process of chaos-mapping-based population initialization is as follows:  
Step 1 st: Generating a chaotic sequence  
A chaotic sequence is generated using Equation (16). \[x_{n+1}=\mu x_{n}(1-x_{n}) \tag{16}\]  
where \(\mu\) determines the dynamic behavior of the system, including the chaotic region. \(x_{n}\) is the current state and \(x_{n+1}\) is the next state. Figure 3: The example of a VM placement scheme. Figure 2: Flowchart of CE-NSGAIII. Step 2 nd: Mapping chaotic values to VM assignments  
The mapping of the loaded tasks to VMs is one of the components of individual. Each task index is assigned a chaotic value from the sequence. This chaotic value is mapped to a bounded integer within a specified range defined by \(lowerbound\) and \(upperbound\), which represents the range for possible VM allocations for each task. The mapped chaotic value determines the VM index for each task in \(individual.setVariables(j,val)\), ensuring diverse task-to-VM allocations. Step 3 rd: Mapping chaotic values to PM assignments  
The mapping of VMs to PMs is another component of the individual. In this process, each VM index is assigned a chaotic value from the sequence. This chaotic value is mapped to a bounded integer within the specified range defined by \(Plowerbound\) and \(Pupperbound\), which represents the range of possible PM assignments. These mapped chaotic values are stored in \(individual.setVariablesP(k,Pval)\), which represents the PM index where each VM resides. Step 4 th: Adding individuals to the population  
After each individual is initialized, the chaosIndex is updated to keep track of the position in the chaotic sequence, ensuring that each individual receives a unique sequence of chaotic values. Instances of individuals that have completed their initialization are then added to the population. Finally, the population initialization is completed once all individuals are mapped. The logistic mapping-based population initialization algorithm (LMPI) is described in the pseudocode of Algorithm 1.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 2 Related Work
### Performance Analysis of the Sign Language Translation System  
This section discussed the effectiveness of the different sign language translation systems based on different evaluation metrics such as Sign Error Rate (SER), Bilingual Evaluation Understudy (BLEU), and the National Institute of Standards and Technology (NIST). In [27], the authors have designed an avatar-based model that can generate sign movements from spoken language sentences. They achieved a 15.26 BLEU score with a Recurrent Neural Network (RNN)-based model. In [28; 29], the authors have proposed a \"HamNoSys\" system that converts the input words to corresponding gestures of ISL. \"HamNoSys\" represents the syntactic representation of each sign using some symbols, which can be converted into the respective gestures (hand movement, palm orientation). Apart from \"HamNoSys\", Signing Gesture Markup Language (SiGML) [30] also has been used for transforming sign visual representations into a symbolic design. In [31], the authors have used BLEU and the NIST score, which are relevant for performance analysis of language translation. Speech to SSL translation has been implemented with two types of natural language-based translations (rule-based and statistical) [12]. The authors have identified that rule-based translation outperforms statistical translation with a 31.6 SER score and a 0.5780 BLEU score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Sign Language** &
\begin{tabular}{c} **Input:** \\ **Speech** \\ \end{tabular} & **Input: Text** & **3D Avatar** & **Sentence-Wise Sign** \\ \hline [PERSON], [PERSON] et al. [11] & ArSL & ✗ & ✓ & ✓ & ✗ \\ [PERSON] et al. [22] & CSL & ✓ & ✗ & ✗ & ✓ \\ [PERSON] et al. [14] & ArSL & ✓ & ✗ & ✗ & ✗ \\ [PERSON] et al. [23] & SSL & ✗ & ✓ & ✓ & ✗ \\ [PERSON] et al. [24] & ASL & ✗ & ✗ & ✓ & ✗ \\ [PERSON] et al. [8] & ISL & ✗ & ✓ & ✗ & ✗ \\ [PERSON] et al. [19] & ISL & ✗ & ✓ & ✗ & ✓ \\ [PERSON] et al. [20] & ISL & ✗ & ✓ & ✗ & ✗ \\ [PERSON] et al. [6] & ISL & ✓ & ✓ & ✗ & ✗ \\ [PERSON] et al. [25] & ASL & ✓ & ✗ & ✗ & ✓ \\ [PERSON] et al. [26] & ISL & ✓ & ✗ & ✓ & ✓ \\
**Proposed** & ISL & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brief description of previous studies of sign language learning systems. Note: Arabic Sign Language (ArSL), Chinese Sign Language (CSL), Spanish Sign Language (SSL), American Sign Language (ASL), and Indian Sign Language (ISL). “Sentence-wise sign” represents the continuous signs corresponding to a sentence in its correspondent spoken language.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Sample Value Shifting Method  
Each of [PERSON]'s three samples is adjusted using the sample value shifting method. Specifically, 128 is added to each sample (since \(2^{8-1}=128\)), ensuring that the sample values fall within the range of 0 to 255. After the adjustment, [PERSON]'s three samples become 118, 134, and 158.
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
## 3 Mathematical Model Description
### Gas Composition and PVT Model of Reservoir Fluid  
The composition of reservoir gas utilised in model is shown in Table 4. Black Oil tables was used for generating the thermodynamic properties of the natural gas in the model, following the method of [PERSON] et al. [47], which described the properties of reservoir fluids under various conditions of pressure and temperature. The main PVT (pressure-volume-temperature) properties of fluids tabulated for different ranges of pressure and temperature of 100 \({}^{\circ}\)C were obtained using the Peng-Robinson equation of state [47; 48]. The calculated data are listed in Table 5 for the gas composition given in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Gas Component** & **Mol (\%)** \\ \hline N\({}_{2}\) & 0.400 \\ \hline CO\({}_{2}\) & 1.570 \\ \hline C1 & 94.604 \\ \hline C2 & 1.470 \\ \hline C3 & 0.990 \\ \hline iC4 & 0.280 \\ \hline nC4 & 0.351 \\ \hline iC5 & 0.122 \\ \hline nC5 & 0.132 \\ \hline C6 & 0.005 \\ \hline C7 & 0.010 \\ \hline C8 & 0.014 \\ \hline C9 & 0.010 \\ \hline C11 & 0.042 \\ \hline \end{tabular}
\end{table}
Table 4: Gas composition of reservoir fluid [44].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Pressure (bar)** & **Bg (m\({}^{3}\)/m\({}^{3}\))** & **\(\mu\)g (cP)** & **Pressure (bar)** & **Bg (m\({}^{3}\)/m\({}^{3}\))** & **\(\mu\)g (cP)** \\ \hline
1.00 & 1.32568 & 0.01357 & 181.52 & 0.00671 & 0.01794 \\
13.03 & 0.10046 & 0.01367 & 193.55 & 0.00631 & 0.01845 \\
25.07 & 0.05163 & 0.01379 & 205.59 & 0.00596 & 0.01898 \\
37.10 & 0.03451 & 0.01394 & 217.62 & 0.00565 & 0.01952 \\
49.14 & 0.02581 & 0.01411 & 229.66 & 0.00538 & 0.02007 \\
61.17 & 0.02055 & 0.01432 & 241.69 & 0.00513 & 0.02064 \\
73.21 & 0.01704 & 0.01455 & 253.72 & 0.00492 & 0.02121 \\
85.24 & 0.01453 & 0.01481 & 265.76 & 0.00472 & 0.02180 \\
97.28 & 0.01266 & 0.01510 & 277.79 & 0.00455 & 0.02238 \\
109.31 & 0.01122 & 0.01542 & 289.83 & 0.00439 & 0.02297 \\
121.35 & 0.01007 & 0.01577 & 301.86 & 0.00424 & 0.02356 \\
133.38 & 0.00914 & 0.01615 & 313.90 & 0.00411 & 0.02415 \\
145.41 & 0.00837 & 0.01656 & 325.93 & 0.00399 & 0.02474 \\
157.45 & 0.00772 & 0.01700 & 337.97 & 0.00388 & 0.02533 \\
169.48 & 0.00718 & 0.01746 & 350.00 & 0.00378 & 0.02592 \\ \hline \end{tabular}
\end{table}
Table 5: PVT properties of reservoir fluid for constant temperature (100 \({}^{\circ}\)C) [44].