esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
The spare parts demand forecast by the proposed artificial neural network approach was explored in this section by using the mathematical tools and techniques discussed earlier. The discovery of the predictor variables or influential factors dictating the historical demand pattern was the most critical part of the research, which ultimately formed part of inputs to the artificial neural network model. Historical data were procured from CASD, which showed lumpiness, thus making forecasting a real challenge. The spare parts were then prioritized based on the highest demand activity and lumpiness factor as shown in Table 2.  
The plot square error dynamics for training a neural network are shown in Figure 7. The MSE training plot reaches a minimum value of 0.074371 after training for 500 epochs. Table 3 shows the computation for testing dataset.  
The trained model was further validated with a normalized (p2, t2) validation dataset. Ultimately, the normalized test dataset p3' was posed to the trained and validated model to obtain the normalized output dataset t3'. The forecasted result required descaling or inverse transformation to depict the real data as shown in Table 4 below (tabulated data are of Tail Rotor Blade).  
The inverse transformation formula requires the maximum and minimum demand data values of the year 2014-2015, which were unavailable; however, approximation to the unknown was carried out by taking the average of the corresponding maximum and minimum values of four quarters for 5 years (2009-2014).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Quarters**} & \multicolumn{4}{c}{**Set of Inputs**} \\ \cline{2-5}  & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & **Actual Output Dataset Values** \\ \hline
1 & 2.8464 & 1 & 0.333333 & 3 \\ \hline
2 & 1.833066667 & 3 & 1.666667 & 2 \\ \hline
3 & 2.8464 & 1 & 0.333333 & 1 \\ \hline
4 & 0.393066667 & 2.333333 & 1.333333 & 3 \\ \hline \multicolumn{5}{c}{**Normalized Dataset Values**} & **Actual = 9** \\ \hline
1 & 0.9 & 0.1 & 0.1 & \\ \hline
2 & 0.569565217 & 0.9 & 0.9 & \\ \hline
3 & 0.9 & 0.1 & 0.1 & \\ \hline
4 & 0.1 & 0.6333333 & 0.7 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Neural network training dataset computation (2009–2015).  
Figure 7: Neural network performance plot.
## 3 Proposed Methods  
This work proposes a method to detect archaeological structures in jungle regions, such as Mesoamerican ones. First, we will broadly describe the proposed method, which uses satellite images from which useful features are extracted. With these features, we perform semantic segmentation of pixels of interest, in our case the archaeological structures, through a supervised classifier trained to detect these pixels. Later, the stages of our method will be explained in detail.  
The proposed method receives an image of the site of interest in RGB color space. We propose applying a color space transformation (RGB to HSL) to generate a new image and use this new image to generate images with different filters. The color space transformation is necessary due to the type of filters we use. Once we have all the generated images (color spaces and filters), their channel values are used as features. Based on these features, a classifier is trained to perform pixel classification that detects the archaeological structure as part of the introduced method. As a result, we can build a new binary image where each white pixel corresponds to a pixel of an archaeological structure, and each black pixel corresponds to a pixel that does not belong to an archaeological structure. Finally, we use the RGB image and the binary image to remove all vegetation from the RGB image to isolate the archaeological structure, generating a segmented RGB image as a result. The general process followed by our proposed method can be seen in Figure 1. In addition, we present a pseudocode Algorithm 1 that shows the process of our method. The following sections describe each stage of the proposed method in more detail.  
```
1:procedureProposedmethod(rgbImage)
2:Color space transformation
3:\(hslImage\leftarrow\) TransformRGBtoHSL(rgbImage)
4:Filters
5:\(laplacian,canny,sobel\leftarrow\) ApplyFilters(hslImage)
6:Detection of archaeological structures
7:\(trainingSet\leftarrow\) GenerateTrainingSet(rgbImage, hslImage, laplacian, canny, sobel)
8:Classify Pixels
9:\(pixelsclassified\leftarrow\) Classifier(trainingSet)
10:Get Structure as image
11:\(structureImage\leftarrow\) GetStructurefromImage(rgbImage, pixelsclassified)
12:return structureImage
13:endprocedure
```  
**Algorithm 1** Workflow of our proposed method.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 6 Performance Evaluation  
In this section, we evaluate the performance of the proposed CLOR protocol by OPNET software [61] and compare it with two cross-layer protocols--CARP and FBR--and an enhanced flooding-based protocol (EFIood).
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### First Set of Tests  
The detection of Defect 1 is the focus in the first set of tests (test numbers 1-6). In these test cases, Defect 2 does not exist, and therefore, the region between 4-4.5 is considered noise. Furthermore, the coherent flexural noises existing in the Defect-1 region are less than those in the second Defect-2 region, with 34 kHz, 30 kHz, and 37 kHz having the least amounts, respectively. As can be seen from Table 3, the defects in these tests have a low SNR and are almost buried in temporal noise, which makes them difficult to be detected in the temporal domain.
## 2 Experiment Description
### Compact Tension Specim  
Based on the standard of the American Society for Testing Material (ASTM E399-09), we designed the CT specimen (Figure 2) [27] and obtained the fracture toughness \(J_{C}\) from the elevated experiments. The CT specimens were prepared, along the TTT direction, in bulk 3D RF material [28]. Two prefabricated cracks with 25 and 20 mm lengths were created by a fresh razor blade. The CT specimens were loaded at a displacement control mode, with a rate of 1 mm/min, by a material test machine (Z005, Zwick/Roell, Germany). Moreover, the fracture toughness measurement for the CT specimens was accomplished in an elevated temperature mechanics test system from 299 to 1273 K. In the elevated temperature furnace, the set temperature was maintained for 20 min (except the room temperature) before the specimens were tested. Based on our experiment experience, the experimental results and the specimen sizes (the diameter was 20 mm and the height was 30 mm), 20 min was sufficient to bring the specimen to equilibrium in the furnace at a high temperature. In addition, the placed thermocouple was in close proximity to the specimen in the high temperature furnace. Then, we were able to measure the temperature of the specimen. Moreover, in this study, the highest testing temperature was 1273 K, which was determined by the high temperature furnace and the fixture fabricated by the high temperature alloy (their working temperature was also about 1273 K).  
Figure 1: Schematic of a scanning electron microscope (SEM) image for the RF bulk material viewing, along the through-the-thickness (TTT) direction.  
Figure 2: (a) CT specimen schematic diagram (\(B\) is the specimen thickness, \(B=W/2\), \(B=25\) mm, the pre-crack length \(a\) is 20 or 25 mm, then \(a/W=0.4\) or 0.5) and (b) is a photo of the CT specimen.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 3 Results  
### Basic Data Analysis  
Table 3 shows the basic AIS data parameters used in the analysis. Based on these data, the numbers of positions and ships were largest for cargo ships, followed by tankers, then passenger ships, and finally towing ships. Among all positional data, the proportions of positions with speeds over 3 kts were 78.7% for cargo ships, 64.8% for tankers, 61.0% for passenger ships, and 33.0% for towing ships. The low value for towing ships can be explained by the main usages of towing ships (berthing and unberthing processes when entering and leaving ports), which are generally performed at low speed. Considering the total numbers of ships over the full timespan, cargo ships were more than three times as numerous as tanker ships. In the case of passenger ships, although the number of ships per season maintained a relatively constant order of magnitude, it decreased by half in winter (December). The increases and decreases in traffic volume of each type of ship demonstrated similar seasonal trends.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Ship Type**} & **Month** & **Number of** & **Number of** & **Number of Ships** & **Data Volume** \\  & **of 2018** & **Positions** & **(SOG \textgreater{} 3 knots)** & **(length \(\geq\) 60 m)** & **(GB)** \\ \hline \multirow{4}{*}{Cargo ship} & 03 & 112,602,933 & 89,955,498 & 7763 & 12.5 \\  & 06 & 131,960,904 & 110,392,193 & 8972 & 15.4 \\  & 09 & 98,351,980 & 75,955,889 & 6886 & 10.7 \\  & 12 & 89,252,999 & 66,234,580 & 4384 & 9.31 \\  & 03 & 53,489,806 & 34,745,229 & 2233 & 4.85 \\  & 06 & 54,331,716 & 36,546,685 & 2334 & 5.10 \\  & 09 & 47,709,507 & 31,655,321 & 1840 & 4.42 \\  & 12 & 48,044,502 & 29,162,586 & 1377 & 4.05 \\  & 03 & 6,568,032 & 3,737,138 & 141 & 0.52 \\ Passenger & 06 & 8,046,074 & 5,322,055 & 165 & 0.75 \\  & 09 & 6,780,066 & 4,281,118 & 145 & 0.61 \\  & 12 & 6,662,976 & 3,862,198 & 76 & 0.55 \\  & 03 & 2,036,415 & 719,845 & 59 & 0.10 \\ Towing & 06 & 2,194,433 & 959,639 & 89 & 0.13 \\ ship & 09 & 2,553,897 & 696,911 & 90 & 0.09 \\  & 12 & 2,926,842 & 756,068 & 47 & 0.10 \\ \hline \end{tabular}
\end{table}
Table 3: Basic characteristics of the AIS data used for analysis.
## 1 Introduction
[29] studied the flow and heat transfer of the impingement cooling system with V-ribs, and the V-ribs enhanced the secondary flow structure in the system and increased the heat flux density on the target plate by 1.34 times. [PERSON] et al. [30] investigated the heat transfer of the target plate with the pin fins with different heights, and with the increase in the pin fins height, the vorticity generated by the pin fins increases, and the \(Nu\) of the target plate increases. [PERSON] et al. [31] studied the effect of circular pin fins on the flow and heat transfer characteristics of the system through numerical simulation and experiment. The result shows that the diameter of circular pin fins is the most important factor for its flow and heat transfer. [PERSON] et al. [32] found through experimental research that pitting the surface of the pin fins can increase the airflow disturbance and improve the impingement cooling. The deeper the pit depth, the better the impingement cooling. The impinging flow together with fanned surfaces has also been employed when coupled with new classes of materials like metal foam, which can increase heat transfer area and promote flow mixing. [PERSON] et al. [33] proposed a concept of a metal foam heat sink with pin fins to improve the cooling performance of high-powered electronics. The metal foam heat sink with pin fins also promotes the improvement of the bottom wall temperature uniformity. [PERSON] et al. [34] carried out multi-objective optimization of fanned metal foam heat sinks by considering heat transfer and pressure drop. At equal pumping power, the finned metal foam heat sink can enhance dissipated heat rates of about 3.3-3.5 times the metal foam heat sink. In order to strengthen the impingement cooling heat transfer in the end wall cavity of the gas turbine guide vane, its structure and parameters have constantly been improved. In this study, the lower surface of the cavity with pin fins, and the upper surface of it is smooth with one step is designed, under the condition of multi-hole impingement and multiple air outlet holes, the heat transfer would be improved and uniform, and flow field in the cavity would be complex. The effects of the impingement hole diameter, the number of impingement holes, and the height, spacing and shape of pin fin on flow and heat transfer are studied. Finally, this study can provide a reference to enhance the turbine vane cooling performance by optimization design.
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 6 Conclusions  
The aim of the study was to explore an appropriate machine learning method for monitor the transformer running state or detector the fault by taking advantage the AE signals. The experiment uses acoustic data from the transformer preprocessed and analyzed using the machine learning algorithms. The results confirm that efficient and quick detection of fault state or distinguish the different type of AE signals is possible. In the dichotomous data catalog for fault detection experiment, the best classifier achieved the sensitivity equal to 100%. Both the SVM with linear kernel PCA and SVM with RBF kernel 2 DPCA have the optimal performance. In the multi-class data catalog for AE signals discrimination experiment, the best classifier achieved the average sensitivity equal to 88.37% and kappa equal to 82.40% when SVM with RBF kernel 2 DPCA was used. These results were obtained with uneven sizes of labeled data in each category, which can be used as a reference for other small data volume multi-classification problems, in addition to sound detection of transformers. Although deep learning is very hot, in some cases, traditional methods such as SVM are still useful. This paper illustrates some advantages of traditional methods with examples, and gives a reference for parameter selection.  
Conceptualization, X.M. and Y.L.; methodology, X.M.; software, X.M.; validation, J.S. and H.X.; formal analysis, J.S.; investigation, J.S.; resources, Y.L.; writing--original draft preparation, X.M. and Y.L.; writing--review and editing, X.M. and Y.L.; visualization, J.S.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China under Grants 61271453 and Shandong Natural Science Foundation under Grants No. ZR2021 MF114.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available upon request by contact with the corresponding author.  
**Acknowledgments:** We express thanks to [PERSON] for his work in the early stage of this research.  
**Conflicts of Interest:** The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Impact of Other Micro and Macro Organisms in MFCs
#### 3.3.1 Bacteria-Based MFCs  
Use of bacteria for MFCS is so evident that often MFC definitions are often defined as bio-electrochemical systems which utilize bacterial metabolism to generate electrical current from a variety of organic substrates [156]. The first generation of bacteria-based  
Figure 8: Classification of MFCs according to their catalytic activities.  
MFCs utilized artificial mediators and in the absence of suitable mediators their current production either plummeted or shut down entirely [31]. Therefore, research interest towards MFCs without artificial mediators significantly increased and still remains as an active research field often referred to as second-generation MFCs. However, only certain microorganisms are suitable for such an operation [48] and are referred to as exoelectrogens [47]. These electrochemically active bacteria are capable of extracellular electron transfer either by having conductive pili or by generating electroactive proteins or molecules that work as natural mediators [48, 52] during their catabolic activity of energy extraction from biomass.  
Two most studied exoelectrogen bacteria for MFCs are _Geobacter sulfureducens_[49, 162] and _Shewanella oneidensis_ MR-1 [49, 163, 164]. _Geobacter sulfureducens_ only performs a direct electron transfer mechanism, either through the extracellular pili can be by pili formed by Pila or appendages formed by the cytochrome OmcS [165]. The _Shewanella_ species can perform both indirect electron transfer and direct electron transfer by outer membrane (OM) cytochromes mechanism within MFC. For indirect electron transfer they utilize self secreted electron mediators and direct electron transfer is performed via outer membrane cytochrome \(c\) and a nanowire [164]. It should be noted that nanowires in _Shewanella_ are extensions of the membrane and, therefore, the direct electron transfer is also performed by OM cytochromes.
###### Abstract  
Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2 Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.  
leaf nitrogen; grass quality; field spectrometer; Sentinel-2; mapping; red edge band +
Footnote †: journal: Remote Sensing, 10, 269
## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Continuous** & **Incremental** & **Difference** & \(p\)**-Value** \\  & **Cuff Inflation** & **Cuff Inflation** & **Difference** & \(p\)**-Value** \\ \hline MALES & & & \\ Dominant Leg (\(n=20\)) & \(209.4\pm 29.4\) & \(208.5\pm 27.1\) & \(0.9\pm 10.2\) & 0.682 \\ Non-dominant Leg (\(n=20\)) & \(206.2\pm 31.5\) & \(204.6\pm 30.5\) & \(1.6\pm 8.2\) & 0.394 \\ Difference & \(3.2\pm 42.8\) & \(3.9\pm 38.2\) & & \\ Combined Legs (\(n=40\)) & \(207.8\pm 30.1\) & \(206.5\pm 28.8\) & \(1.3\pm 9.1\) & 0.383 \\ \hline FEMALES & & & \\ Dominant Leg (\(n=20\)) & \(211.3\pm 57.8\) & \(210.5\pm 53.8\) & \(0.8\pm 8.1\) & 0.643 \\ Non-dominant Leg (\(n=20\)) & \(203.5\pm 48.3\) & \(207.0\pm 50.2\) & \(-3.4\pm 8.1\) & 0.073 \\ Difference & \(7.8\pm 43.4\) & \(3.5\pm 42.1\) & & \\ Combined Legs (\(n=40\)) & \(207.4\pm 52.7\) & \(208.7\pm 51.4\) & \(-1.3\pm 8.3\) & 0.327 \\ \hline SEX DIFFERENCES & & & \\ Dominant Leg (\(n=20\)) & \(1.9\pm 14.5\) & \(2.0\pm 13.5\) & & \\ Non-dominant Leg (\(n=20\)) & \(-2.6\pm 12.9\) & \(2.4\pm 13.1\) & & \\ \hline COMBINED MALES/FEMALES & & & \\ Dominant Leg (\(n=40\)) & \(210.4\pm 45.2\) & \(209.5\pm 42.2\) & \(0.9\pm 9.1\) & 0.534 \\ Non-Dominant Leg (\(n=40\)) & \(204.8\pm 40.3\) & \(205.8\pm 41.1\) & \(-0.9\pm 8.4\) & 0.493 \\ Difference & \(5.5\pm 42.6\) & \(3.7\pm 39.7\) & & \\ Combined Legs (\(n=80\)) & \(207.6\pm 42.6\) & \(207.6\pm 41.4\) & \(-0.01\pm 8.7\) & 0.990 \\ \hline \end{tabular} Values are mean \(\pm\) SD. No significant differences (adjusted \(p>0.01\)) in all comparisons.  
\end{table} 
Table 3: Arterial Occlusion Pressure Using Two Cuff Inflation Protocols.
# Denoising Diffusion Models on Model-Based Latent Space
## 3 Proposed Method
### Latent-Space Encoding  
Lossy image compression is a fundamental branch of image processing that addresses the problem of reducing the data required to represent an image by selectively discarding the information that is deemed less relevant for the human perception. Typical lossy compression algorithms encode images in ways that exploit the inherent redundancy, allowing them to be efficiently represented using fewer bits. The first aspect to consider when designing a compression algorithm that can be leveraged for latent-space image synthesis is the convolutional encoder-decoder style of the generative model itself, which can only produce output of fixed size. Conversely, the most popular image compression algorithms, with JPEG on top of all, produce variable-length compressed representations for different images. For this reason, we devise a simple yet effective compression strategy based on VQ techniques, which maintains a constant compressed representation size for each patch by employing predefined codebook mapping input vectors to fixed-length code words, thus ensuring consistent compression regardless of image content. Similar strategies have been popular since the 1980s (see e.g., [13; 14]) for their simplicity and overall good performances. In the deep learning era, VQ-like approaches were rediscovered [12], as was the case with other compression techniques [15; 16].  
Given an RGB image \(x\in\mathbb{R}^{w\times h\times 3}\), the model-based compression algorithm produces a latent representation \(z\in\mathbb{R}^{\frac{w}{h}\times\frac{1}{h}\times x}\). The compression algorithm is designed by first converting the image to the YCbCr color space, this separates the image information into its perceptually relevant components, enabling a better use of compression techniques. Then, for each channel from 1 to 3, the image is divided in non-overlapping patches of \(s\times s\) pixels, with each patch capturing a localized region of the image, preserving both spatial and chromatic information. Then, finally, the core of our compression techniques involves vector quantization applied to each individual patch. Vector quantization replaces each patch with one (or more) representative vector, denoted as codewords, from a fixed-size codebook, thus allowing the whole (\(s^{2}*8\) bit) block to be encoded as the index \(i\) of the assigned codeword. Clearly, the strategy of the definition of the codebook and the codewords assignment strategy are crucial to set the optimal tradeoff between compression ratio and information preservation; therefore, hereafter, we detail the multiple strategies experimented. The complete compression algorithm is schematized in Figure 1. In Section 4.1, instead, we flesh out all the technical details of the practical implementation of the quantization approaches, together with results on a lossy compression benchmark.