esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework
### High-Frequency Components Fusion
#### 3.2.3 Sparse Coding and Coefficients Fusion  
When the dictionary \(\mathbf{D}\) is trained, OMP algorithm is implemented for sparse coding. Supposing input images are split to \(k\) image blocks, each image block \(\mathbf{b}_{i},i\in(1,2...k)\) is coded by OMP algorithm with the input dictionary \(\mathbf{D}\). Each image block \(\mathbf{b}_{i},i\in(1,2...k)\) is coded to sparse coefficients \(\mathbf{z}_{i},i\in(1,2...k)\). Then the coefficients is fused by \"Max-L1\" fusion rule. The fusion rule is demonstrated in Equation (4).  
\[\mathbf{z}^{f}=\sum_{i=1}^{k}\mathbf{z}_{k}\otimes O^{k},\textit{ where}\left\{\begin{array}{c}O^{k}=1,max(\|\mathbf{z}_{1}\|_{1},\|\mathbf{z}_{2}\|_{1}...\|\mathbf{z}_{k}\|_{1})=\|\mathbf{z}_{i}\|_{1}\\ O^{k}=0,\textit{otherwise}\end{array}\right. \tag{4}\]  
where the \(\mathbf{z}_{k}\) is the sparse coefficients corresponding to each image patches. \(\mathbf{z}^{f}\) is the fused vector, \(\|\bullet\|_{1}\) is the \(L_{1}\) norm. \"Max-L1\" is a fusion algorithm of sparse coefficient that has been widely used in many references [43; 65]. This algorithm does the sparse representation of the source image first, then picks the sparse coefficients with large L1 norm as the transformation coefficients, and finally inverts the transformation coefficients to obtain the fused image.  
The fused coefficient vectors are restored back to image by Equation (5).  
\[\mathbf{I}_{h}^{f}=\mathbf{D}\mathbf{Z}^{f} \tag{5}\]  
where the \(\mathbf{Z}^{f}=\{\mathbf{z}_{1}^{f},\mathbf{z}_{2}^{f}...,\mathbf{z}_{l}^{f}\}\), are fused vectors corresponding to the source image patches and \(\mathbf{D}\) is the dictionary trained by the proposed method.
## 2 Finite Element Model
### Track and Ballast Model  
In this simulation, the ground model was constructed using rigid shell elements to prevent deformation, as shown in Figure 3, and the characteristics of the track and ballast were applied as slip rate-friction functions.  
Generally, in surface-to-surface friction, the frictional force depends on the perpendicular force applied to the contact surface and roughness, and it acts opposite to the object's motion along the contact surface.  
\[F_{f}=\mu mg=\mu N \tag{1}\]  
\(F_{f}\): Friction force  
\(\mu\): Coefficient of friction  
Figure 3: Track and ballast surface finite element analyses (FEA) model.  
Figure 2: FEA train model (320 elements/1 car).  
_m_: Mass  
\(g\): Acceleration due to gravity  
\(N\): Vertical normal force  
In our analysis, the friction coefficient of the track surface was set to 0.6 in Table 3, considering Korea's annual average temperature of 12.9 \({}^{\circ}\)C in 2022 [21].  
Typically, the post-derailment braking behaviour of railways is affected by various factors such as the dissipation of kinetic energy when the wheels enter the ballast layer and become buried, structural friction considering elements such as sleepers and rails, residual wheel slippage until complete application of the braking force, and adhesion of the wheels to the ballast surface during complete deceleration when the ballast surface undergoes wear. Therefore, the Coulomb friction model, used under surface-to-surface contact conditions, is inappropriate for the ballast layer. Thus, this study used the Stribeck curve to simulate the relationship between the relative velocity of the wheel and ballast surface and the friction coefficient [23; 24]. The slip rate in the Stribeck curve refers to a physical quantity representing the velocity of relative motion between two contact surfaces. Thus, this study considered the relative velocity as a relevant physical quantity.  
\[Slip\;rate=\left\|V_{rel}\right\|=\left\|V_{ground}-V_{train}\right\| \tag{2}\]  
\(V_{rel}\): Relative motion vector  
\(V_{ground}\): Ground motion vector (velocity)  
\(V_{train}\): Train motion vector (velocity)  
In Figure 4, boundary friction refers to the state of complete stopping; the wheel-ballast mixed conflict refers to the state wherein the torque of the wheel decreases, the wheel becomes stuck in the ballast, or the gravel surface wears down and becomes adhesive. In particular, wheel-ballast dynamic friction refers to the state wherein the gravel surface does not affect the system, and the wheel slips while driving at high speeds after derailment. Therefore, \(\mu_{R}\) was set to be the same as that of the track surface and assumed to vary from one to four. The ballast conditions were evaluated, as shown in Figure 5.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 4 Technological Trends, New ICT Systems, and Industrial Internet of Things (IIoT) Solutions
### Namur Open Architecture  
NAMUR Open Architecture (NOA) [15] is a framework that will simplify the introduction of principles and solutions related to Industry 4.0, digitalization and industrial IoT in the process industry. NOA describes how information exchange between process control systems and the new area \"monitoring and optimization (M+O)\" can be performed with open interfaces based on data diodes to ensure adequate information security.  
NOA is intended for existing brownfield processing plants where, in terms of cost and complexity, it is unrealistic to change the basic principles of the automation pyramid and the Purdue model's layering. NOA shall thus safeguard the integrity of process control systems (OT) while making data and information from controllers and I/O devices available for further analysis and processing in the IT domain (M+O).  
In this context, a concept called \"NOA diodes\" will provide one-way communication with adequate mechanisms for information security. NOA also defines a solution for communicating, e.g., new set points for process control from M+O back to the OT systems, but details of how this will be implemented as of November 2021 are not fully specified.  
An important limitation of NOA is that communication to and from safety instrumented systems (SIS) is not part of the standard. It is explicitly stated that the concepts in NOA should not be used for this purpose. Awareness in the petroleum industry around this will be important in order to ensure independence by reducing the risk that NOA will, in the future, be used to extract information from systems that safeguard functional safety.
## 3 Data and Methods  
Two-dimensional multichannel seismic data were accessible, which were collected by Shell New Zealand Limited in 2014. The seismic data were acquired using an array of tuned air guns with a total volume of 4320 cubic inches, and a 7950 m long, 636-channel streamer with hydrophones spacing of 12.5 m. The seismic data, processed by post-stack time migration, have a sampling interval of 4 ms and a line spacing of 2 km \(\times\) 4 km, and cover an area of approximately 6400 km\({}^{2}\) (Figure 1B). The dominant frequency for the interested channel-developed interval is about 35 Hz, resulting in a vertical resolution of \(\sim\)13 m. This is based on an average interval velocity of \(\sim\)1850 m/s as determined from sonic logging data of nearby petroleum exploration wells of Galleon-1 and Caravel-1 (Figure 1B) [31; 32].  
For our seismic data analysis, we adopted the classical seismic stratigraphic method [33; 34]. First, seismic sequence boundaries were defined by examining systematic seismic reflection terminations such as onlap, downlap, and truncation, to establish the seismic sequence stratigraphic framework [34]. The ages of the seismic sequence boundaries were established by correlating with the dated regional stratigraphic classification (Figure 2) [17; 18; 35; 36]. Isochron maps of each seismic sequence were used to analyze the plan-view distribution characteristics of each sequence stratum. Second, seismic facies were identified based on seismic reflection parameters, such as amplitude, continuity, configuration, and external forms [33]. Since there was no calibration available from drilling data, the geological interpretation of the seismic facies was achieved through literature examples of similar seismic facies and constraints from the regional geological setting. Third, the plan-view distribution characteristics of the channels and related depositional elements on each seismic sequence were mapped by analyzing seismic facies profile by profile. Finally, we integrated seismic sequence and seismic facies interpretations to infer the sedimentary evolution processes and controlling factors of the tributary channels system.
## 4 Experiments and Discussion
### Comparison in Testing Timing  
As shown in Table 3, five crack detectors were used to process the remaining 100 concrete images, and the average processing timings were calculated. From the comparisons, the crack detecting method by global analysis was generally more efficient than the local analysis-based one. Specifically, for the Canny-based method [4], the built-in edge function of MATLAB was exploited for processing the input concrete images, and the threshold parameter setting was based on the receiver operating characteristic analysis and Bayesian decision theory. As for the Otsu-based method [8], the input image was preprocessed with the Prewitt operator. Then, the built-in function gray-thresh of MATLAB was applied for segmenting the cracks, and post-morphological processing was further utilized for removing background noise. In comparison, the Otsu-based method had no iterative steps and, thus, it was faster than the Canny-based one. For the local analysis based methods, the average timing of the SVM-based one [11] was less than that of the DL-based one [26]. A possible reason is that the SVM model is a shallow network, and it needed fewer calculations than the DL's multilayer network. However, for these two methods, the divided image patches were processed one by one, which resulted in their slow testing performance. In this work, all of the divided image patches could be squeezed into one image matrix, which was then calculated in parallel. Therefore, the final testing timing of proposed MECD method was less than that of other ones.
# A Text Abstraction Summary Model Based on BERT Word Embedding and Reinforcement Learning
## 5 Result and Analysis
### Result  
In the extraction experimental group, only sentences are selected without rewriting, in other words, the abstraction fitting function is f2 (s\({}^{\prime}\)) = s\({}^{\prime}\), and the rest are the same as the full experiment. As shown in Table 2, the experimental results of m1, m2 have many advantages in the ROUGE-1 standard compare with extractive baselines [3; 6; 22; 30], and are slightly higher than the method in the literature [21]. In the ROUGE-2 standard, our method has an obvious effect. In the ROUGE-L standard, our model is superior to the models Refresh [3] and Lead-3 [6], but lower than the model NeuSUM [30].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R-1** & **R-2** & **R-L** & **R-AVG** \\ \hline Lead-3 [6] & 40.34 & 17.70 & 36.57 & 31.54 \\ Refresh [3] & 40.00 & 18.20 & 36.60 & 31.60 \\ SummaRuNNer [22] & 39.9 & 16.3 & 35.1 & 30.43 \\ HSSAS [21] & 42.3 & 17.8 & 37.6 & 32.57 \\ NeuSUM [30] & 41.59 & 19.01 & **37.98** & 32.86 \\ (m1) BEAR (ext+base) & 42.43 & **20.36** & 37.23 & 33.34 \\ (m2) BEAR (ext +large) & **42.54** & 20.35 & 37.24 & **33.38** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of models with respect to the extractive baselines on CNN/Daily Mail.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R-1** & **R-2** & **R-L** & **R-AVG** \\ \hline Pointer Generator + Coverage [6] & 39.53 & 17.28 & 36.38 & 31.06 \\ Inconsistency loss [10] & 40.68 & 17.97 & 37.13 & 31.93 \\ DCA [11] & 41.69 & 19.47 & 37.92 & 33.11 \\ Rnn-ext + abs + RL + rerank [9] & 40.88 & 17.80 & 38.54 & 32.41 \\ (m3) BEAR (ext + abs + base) & 39.84 & 18.83 & 37.35 & 32.01 \\ (m4) BEAR (ext + abs + RL + base) & 40.91 & 19.88 & 38.45 & 33.08 \\ (m5) BEAR (base + nature) & 40.95 & 17.89 & 38.55 & 32.80 \\ (m6) BEAR (base + WordPiece) & 41.76 & 20.20 & 39.39 & 33.78 \\ (m7) BEAR (large + WordPiece) & **41.95** & **20.26** & **39.49** & **33.9** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of models with respect to the abstractive baselines on CNN/Daily Mail.  
In the complete experimental group, the experimental results of our model are significantly improved compared with the earlier work and baseline model. As we all know, we obtain the highest ROUGE score for the current abstraction summary in the CNN/Daily Mail dataset. As shown in Table 3, compared with the baseline model [9], the scores of ROUGE-1, ROUGE-2, ROUGE-L, and R-AVG are respectively improved 1.07%, 2.46%, 0.95%, and 1.46%.Compared with the highest scoring model [11] in 2018, the score of Rouge-1 is only slightly improved, but these two metrics of Rouge-2 and Rouge-L are significantly improved, and these two models we have fully trained are also significantly better than the abstraction methods [6; 10].  
We also compared our best model with the best extractive baseline model and the best abstractive baseline model. The comparison bar chart is shown in Figure 7.
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 1 Introduction
### Design Challenges  
Despite a substantial body of research dedicated to the layout optimisation of discrete structures, most existing works predominantly address only one or two types of mechanical constraints, and are confined to a limited set of structural elements. For example, [PERSON] and [PERSON] [39] focused solely on global stability; [PERSON] and [PERSON] [40], [PERSON] et al. [14], [PERSON] et al. [56], and [PERSON] et al. [15] addressed stress and global stability constraints; meanwhile [PERSON] [31] considered stress, and both local and global stability. Additionally, [PERSON] and [PERSON] [57] investigated optimisation with constraints on displacement, stress, local stability, and eigenfrequency. However, few studies have attempted to simultaneously integrate constraints on displacement, stress, and both local and global stability. An exception is the work by [PERSON] et al. [34], which demonstrated this approach for truss structures with a modest number of elements, leaving its application to large-scale structures unexplored. Large-scale structures, typically optimised using ground structures with refined grid divisions and dense member connectivity, present additional challenges in problem formulation, optimisation convergence, and computational efficiency compared to low-resolution structures.  
Furthermore, prevailing approaches for controlling geometric complexity are typically introduced alongside optimisation problems focused on displacement or structural compliance [51; 52; 54; 58]. This raises concerns about their effectiveness in handling multi-constrained problems, particularly when stress and local and global stability constraints are involved. These design-dependent constraints are highly sensitive to changes in design variables and present significant challenges in numerical modelling and convergence [18; 21; 37; 59], such as stress singularities and pseudo buckling modes, especially when the simplification of geometric complexity is coupled with a reduction in element stiffness.
## 3 Experimental Test Results
### Impulse Test Results
#### 3.2.3 Current Rise and Discharged Times  
For every voltage and current trace, the current rise and discharged times were measured, as shown in Figure 4. Figures 10 and 11 show the current rise and discharged  
Figure 8: Current traces of the test load for various resistance values at a charging voltage of 100 kV.  
Figure 9: Impulse impedance versus peak current for all ground electrodes.  
times, respectively, with increasing peak currents for all the ground electrodes. The current rise times can be defined as the time taken for the air voids in soil to break down and achieve fully conductivity. These times were found to be independent of the impulse polarity, ground electrodes and current magnitudes. The current discharge times represent the time taken to discharge the current to the ground. Faster current discharge to the ground occurred in ground electrodes (ii) GEM and (iii)-(iii)d (GEM_10L, GEM_20L, GEM_50L and GEM_100L) due to their lower \(R_{DC}\) or \(Z_{imp}\) values, indicating relatively better conduction than ground electrode (i) with higher \(R_{DC}\) and \(Z_{imp}\) values. The trend of better conduction at higher current magnitudes is also observable in ground electrode (i), where the current discharged times were found to decrease with increasing currents. However, we observed that the current discharged times of electrode (i) GEN were longer in positive polarity compared to negative polarity, despite the higher discharge activity observed in positive polarity. Hence, better conduction is expected to take place in positive compared to negative polarity. This could be due to statistical discharge behaviour in the soil, which could arise from non-uniformity in the soil properties.  
Figure 11: Current discharged times versus peak current for all ground electrodes.  
Figure 10: Current rise times versus peak current for all ground electrodes.
# Hydraulic Characterization of Green Roof Substrates by Evaporation Experiments
## 2 Materials and Methods  
### Substrate Characteristics  
Three green roof substrates were considered in this investigation, including two commercial substrates and a specifically developed growing substrate that showed good hydraulic characteristics for use in ornamental plant production [38]. The first commercial substrate is Terra Mediterranean(r) (TMT), manufactured by Harpo Verdepensile (Harpo spa, Trieste, Italy), consisting of a mixture of 80% mineral fraction (lapillus, pumice and zeolite) and 20% organic fraction (peat and compost). According to the technical specifications released by the manufacturer, the substrate dry bulk density, \(\rho_{b}\), is 850-1000 kg m\({}^{-3}\), the saturated hydraulic conductivity, \(K_{s}\), is larger than 1200 mm h\({}^{-1}\) and the field capacity (i.e., water content at 100 cm suction), \(\theta_{fc}\), is 0.30-0.45 m\({}^{3}\)m\({}^{-3}\).The second commercial substrate is AgriTERRAM(r) TV (ATV) manufactured by Perlite Italiana srl (Corsico, Milan, Italy). The mineral fraction (75-80%) includes lapillus, pumice and expanded perlite and the organic fraction (20-25%) includes peat, bark, coconut fiber and organic conditioners. Technical specifications certify that \(\rho_{b}\) = 400 kg m\({}^{-3}\), \(K_{s}\) > 780 mm h\({}^{-1}\) and \(\theta_{fc}\) > 0.50 m\({}^{3}\)m\({}^{-3}\). The third growing substrate (MIX) was prepared by mixing on a volume basis 25% peat, 25% compost and 50% mineral soil. Commercial 100% sphagnum peat moss (Vigorplant, Fombio, Lodi, Italy) and 5-month-aged compost from orange juice processing wastes and garden cleaning [39] were used as organic fractions. The mineral fraction was obtained using 2 mm sieved sandy loam soil (Typic Rhodoxeralf, clay = 15.9%, silt = 27.2%, sand = 56.9%, USDA).  
For each substrate, four replicated samples were prepared by compacting a given weight of material into plastic cylinders with a 9.3 cm inner diameter and 12 cm height. In order to avoid artefacts due to sample preparation, compaction was conducted in four successive steps by beating the substrates with five strokes from a height of 5 cm followed by five rotations with a pestle at each increment.
## 4 Computer Simulations
### 1. Simulation: Miss rate simulation  
In the first simulation miss rate (misidentification rate) of the proposed signal processing algorithm was tested at different shot noise levels. The _SNR_ was varied from 1 dB to 28 dB with 1 dB separation and a set of 10000 simulations was carried out at different _SNR_. When the position difference between the estimated zero order fringe peak \(\hat{p}_{d}\) and the computer-generated zero order fringe peak \(p_{d}\) is bigger than half fringe (8 samples), the zero order fringe peak is considered to have been misidentified. Table 2 shows the miss rate of the proposed signal processing algorithm and miss rate is defined as the ratio of number of miss to 10000.  
In Figure 9, miss rate was plotted along with Bit Error Rate (BER) of binary fiber optic communication system [19]. In Figure 9 (or Table 2) we have a rule of thumb that every dB improvement in _SNR_ (over the range of 26 \(\sim\) 31 dB) produces approximately one order of magnitude improvement in error rate. This kind of behavior is also the case for binary fiber optic communication system (two orders of magnitude improvement in error rate for the binary fiber optic communication system).  
To extrapolate the miss rate beyond the range of 10\({}^{-4}\) on the BER curve, data points in abscissa in Figure 9 were left-shifted by \(\sim\)14.2 dB (by trial and error) until data points of miss rate between 16 dB \(\sim\) 31 dB were visually fitted on the BER curve. Then four more data points were extrapolated beyond the data point of miss rate at 31 dB _SNR_ on the BER curve as shown in Figure 10. It is predicted that miss rate will be 3 x 10\({}^{-8}\) at _SNR_ of 35 dB.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline _SNR_ **[dB]** & **Miss rate** & _SNR_ **[dB]** & **Miss rate** \\ \hline
1 & 0.95 & 17 & 0.46 \\
2 & 0.94 & 18 & 0.40 \\
3 & 0.93 & 19 & 0.34 \\
4 & 0.91 & 20 & 0.30 \\
5 & 0.89 & 21 & 0.23 \\
6 & 0.87 & 22 & 0.19 \\
7 & 0.85 & 23 & 0.14 \\
8 & 0.83 & 24 & 0.10 \\
9 & 0.80 & 25 & 0.066 \\
10 & 0.76 & 26 & 0.041 \\
11 & 0.73 & 27 & 0.019 \\
12 & 0.69 & 28 & 0.009 \\
13 & 0.64 & 29 & 0.004 \\
14 & 0.60 & 30 & 0.001 \\
15 & 0.56 & 31 & 0.0003 \\
16 & 0.51 & 32 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Miss rate of the proposed signal processing algorithm as a function of _SNR_.  
Figure 9: Comparison between miss rate and BER.
## 3 Results
### RQ1: Which Electrophysiological Signals May Index Emotional Processing?
#### 3.1.4 Eog  
EOG is a technique used to record the electrical activity of eyeball movement. This method was reported in only one study. Extraction of the global velocity of eyeball movement was performed by computing the absolute values of the EOG derivatives [36]. In this study, increased eyeball movement was observed in patients with bipolar disorder type I after perceiving stimuli inducing emotions, such as happiness, disgust, and sadness, in comparison with bipolar disorder type II and control subjects. Stimuli associated with happiness and sadness increased eyeball movements when presented after a stimulus associated with disgust. Furthermore, disgust stimuli increased eyeball movements when presented after a neutral stimulus.