esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
After the Fukushima accident, the importance of an emergency power supply for a nuclear power plant has been emphasized more. In order to maximize the performance of the existing emergency power source in operating nuclear power plants, adding a waste heat recovery system for the emergency power source is suggested for the first time in this study. In order to explore the possibility of the idea, a comparison of six supercritical carbon dioxide (S-CO\({}_{2}\)) power cycle layouts recovering waste heat from a 7.2 MW alternate alternating current diesel generator (AAC DG) is first presented. The diesel engine can supply two heat sources to the waste heat recovery system: one from exhaust gas and the other from scavenged air. Moreover, a sensitivity study of the cycles for different design parameters is performed, and the thermodynamic performances of the various cycles were evaluated. The main components, including turbomachinery and heat exchangers, are designed with in-house codes which have been validated with experiment data. Based on the designed cycle and components, the bottoming S-CO\({}_{2}\) cycle performance under part load operating condition of AAC DG is analyzed by using a quasi-steady state cycle analysis method. It was found that a partial heating cycle has relatively higher net produced work while enjoying the benefit of a simple layout and smaller number of components. This study also revealed that further waste heat can be recovered by adjusting the flow split merging point of the partial heating cycle.  
emergency diesel generator; supercritical carbon dioxide cycle; waste heat recovery system; bottoming cycle
## 2 Methods
### Proposed Approach for Detecting Presymptomatic Parkinson's Disease (PPD)  
Based on the insight of degrading stability in the sensorimotor loop, the proposed approach hinges on the detection of the transition of the human sensorimotor system from a stable system (healthy individual) to an unstable system (individual with PD) on the basis of a response recorded from a simple movement control task in the clinic and repeated several times over a period of time. Note that we expect this transition of the human sensorimotor system from a stable to an unstable system to be gradual, as PD is a slowly progressing disease.  
In control system theory, the _poles_ of a linearized system are a set of complex numbers that represent the nature of (and more specifically the exponents associated with) the transient behavior and thus the stability of that system. The poles of the dynamical system are also roots of differential equations that govern the system's dynamics. A system with poles in the left-half of the complex plane is stable, that is, transient response decays with time, whereas a system with at least one pole in the right half of the complex plane is unstable, that is, the transient response grows with time. We therefore propose to use the poles as a measure of stability of the representative human sensorimotor system involved in a specified clinical task.  
The proposed approach can be summarised as follows. A subject performs a series of specific clinical tasks on several occasions repeated over a period of time (not necessarily at equal intervals), and poles are estimated from these data using an algorithm (explained in Section 2.3.1). Finally, a classification algorithm is applied to the estimated poles to classify whether a particular individual is healthy or has PPD.  
The degradation of stability refers to the gradual movement of at least one pole towards the right half of the complex plane. Thus, monitoring the movement of the pole(s) over time is necessary to observe this phenomenon, rather than evaluating them at a single  
Figure 1: The closed-loop feedback system representing the human sensorimotor system.  
time instant. Consequently, participants must undergo repetitive clinical movement control tests on multiple occasions over a period of time, such as during routine yearly checkups. The detection of significant movement of at least one estimated pole towards the right half of the complex plane during these clinical tasks indicates the possibility of PPD. The transition of the pole(s) towards the right-half of the complex plane occurs gradually, thereby requiring the application of repetitive clinical movement control tests for several years to track the movement of the pole(s) and facilitate the identification of the disease. Therefore, the validation of our proposed approach using real subjects would necessitate a large-scale study over a prolonged period.  
We now consider a simple simulation example to further detail the proposed method.
## 3 Results and Discussion
### Flexure Strength  
The flexural strengths of the recycled concrete with SF, PVA-F, and HF are shown in Figure 11, where the RC flexural strength was 4.87 MPa. From Figure (a)a, it can be seen that the flexural strength of the recycled concrete showed an increasing trend followed by a decreasing trend. The highest flexural strength was achieved when the steel fibre volume dosing was 1.0%, with an increase of 55.2% compared to RC. The main reason for this is that, when the steel fibre content is low, the fibres are evenly dispersed in the concrete, and the fibre and concrete are subjected to common forces and coordinated deformation. As the volume content of steel fibre increased, the steel fibre in sheets  
Figure 10: Cube compressive strength of fibre-reinforced recycled concrete.  
effectively controlled the development of micro-cracks and energy absorption, so that the concrete can continue to carry the load despite cracking. Thus, the flexural strength of recycled concrete was enhanced. However, when the steel fibre was mixed in excessive quantities, the phenomenon of steel fibre agglomeration occurred during the concrete preparation and mixing process. Thus, the flexural strength was reduced.  
As can be seen from Figure (b)b, the flexural strength was optimum at 0.2% by volume of polyvinyl alcohol fibre and increased by 47.9% compared to RC. As the volume amount of polyvinyl alcohol fibre was increased, the flexural strength of the recycled concrete first increased and then decreased significantly. The possible reasons for this are that the reasonable amount of polyvinyl alcohol fibre can be dispersed evenly within the concrete matrix to fill the voids, reducing the possibility of generating through cracks, but the excessive amount of polyvinyl alcohol fibre absorbs a significant amount of water during the concrete mixing process and forms bundles of fibre that are difficult to disperse, increasing the interface weakness of the concrete. Thus, the flexural strength was reduced.  
The flexural strength of hybrid fibre recycled concrete is shown in Figure (c)c. Comparing the three groups of hybrid specimens (RSP-1, RSP-2, and RSP-3), it can be seen that RSP-2 had the highest flexural strength, indicating that the mixing of PVA-F and SF at the same time in the optimal amount of the single admixture was conducive to realize a win-win situation, together limiting the development of microcracks so that the flexural performance was significantly enhanced.
# Nozzle with a Feedback Channel for Agricultural Drones
## 4 Result  
### Spraying Distributions  
The spatial distributions of the spraying area, the average planar area covered by the liquid phase, were obtained by dividing the image, in order to sub-analyzing the windows having a size of 64 by 64 pixels. The spraying area values were obtained by summing the area of the droplets inside the sub-analyzing windows. The final spatial distributions were evaluated and converted to contour plot at each sub-window, by averaging values from 2000 images.  
Figure 7 compares the contours of the spraying distributions. The contours of the spraying area at location 1, in Figure 3, are represented in Figure 8, for a clearer comparison of spraying distributions. Large amounts of droplets are ejected along the centerline of the nozzle in all cases, and the droplet amount decreases with increasing distance from the nozzle tip. In addition, for the quantitative comparison, the line profile of the spraying amount for each load at \(z=5\) is shown in Figure 9. Figure 9 is the sum of the spraying amount for 1 s at the z = 5 position, and we can see that the spraying width increases as the load increases. In the commercial nozzle, the sprayed water is concentrated near the nozzle tip, but the developed nozzle has distinct values farther from the nozzle tip, as well. In order to compare the spraying amount in terms of mass flow rates, the mass flow rates were obtained by calculating the volume of the entire droplet sprayed for 1 s, with an assumption of spherical droplets. The comparison of corresponding mass flow rates is represented in Figure 10. As represented in Figures 9 and 10, both the spraying amount and effective spraying angle increased as the load increased. This means that the proposed nozzle with a higher load makes it easier to make a uniform spray.  
Figure 6: Representative images obtained at the location of (**a**) 1 and (**b**) 2 for different weights.
## 2 Materials and Methods
### Comparison between HJ-1-Based Forest Map and Other Land Cover Products  
To assess the forest cover classification of Northeast China, the forest map resulting from this study was compared with two global land cover products: (1) GlobCover 2009 [60], and (2) MCD12Q1  
Figure 4: Forest classification based on multi-temporal images and an object-oriented method (a, b, c, d, e, f, g, h, i, j, k, l represents threshold at each decision tree node, respectively).  
2009 [61]. The detailed information about these two land cover products is presented in Tables 2 and 3 along with that of the product derived from this study.  
The GlobCover 2009 product is available at its official website ([[http://ionial.esrin.esa.int/index.asp](http://ionial.esrin.esa.int/index.asp)]([http://ionial.esrin.esa.int/index.asp](http://ionial.esrin.esa.int/index.asp))). The MODIS MCD12Q1 land cover product has several land cover classification schemes, and the primary land cover scheme from the International Geosphere Biosphere Program (IGBP) was used in this study, which is available at the USGS website ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov))).  
As the MCD12Q1 land cover products use similar forest classification schemes to those for the HJ-1-based forest map (Table 3), not only the total forest area and spatial distribution were compared, but also the extents and quantities of different forest types were assessed in Northeast China. The GlobCover 2009 product has an obviously different forest classification scheme with eight forest-dominated types being combined into one forest layer, and the comparison between this product and the derived HJ-1-based forest map was made only for the total area and the spatial distribution of forest.  
In addition, the forest area records for the entirety of Northeast China were available by referencing provincial level agricultural census data. These data were documented in the statistical yearbooks of Jilin Province, Liaoning Province, Heilongjiang Province, and the Inner Mongolia Autonomous Region, and were compared with the forest area estimated from the HJ-1-based forest map.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Characteristics** & **GlobCover (2009)** & \begin{tabular}{c} **MODIS Land Cover** \\ **Type (MCD12Q1)** \\ \end{tabular} & **This Study (2010)** \\ \hline \begin{tabular}{c} Sensor \\ Time of Data collection \\ \end{tabular} & \begin{tabular}{c} MERIS \\ Jan.–Dec. 2009 \\ UN LCCS (22 classes) \\ \end{tabular} & \
\begin{tabular}{c} MODIS \\ 2010 \\ IGBP (17 classes) \\ \end{tabular} &
\begin{tabular}{c} HJ-1 CCD \\ 2010 \\ IPCC modified \\ (12 classes) \\ \end{tabular} \\ Spatial resolution & 300 m & 500 m & 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of land cover datasets used in this study.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### Fully Connected Layer  
In the fully connected layer, each neuron is directly connected to every neuron in both the preceding and succeeding layers. This layer performs a weighted sum of the input data, followed by the application of an activation function to introduce non-linearity. This non-linear transformation allows the network to learn intricate relationships in the data, making fully connected layers essential for the final classification step.  
In the proposed model, the genetic algorithm (GA) selects the most relevant features from the combined output of the CNN-LSTM network, ensuring that only the most informative features are passed into the fully connected layers.  
As described in Table 2, the features first pass through a dense layer with ReLU activation. This activation function enables the layer to learn complex and non-linear feature representations, enhancing the model's ability to capture subtle patterns in the data. The output from this layer is then fed into the final fully connected layer, which employs a sigmoid activation function. The sigmoid activation converts the output into probabilityscores corresponding to the target classes, enabling the model to make a final classification decision with high accuracy and confidence.  
This architecture ensures an efficient flow of information, combining optimized feature selection from GA with the powerful representation capabilities of dense layers, resulting in robust and reliable fault classification performance.
## 3 Eight-Scale Parameter Adjustment of Adaptive Inverse Hyperbolic Tangent Algorithm  
The two-scale method is used to enlarge low and high luminance levels. It can be used to automatically adjust the local gain in low- and high-luminance images, making the local details visible. However, the two-scale method ignores medium luminance levels, and this is a potential problem. To address this, a transformation function must be provided that can also retain the linear characteristics for medium luminance levels. For most adaptive enhancements of any part of the luminance range, it can be expanded to four and eight scale models [12, 13]. This transformation function has an adaptive enhancement rate for any part of the luminance range, and thus additionally so for details in the low, medium and high-luminance regions.  
Figure 1 shows a block diagram of the 8 SAIHT algorithm. The input data is converted from its original format to a floating point representation of RGB values. The principal characteristic of the proposed enhancement function is an eight-scale adaptive adjustment of the Inverse Hyperbolic Tangent algorithm, determined by each pixel's radiance.  
Figure 2 shows a block diagram of 8 SAIHT parameter values, including eight-scale _bias_ and _gain_ parameters. The 8 SAIHT will use a range of inputs divided into eight bands for processing by its own parameters, respectively. After reading the image file, the eight-scale parameters (including _biasHHh_, _gainHHh_, _biasHHh_, _gainHHh_, _biasHHh_, _gainHHh_, _biasHLh_, _gainHLh_, _biasHHh_, _gainHHh_, _biasHLh_, _gainHHh_, _biasHLh_, _gainHHh_, _biasLLHh_, _gainLHL_ and _gainLLL_) are computed. These parameters control the shape of the sub-band, respectively.  
The enhanced output image Enhance_8 SAIHT resulting from the eight-scale approach for processing input image \(x\), is described by:(2)  
where \(k\) is the number of sub-band used, and \(x_{k}\) is the sub-band image of the input image.  
Eight-scale is used for the enhancement of the sub-band luminance levels. It will automatically adjust the local _gain_ and _bias_ in the sub-band luminance images, making details visible. This transformation function has high compression for the upper part of the luminance range, and will additionally compress details in the sub-band luminance regions.  
Figure 1: A flowchart of the Eight-Scale Adaptive Inverse Hyperbolic Tangent (8 SAIHT) algorithm.
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 4 The Proposed HWN Architecture
### Multipath Transmission Control Protocol (MPTCP)
After initialization, the MPTCP management layer distributes the data segments over the two links by means of a scheduling mechanism, whose objective is to provide load balancing, improve throughput, and achieve fairness.
## 4 Results and Discussion
### Essential Components and Strategic Spaces  
The analysis of the essential components and strategic spaces began by mapping the critical infrastructure in the historic city center of Guimaraes. In addition, an evaluation of several buildings within this area was conducted, indicated by the brown color on the map (Figure 2a). As mentioned in the methodology section, previous studies on fire risk in this area have already been conducted and documented in [4; 10; 20; 21]. This assessment is made based on the ARICA Simplified Method and takes into consideration some building characteristics: type of use, number of floors, state of conservation, number of people that live on the building with and without disability, and building materials, among others. The Fire Risk Index obtained from these studies is derived from four main phases: (i) the ignition or inception phase, (ii) the development and propagation phase, (iii) the building evacuation phase, and (iv) the firefighting phase. These phases are crucial to assessing and understanding the overall fire risk in the area [4; 10; 20; 21]. Considering that vulnerability is closely linked to fire risk, the level of fire risk for each evaluated building/infrastructure was also mapped (Figure 2b). The historic center contains numerous critical infrastructure, including those related to commerce, culture, education, health, sanitation, social services, transport and telecommunications networks, water, and worship. The remaining mapped buildings are primarily used for residential or service purposes. It is worth noting that the majority of this infrastructure and these buildings have a high Fire Risk Index, highlighting the vulnerability of the city center. Furthermore, the vulnerability of each studied zone was analyzed, considering the number of existing critical infrastructure (Figure 2c). Zone 2 and Zone 3 (as defined in Figure 1b) present high vulnerability, while Zone 5 and Zone 6 (as defined in Figure 1b) exhibit medium vulnerability. In the case of extreme events, these areas are the most susceptible, emphasizing the need for emergency plans to prioritize attention to these areas.  
Additionally, a detailed analysis was conducted to assess the intrinsic vulnerability of each piece of critical infrastructure by evaluating its criticality (Figure 3). The results indicate that the majority of critical infrastructure with very high levels of criticality is situated in Zones 2 and 3, which aligns with the areas exhibiting the highest vulnerability due to the concentration of critical infrastructure. On the other hand, the remaining critical infrastructure exhibits either a very low or low level of criticality. Therefore, even if they have a moderate Fire Risk Index, they do not demonstrate important intrinsic vulnerability.  
Figure 2: Mapping of the (**a**) critical infrastructure present in the study zone and the buildings that were evaluated in terms of fire risk; (**b**) Fire Risk Index for each building and critical infrastructure and (**c**) vulnerability of each zone considering the amount of critical infrastructure present in each zone.
## 3 Results
### Assessing Various TEAS Frequencies in Each Time Slot  
The second task involved investigating how the brain's activity fluctuated across different phases of the TEAS sessions. The classification accuracies for the pre-stimulation, during-stimulation, and post-stimulation phases are presented in Table 3, and the corresponding sensitivity, specificity, precision, and F1 score for each individual class are reported in Table S2 (Supplementary Material). These results, as summarized in Tables 3 and S2, indicate that the EEGNet model achieved high classification accuracies across all combinations of time slots at each TEAS frequency. The accuracies for all frequencies consistently exceeded 92%, demonstrating the model's robustness in distinguishing between brain activities across different phases of the TEAS sessions. This provides a view of how TEAS affects brain activity over time, by not only influencing brain activity during the stimulation period but also having lingering effects, which are crucial for optimizing therapeutic strategies and advancing knowledge of TEAS neurophysiological mechanisms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics\textbackslash{}Slots** & **Slot 2** & **Slot 3** & **Slot 4** & **Slot 5** \\ \hline Total Accuracy & 95.65 & 96.70 & 96.45 & 97.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total classification accuracy during the stimulation across different TEAS frequencies. All values are presented as mean accuracies, with the overall range of standard deviations being 0.2% to 0.62%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comb.** & **Phase Description** & **2.5 pps** & **10 pps** & **80 pps** & **Sham** \\ \hline
1,2,6 & pre-, during-, post-stimulation & 94.65 & 96.12 & 96.65 & 96.24 \\
1,2,7 & pre-, during-, post-stimulation & 95.67 & 96.68 & 94.84 & 92.40 \\
1,2,8 & pre-, during-, post-stimulation & 98.08 & 97.83 & 95.78 & 96.09 \\
1,3,6 & pre-, during-, post-stimulation & 97.45 & 97.21 & 95.84 & 94.51 \\
1,3,7 & pre-, during-, post-stimulation & 96.86 & 97.32 & 94.94 & 95.75 \\
1,3,8 & pre-, during-, post-stimulation & 98.22 & 96.38 & 97.14 & 92.68 \\
1,4,6 & pre-, during-, post-stimulation & 96.59 & 96.13 & 96.85 & 95.78 \\
1,4,7 & pre-, during-, post-stimulation & 95.51 & 97.25 & 94.23 & 94.83 \\
1,4,8 & pre-, during-, post-stimulation & 97.80 & 97.39 & 96.23 & 93.60 \\
1,5,6 & pre-, during-, post-stimulation & 96.23 & 97.17 & 97.84 & 96.38 \\
1,5,7 & pre-, during-, post-stimulation & 96.26 & 97.21 & 96.90 & 95.47 \\
1,5,8 & pre-, during-, post-stimulation & 97.59 & 96.83 & 94.91 & 95.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy for different TEAS frequencies across time slots. All values are presented as mean accuracies, with the overall range of standard deviations being between 0.18% and 0.70%. The column “Comb”. denotes the combinations of time slots used for classification. Slot 1 corresponds to the pre-stimulation phase, slots 2 through 5 correspond to the during-stimulation phase, and slots 6 through 8 correspond to the post-stimulation phase.