esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Suspended Particulate Matter: Distribution, Grain Size and Sources
As already stated for PHg, in the case of PMeHg a strong correlation with fine particles (2-8 \(\upmu\)m) was observed in autumn when the river discharge was highest (N = 17, r = 0.779, \(p\) < 0.01). Although the PMeHg concentrations were extremely low, a significant correlation with PHg was found in the case of a high river flow observed in autumn (N = 17, r = 0.943, \(p\) < 0.01) most likely due to a vertically well-mixed water column along the Nalon River estuary. Conversely, a weaker correlation was observed in spring, when the river discharge was lower (N = 17, r = 0.772, \(p\) < 0.01) and no correlation was observed in summer. However, a slight increase in the PMeHg concentration was found in saltwater from autumn (\(\leq\)0.005 \(\upmu\)g g\({}^{-1}\)), to spring (0.006-0.015 \(\upmu\)g g\({}^{-1}\)) and summer (0.015-0.022 \(\upmu\)g g\({}^{-1}\)) following a decrease in the river discharge. This can be related to the role of tidal currents which were stronger when the river discharge was low (summer), thus promoting the resuspension of fine particles which were more enriched in MeHg with respect to those transported by the river freshwater. Nevertheless, MeHg percentages in the SPM were found to be extremely low and generally below 1% (0.12-0.65%, 0.09-1.56% and 0.09-0.27% in spring, summer and autumn, respectively). This is in agreement with the results of previous investigations focused on estuarine sediments thus demonstrating that only low amounts of the total Hg are in methylated form (from 0.003 to 0.009 \(\upmu\)g g\({}^{-1}\) of MeHg in the surface sediments) [33] and potentially resuspended in the water column [37]. Iron and Mn were found to be notably present in the SPM and their average concentrations increased from summer (6.75 \(\pm\) 1.72% for Fe and 4.60 \(\pm\) 1.29% for Mn) to spring (8.17 \(\pm\) 1.33% for Fe and 4.98 \(\pm\) 1.19% for Mn) following the increase in the river flow showing the maximum values when the river discharge was higher in autumn (10.9 \(\pm\) 1.38% for Fe and 5.86 \(\pm\) 0.96% for Mn) (Table S2). The occurrence of such elevated concentrations of PFe and PMn could be the result of the combined effect of riverine suspended particles together with the resuspension of bottom sediments. In addition, Asturias is known as one of the main Hg mining districts worldwide and anthropogenic inputs of Hg and As in the Nalon River estuary are mainly due to the long-term mining activity in the river basin [25; 29; 31; 33]. In detail, the physico-chemical alteration of tailing deposits at La Soterrana, El Terronal and Los Rueldos decommissioned mine sites can lead to an increasing mobility of Hg, As and other potentially toxic trace elements in the Caudal River freshwater, which is the second most important tributary of the Nalon River [59]. This can also be a possible explanation for the elevated PFe and PMn concentrations since iron sulphides (pyrite and marcasite) and iron oxides were found to be the most abundant mineral phases in mine tailings [24].
###### Abstract  
The present study investigated the influence of the hybridization of steel and polyolefin fiber on the mechanical performance and chloride ion penetration of base concrete designed for marine shotcreting purposes. The purpose of fiber hybridization is to reduce the risk of corrosion that might occur during service life. Sets of hybrid fiber reinforced base concrete, whose water to binder ratio was 0.338, were prepared. The fiber contents in the base concrete were 0.54 and 1.08 vol%, and the volume proportion of polyolefin fiber in the hybrid fiber varied from 0 to 100%. Although the effect of fiber hybridization was not clearly observed from the compressive strength, a synergetic effect which increased both the flexural strength and toughness occurred at a fiber content of 1.08 vol%. The optimum ratio of steel and polyolefin fiber was 50:50. With respect to chloride ion penetration, an increasing amount of steel fiber increased the amount of current passing through the base concrete specimen due to the presence of electrically conductive steel fiber. However, chloride ion diffusivity was not greatly affected by the presence of steel fiber.  
steel fiber; polyolefin fiber; hybridization; flexural strength; flexural toughness; chloride ion penetration +
Footnote †: journal: Applied Sciences  
11 October 2021  
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**C Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### Results of Combined Features by Wavelengths and Texture Information
For example, Ash and VM represent the mineral inorganic content and the coalification degree of coal, respectively, which greatly contribute to the coal density [57], grindability [58] and coal color variation, resulting in differences in the texture features of coal images. Variations in MC can also affect the brittleness and strength of coal, altering the coal properties and the external appearance [59]. Among three texture methods, the texture features with the best performance for each index were selected for combined modeling with spectra date. By comparing the model results using texture features and the combined spectral-texture features in Table 5, it can be found that the models combining texture and spectra features have the best performance of accuracy, all of which demonstrate improvement compared with the models based on single spectra or texture features. The optimal performance of Ash is \(R_{p}^{2}=0.993\), \(RMSE_{p}=0.659\), \(RPD=12.217\), reflecting improvements in \(R_{p}^{2}\) of 0.009 and 0.182 compared with models using only feature wavelengths or texture information. For VM, the optimal performance of VM is \(R_{p}^{2}=0.989\), \(RMSE_{p}=0.583\), \(RPD=9.904\), with enhancements of 0.018 and 0.062 in \(R_{p}^{2}\). The optimal performance for MC is \(R_{p}^{2}=0.979\), \(RMSE_{p}=0.112\), \(RPD=7.159\), and \(R_{p}^{2}\) improves by 0.207 and 0.022. For FC, the optimal performance is \(R_{p}^{2}=0.948\), \(RMSE_{p}=0.838\), \(RPD=4.544\), with \(R_{p}^{2}\) improving by 0.039 and 0.434. CV shows an optimal performance of \(R_{p}^{2}=0.994\), \(RMSE_{p}=0.217\), \(RPD=13.849\), \(R_{p}^{2}\) improved by 0.003 and 0.375 in spectra and texture features models, respectively. The comparison reveals that MC exhibits greater enhancement, probably due to the fact that moisture is more sensitive to coal texture features. All coal quality indices achieved the best prediction performance in the LSSVR model based on combined spectral-texture features. Figure 7 shows the scatter plots of the optimal model predictions for different coal quality indices. It can be found that the predicted sample points of the model are centrally distributed on the 1:1 line, which indicates that the real value and the predicted value fit well, and the model has excellent prediction.
# FA-VTON: A Feature Alignment-Based Model for Virtual Try-On
## 4 Methods
### Parser-Free Framework Based on Knowledge Distillation
#### 4.5.2 Channel-Wise Distillation (CWD)  
Due to the teacher model's input containing various parser-based prior knowledge, we introduce channel-wise knowledge distillation [42]. This method focuses on features from different channels, softly adjusting the activations of corresponding channels between the teacher and student networks. To perform this, we first convert the channel activations into probability distributions and measure their differences using probability distribution metrics, adopting KL divergence in our text. Subsequently, we use a trained teacher model to obtain activation maps for predicting channel-specific masks, enabling the student network to learn useful knowledge from the teacher network.  
We denote the feature maps of the teacher network and the student network as \(y_{c}^{T}\) and \(y_{c}^{S}\), respectively. Then, the channel-wise distillation loss can be expressed in general form as follows:  
\[L_{cvd}=\varphi\Big{(}\phi\Big{(}y_{c}^{T}\Big{)},\phi\Big{(}y_{c}^{S}\Big{)} \Big{)}, \tag{12}\]  
where \(\phi(\cdot)\) is used to convert the feature values into probability distributions as follows:  
\[\phi(y_{c})=\frac{exp\Big{(}\frac{y_{cj}}{\tau}\Big{)}}{\sum_{i=1}^{W:H}exp \Big{(}\frac{y_{cj}}{\tau}\Big{)}}, \tag{13}\]  
where \(c=1,2,\dots,C\) represents the channel; \(i\) represents the pixel position within the channel; and \(\tau\) represents the temperature parameter for distillation, where a higher \(\tau\) leads to softer output probability distributions, meaning each channel focuses on a larger spatial area. If the number of channels between the teacher and student networks does not match, a \(1\times 1\) convolutional layer is used to upsample the number of channels in the student network. This paper uses KL divergence to evaluate the difference between the output channel distributions of the teacher and student. The formula is as follows:  
\[\varphi\Big{(}y^{T},y^{S}\Big{)}=\frac{\tau^{2}}{C}\sum_{c=1}^{C}\sum_{i=1}^{ W:H}\phi\Big{(}y_{c,i}^{T}\Big{)}\cdot\log\left[\frac{\phi\Big{(}y_{c,i}^{T} \Big{)}}{\phi\Big{(}y_{c,i}^{S}\Big{)}}\right]. \tag{14}\]  
KL divergence is an asymmetric measure. In the above equation, it can be seen that when \(\phi\Big{(}y_{c,i}^{T}\Big{)}\) is larger, \(\phi\Big{(}y_{c,i}^{S}\Big{)}\) should be as large as \(\phi\Big{(}y_{c,i}^{T}\Big{)}\) to minimize KL divergence. When \(\phi\Big{(}y_{c,i}^{T}\Big{)}\) is smaller, KL divergence pays less attention to minimizing \(\phi\Big{(}y_{c,i}^{S}\Big{)}\). Therefore,the student network can learn significant features from the teacher network, especially when student networks do not have human-agnostic images as inputs.
###### Abstract  
To effectively mitigate the effects of atmospheric turbulence in free space optical (FSO) communication, we propose a parallel carrier frequency offset estimation (FOE) scheme based on time-tagged QPSK partitioning (TTQP). This scheme can be applied to spatial diversity polarization multiplexing (PM) coherent FSO communication systems. Specifically, the TTQP scheme performs QPSK partitioning by time-tagging signal points, accurately recording the time intervals between signals, and significantly reducing implementation complexity through a modified Mth power algorithm. The simulation results for the PM 16-quadrature amplitude modulation (QAM) validate the effectiveness of the proposed scheme. Compared to traditional QPSK partitioning algorithms, the TTQP algorithm achieves high accuracy, low complexity, and multi-format versatility in high-speed coherent FSO communication.  
free-space optical; frequency offset estimation; atmospheric turbulence; quadrature amplitude modulation; QPSK partitioning +
Footnote †: journal: _Photonics_  
0
Footnote 0: _Princeton University, Princeton, NJ 00047, USA_
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Smart Agriculture Scenario
#### 4.2.2 RX at 615 m
This is due to its poor PDR; (**f**) Energy consumed per 127 B packet transmitted. O-QPSK is the less electric charge-consuming radio setting, with 29.5 mC while the reference consumes 250 mC.
###### Abstract  
Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS's energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.  
edge caching; energy-efficient; mobile edge computing; 5G cellular networks +
Footnote †: journal: Applied Sciences
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 1 Introduction
This variation has considerable influence on the energy of the signals. For example, a higher forward speed might create a higher energy for the same section of track than a lower speed. Therefore, it is difficult to determine whether the change in the signal energy is due to speed variation or track change. These challenges in the energy-based methods are addressed in this paper. In this paper, an algorithm is proposed for railway track fault detection while using measurements on the bogie of an in-service train. A regular Irish Rail passenger train was instrumented while using accelerometers and a Global Positioning System (GPS). The location of the train and its approximate forward speed were recorded using the GPS system. Six kilometres of Dublin-Belfast railway line is inspected here from 60 train passes. The instantaneous amplitudes of the acceleration signals are extracted while using the Hilbert transform. A novel process is proposed for finding the energy levels of the amplitudes in the space domain. A data cleaning process is performed to remove the passes with low energy due to low train forward speeds. The cleaned data are then scaled using a new scaling method that emphasizes the importance of the vehicle forward speed to the level of energy. The scaled energy signals show a high level of consistency, which is ideal for the frequent inspection of railway tracks. It is suggested that a consistently high level of scaled energy over multiple passes might indicate a possible defect in the track. The TRV data recorded after the train measurements are used to check the track condition at two areas with high energy levels. It is shown that high energy parts of the track match well with the faults identified in the TRV data.
# A Comprehensive Methodology for Investment Project Assessment Based on Monte Carlo Simulation
## 3 Application of the Methodology on a Virtual Investment Project
### Optimization of the Production Plan  
The preliminary production plan, partially based on market requirements and considering the contracted production volume for product A, is subsequently optimized for the target requirement of maximizing NPV.  
In the software settings, it necessary to define the production volume A, B, and C as controlled variables. That means that the program will search for an optimal solution so that the produced amounts of products A, B, and C are such that we maximize the goal and meet all restrictions.  
The optimization requirements are set as follows:  
* The objective of the optimization is to maximize the mean NPV.
* Restriction for optimization:
* Constraints in the use of the time of the production line. Minimum use of 7100 h and maximum available line time capacity of 7300 h.
* The production volume of product A is limited to a minimum of 700 t/year due to the contracts.
* For the controlled variables (production volumes A, B, C), a minimum volume of 10 t/year was set (because at least a minimum production would be available and the product would not be dropped from the production program), and a maximum volume of 1000 t/year (which was only a theoretical limitation--such a volume would not be sold on the market due to the demand forecast).  
Figure 6: Sensitivity analysis for NPV.  
In this optimization, the requirement for restrictions in terms of risk is not considered. As a risk, it is possible to understand, e.g., high dispersion of NPV values expressed by the standard deviation or the high coefficient of variation. It can also be the minimum value of the NPV indicator or another statistical characteristic of the output (skewness, kurtosis, etc.).  
The output of the optimization is presented in Figure 7. It can be seen that the optimal solution was found very quickly during simulation, and it represents production in the volume of 999 t/year of product A, 10 t/year of product B, and 166 t/year of product C. These are extreme values within the intervals for the production volume limit. Although this production would represent the maximum NPV value (EUR 3,989,753), the standard deviation would rise to EUR 126,749, which is EUR 6,746 more than with the original production program (Figures 7 and 8).
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 3 Experimental Results and Discussion
### Performance Comparison with Existing Literature  
Table 8 illustrates the highest performance measure obtained from different existing works related to fMRI based ASD identification using brain atlases and our proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site ID** & **No of Subjects** & **Accuracy** & **Sensitivity** & **F1-Score** \\ \hline PITT & 50 & 0.94 & 0.96 & 0.94 \\ YALE & 40 & 0.95 & 0.91 & 0.95 \\ NYU & 172 & 0.92 & 0.92 & 0.91 \\ UM & 119 & 0.93 & 0.92 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison using Model-2 and BASC atlas on data obtained from individual screening sites.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **L-SVM** & **KNN** & **DT** & **RF** & **GNB** & **Model-2** \\ \hline AAL & 0.6613 & 0.481 & 0.5224 & 0.5637 & 0.6176 & 0.8737 \\ BASC & 0.6166 & 0.5473 & 0.5115 & 0.5427 & 0.62 & 0.8787 \\ CC200 & 0.6865 & 0.5488 & 0.5166 & 0.574 & 0.6026 & 0.8668 \\ POWER & 0.6697 & 0.5265 & 0.5161 & 0.5254 & 0.6062 & 0.8533 \\ \hline \hline \end{tabular}  
* Here, L-SVM indicates linear support vector machine; KNN means k-nearest neighbor; DT represents decision tree; RF indicates random forest and GNB means Gaussian Naïve Bayes classifier.  
\end{table}
Table 7: Performance comparison with different machine learning algorithms.  
The present study marked a significant performance improvement compared to existing studies. Despite that, some limitations need to be addressed. Only functional MRI data were utilized here for classification, whereas a combination of functional and structural MRI data has proven to achieve high prediction accuracy in [31; 56]. Therefore, in future studies, other imaging modalities, such as structural MRI along with functional MRI data, may contain complementary information regarding ASD. However, implementation of the domain adaption technique [57] and encoding decoding technique [58] would also aid in prediction with more reliability and generalize well on unseen data obtained from different screening sites following different acquisition protocol. Other advanced neural network architectures, such as CNN, 3D based CNN model, etc., can also be utilized for prediction purposes and might prove to be fruitful. Furthermore, other options for implementing pipeline steps, such as usage of other available atlases, such as CC400, HO, Dosenbach, MSDL, etc., usage of first principal component-based time series extraction as in [59], non-correlation based functional connectivity matrix parametrization as in [60] and graph-based spectral method of vectorization as in [61], are aimed to be implemented in future studies.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 4 Results and Analysis  
The proposed method in this paper utilizes the evaluation metrics mentioned in Section 3.2 for quantitative assessment. Ablation and comparative experiments were conducted to demonstrate the advantages of the proposed method. In the comparative experiments, ADSC-U-Net was compared to U-Net, SegNet [31], DeepLab [32], and DeepCrack [33] to validate the effectiveness of the proposed algorithm.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 2 Proposed Method
### Data Embedding in the Encrypted Image  
After receiving the encrypted image \(E\) and auxiliary information, the data hider can embed secret bits into the image even if he/she does not know the content of the original image. Firstly, the bit planes of pixels which are encrypted in \(I_{ED}\) are replaced with auxiliary information, and then the replaced bit planes and secret bits are concatenated as embedded data, and the embedded data is embedded into the encrypted image. An overview of the process of encoding phase is presented in Figure 4.  
The number of pixels in \(I_{W}^{s}\) is \(S\), which are immediately arranged behind the pixels of \(I_{B}\). Thus, we can know which part of the pixels belong to \(I_{W}^{s}\). Then the data hider can use the data-hiding key \(K_{h}\) to pseudo-randomly select the pixels of \(I_{W}^{s}\) for data embedding. In the proposed method, the (\(m\)+1)th-8 th MSBs of these selected pixels are directly replaced by embedded data to generate a marked encrypted image, and the values of the marked pixels are derived as:  
\[E_{h}^{\prime}(i,j)=\sum_{l=1}^{m}E_{l}(i,j)\times 2^{l-1}+\sum_{l=m+1}^{8}d_{l -m}\times 2^{l-1} \tag{11}\]  
where \(d_{l-m}\) is a secret bit with a value of 0 or 1.  
Each pixel of \(I_{W}^{s}\) can accommodate (8-m) bits of data, and the number of pixels in \(I_{W}^{s}\) is determined by \(m\) and \(T\). Thus, the embeddable capacity of an image under fixed \(m\) and \(T\) is:  
\[Cap=(8-m)\times S \tag{12}\]  
Figure 4: Overview of the encoding phase.  
Obviously, the maximum embeddable capacity of an image depends on the value of \(m\) and \(T\). How to obtain the optimal values of \(m\) and \(T\) is introduced in Section 3.
## 4 Real-Scale Vehicle Tests and Result Analysis
### Test and Result Analysis  
#### 4.3.1 Leaf Spring Failure (Case 1)  
Firstly, the mode shapes and modal energy differences of the leaf spring with and without failure were analyzed. The modal frequencies and mode shapes of the baseline suspension and Case 1 were compared, and the results are shown in Figure 13.  
Figure 13a,b shows the bounce and pitch mode vibration shapes at baseline; Figure 13c,d show the bounce and pitch mode shapes of the spring with 80% stiffness. It is apparent that the amplitudes of pitch and roll motions in Figure 13c,d are greater than that in Figure 13a,b, which verifies the results obtained by the simulation.  
Then, the modal energy difference in bounce and pitch mode was obtained to highlight the change of stiffness. It can be seen from Figure 14 that the MED differences change with the stiffness of the faulty suspension spring. It can be concluded that based on the ACS-SSI modal identification method, the spring stiffness fault can be diagnosed by the change of the modal energy difference. In practical applications, appropriate test calibration for different vehicle types and fault forms can make the algorithm more widely used and more suitable for online monitoring.  
Figure 14: Difference of rear MED between baseline and spring fault.  
Figure 13: Differences of mode shape between baseline and spring fault. RF: Right-Front; LF: Left-Front; LR: Left-Rear.