esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Observation of Gaseous Components and Analysis of Agricultural Chemicals in Greenhouse
The agricultural chemicals that were analyzed contained VOCs in the range of 13 to 710 mg/L, and the VOCs level was high in this order: vapor deposition agent > germicide > herbicide > pesticides at 710.71, 86.43, 33.63, 13.92 mg/L, respectively. Looking at the representative VOCs contained in the agricultural chemicals that were investigated, the % contents in pesticides, fungicides, and herbicides were high in this order: benzene > ethylbenzene > m&p-Xylene > toluene > styrene > o-Xylene at 33.58, 16.37, 15.33, 12.03, 8.36, 4.76%, respectively, and all other aerosols were less than 3%. Even though benzene was not analyzed in the pesticide sample, the overall average of Benzene is the most abundant among the major VOCs. This means that the main VOCs of pesticides, fungicides, and herbicides, except for vapor deposition agents, were BTEXS, which is consistent with BTEXS soil contamination, which is increasing due to agricultural activities in rural areas in China, as suggested in a previous study [33]. Meanwhile, the vapor deposition agent showed the highest ratio of naphthalene (58.90%) and acrylonitrile (36.63%) among the total VOCs. However, the concentration of BTEXS in the vapor deposition agent was higher than that of the other VOCs, and naphthalene and acrylonitrile are considered to be characteristics of the agricultural chemicals measured in this study.
## 3 Results and Discussion
### X-ray Diffraction  
Compared with 3 and 7 days, the reaction extent of nS at 28 days is much higher, and CH reduction is significant [10]. Thus, XRD analysis was done at the age of 28 days. The XRD patterns of UHSC at the curing age of 28 days are presented in Figure 8. A 2 \(\theta\) degree angle between 10 and 75 degrees was selected. For concrete with a low water-binder ratio (0.2), a large amount of cement is not hydrated, so XRD analysis found the majority of cement remained non-hydrated. The trend of Ca(OH)\({}_{2}\) (CH) consumption was determined by the intensity variation of the main diffraction peaks (A--Alite, B--Belite, P--Portlandite). Compared with nS0, the CH intensities corresponding to nS2 and nS4 specimens decreased. This was due to the pozzolanic reaction of the added nS and CH, which consumed the CH produced by cement hydration [43]. This finding is consistent with that of [PERSON] et al. [13], which indicated higher pozzolanic performance in the presence of a higher percentage of nS. In addition, XRD a qualitative analysis method. For the quantitative measurement of CH content, thermogravimetric analysis (TGA) is much better. A TGA should be done in further study.  
Figure 7: Development of the IRH and IT (**a**) and autogenous shrinkage (**b**) with the age of the nS4 specimen. Linear regression results (**c**) of the IRH and autogenous shrinkage of the nS4 specimen in stage II.
## 5 Case Study
### Verify the Evaluation Index of Dispatchability on EGCS
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period/h** & **1** & **2** & **3** & **4** \\ \hline \(P_{\text{F}}^{1}\)/MW & 0 & 50 & 86.67 & 63.33 \\ \(P_{\text{F}}^{2}\)/MW & 0 & 46.67 & 73.33 & 46.67 \\ \(P_{\text{F}}^{3}\)/MW & 80 & 23.33 & 40 & 40 \\ \(R_{\text{F}}^{1}\)/MW & 0 & 10 & 13.33 & 11.37 \\ \(R_{\text{F}}^{2}\)/MW & 0 & 13.33 & 26.67 & 28.33 \\ \(R_{\text{F}}^{3}\)/MW & 0 & 36.67 & 60 & 35 \\ \(E_{\text{F}}\)/MWh & 1.6 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Output of units and their dispatching capacity allocation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable index** & **Period1** & **Period2** & **Period3** & **Period4** \\ \hline Dispatching capacity \(R_{\text{F}}\)/MW & 120 & 170 & 150 & 200 \\ dispatchability requirement \(F_{\text{F}}\)/MW & 0 & 60 & 100 & 74.7 \\ dispatchability margin \(M_{\text{F}}\)/\% & — & 183.3 & 50 & 167.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dispatchability evaluation index. As shown in Table 6 and Figure 8, when the reliability level of the system is too high, the optimal dispatching model will not converge; that is, the optimal solution cannot be obtained. The system is relatively simple, so it is impossible to meet the high reliability. With the decrease in system reliability level, the value of dispatching capacity will change slightly, the demand for dispatchability will change greatly with the reliability level, and the system dispatchability margin index will show an overall upward trend. At the same time, because the reliability required in the system is reduced, the optimal solution of the system operating cost is also gradually reduced, indicating that the system reliability and the system dispatchability margin are negatively correlated, but with the system operating are cost positively correlated.
## 6 Discussion
### Limitations and Future Development  
From the prediction results for each mode, we can see that the model's ability to capture data trends improved for decomposed data, achieving our initial experimental goal: for nonlinear data with poor time series characteristics, we can first decompose them into relatively stable data and then learn their patterns. Although decomposing the data into multiple columns has reduced the difficulty of finding data patterns, it also introduces the possibility of error accumulation, which may lead to suboptimal final results. While this method has shown some effectiveness in predicting earthquakes between magnitudes _Ms_ 5.0 and 6.0, it also shows limited sensitivity to earthquakes of magnitude _Ms_ 6.0 and above.  
1. Seismological Parameters and Data Sources: The seismological parameters used in this study are calculated from earthquake catalog data. Therefore, we are still relying only on catalog data, which may not fully reflect the occurrence of large earthquakes. Prior research has shown that anomalies in thermal infrared data, ionosphere, gravity and magnetic data, and groundwater level changes occur before earthquakes [49; 50; 51; 52; 53; 54]. Therefore, combining earthquake catalog data with these other types of data might better reflect the impending arrival of large earthquakes and improve forecast accuracy.  
2. Limited Samples of Large Earthquakes: The number of earthquakes of magnitude _Ms_ 6 and above in our dataset is relatively small. This limited number of large earthquake samples results in a poorer learning of patterns preceding their occurrence.  
3. Model Limitations: The deep learning model used may not effectively capture underlying patterns. Future research could explore more complex and powerful models, such as Transformer, to better learn these patterns.  
4. Decomposition Methods: While the VMD method has been effective in transforming poorly timed monthly maximum magnitude data into more stable data, potentially enhancing forecast accuracy, other decomposition methods like Successive Variational Mode Decomposition (SVMD), Feature Mode Decomposition (FMD), and Wavelet Transform might achieve even better results in earthquake prediction research [55; 56; 57].  
5. Regional Segmentation: The current study focuses on the entire North-South Seismic Belt. Future research could segment this region into southern, northern, and central sections for separate analysis. This approach would effectively reduce the impact of inter regional heterogeneity on the model, clarifying seismic activity characteristics within each sub-region. Independent analysis and prediction for each segment could lead to the more accurate identification of local seismic patterns and improved forecast accuracy.
## 3 Sea Experiment in the East Sea  
### Overview  
On 20-21 August 2015, an experiment was conducted in the East Sea of Korea to measure the ocean bottom reverberation using an active triplet towed array. The experiment site was located off the coast of Pohang city, which is in the east-southern region of East Sea, as shown in Figure 2a. The water depth varies, approximately between 500 to 1500 m. The state of the sea was calm, and the wind speed was between 1.7 and 2.8 m/s, so we expect that the surface reverberation was small. The research vessel (R/V) Cheong-Hae towed a variable depth source and a triplet towed receiver array, a nested array consisting of 96 triplets as shown in Figure 3.  
The trajectories of the research vessel are displayed in Figure 2a. Although a total of five types of source waveforms were used in the experiment, we analyzed only two waveforms, namely, the continuous wave (CW) and the linear frequency modulation (LFM). Their center frequency is 3 kHz. The CW used has pulse durations of 0.1, 0.3, and 1 s, respectively. The bandwidths of the LFM were set to 400 Hz, 1 kHz, and 2 kHz. For two days, a total of 678 pings (364 pings on the first day, 314 pings on the second day) were launched. Among them, the number of CW pings was 265 and the number of LFM pings was 273.  
Figure 2: Sea trial environment for the measurement of ocean reverberation in the East Sea, Korea. (**a**) Trajectory of the research vessel at the experimental site. The symbols of red circles and blue crosses indicate the positions where the CW pings and the LFM pings are launched, respectively. The pentagram symbols and the “Trajectory B” denote the positions of the pings used for the analysis. (**b**) Mean sound speed profiles of the first and second days.  
The sound speed profile was acquired with expendable bathythermographs (XBTs). Figure 2b shows the mean sound speed for each day and we see that the minimum sound speed depth was observed between 226 m and 274 m. For this deep-water sound speed, the bottom-reflected wave is dominant. The ocean sediment at the experimental site consists of fine silt and clay corresponding to a mean grain size \(M_{z}\) of 7\(\Phi\) to 10\(\Phi\).  
During the experiment, the operation depth of the source and receiver varied. Figure 4a shows the mean depth of the source and receiver and their ping number. The operating depth of the source and receiver was between 4 to 240 m on the first day, and 4 to 74 m on the second day. Figure 4b represents the mean tow speed of the research vessel.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
###### Abstract  
Image-based data hiding methods have been used in the development of various applications in computer vision. At present, there are two main types of data hiding based on secret sharing, namely dual-image data hiding and multi-image data hiding. Dual-image data hiding is a kind of secret sharing-based data hiding in the extreme case. During the image transmission and storage process, the two shadow images are visually highly similar. Multi-image data hiding disassembles the cover image into multiple meaningless secret images through secret sharing. Both of the above two methods can easily attract attackers' attention and cannot effectively guarantee the security of the secret message. In this paper, through the Shamir threshold scheme for secret sharing, the secret message is disassembled into multiple subsecrets that are embedded in the smooth blocks of multiple different images, by substituting the bitmap of block truncation coding. Thus, the shortcomings of the above two data hiding methods are effectively avoided. The proposed method embeds the secret messages in the compressed images, so it satisfactorily balances the visual quality and the embedding capacity. In our method, the shadow images make sense while they are not visually similar. The compression ratio is four, so the embedding capacity of our method has an obvious advantage under the same storage space.  
data hiding; secret sharing; high embedding capacity; low loss Article
## 3 Results and Discussion
### Taguchi Analysis
#### 3.2.2 Taguchi Confirmatory Runs and Prediction Analysis  
The confirmatory results are shown in Table 9. MPs mixture solutions were made by five MPs, each at concentrations of 0.01 mg/L, 0.1 mg/L and 1 mg/L, respectively, mixed in tap water and had final compound concentrations of 0.05 mg/L, 0.5 mg/L and 5 mg/L, respectively. The optimal operating conditions were also validated by changing aqueous matrix; the five MPs at each 0.1 mg/L were prepared in 0.5 mg/L humic acid solution in tap water to obtain mixture solution with humic acid and MPs at mass ratio of 1:1. The Fe(0) did show relative high DOC removal efficiency of 76 \(\pm\) 0.07%, on the contrary, FeSO\({}_{4}\) only achieved 54 \(\pm\) 0.07% DOC reduction. Hence, Fe(0) proved to possess a better role in Fenton oxidation than FeSO\({}_{4}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Model Summary** & **S** & **R\({}^{2}\)** \\ \hline FeSO\({}_{4}\) & 3.81 & 66.41\% \\ Fe & 0.81 & 98.80\% \\ \hline \end{tabular} S: Standard deviation between data points and fitted values; \(\text{R}^{2}\): Regression value (% of the variation in response data explained by the model).  
\end{table}
Table 7: ANOVA model summary.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model Summary** & **S** & **R\({}^{2}\)** \\ \hline FeSO\({}_{4}\) & 3.81 & 66.41\% \\ Fe & 0.81 & 98.80\% \\ \hline \end{tabular} S: Standard deviation between data points and fitted values; \(\text{R}^{2}\): Regression value (% of the variation in response data explained by the model).  
\end{table}
Table 8: ANOVA analysis of SN ratios.  
Figure 2a,b in the Supplementary Material shows the predicted and actual SN ratio values from the FeSO\({}_{4}\) and Fe(0) Fenton oxidation results. They depict significantly high correlations for Fe(0) (R\({}^{2}\) = 0.996) than FeSO\({}_{4}\) (R\({}^{2}\) = 0.956). The regression coefficient values obtained were acceptable at 95% confidence intervals.
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 2 Materials and Methods
### Preprocessing  
Once we had defined the instance variables, we established the normalization method for the instance. This process was essential to ensuring that all the variables had the same scale and, thus, to facilitating their manipulation by the LSTM network. The normalization method considered was standardization, ensuring that all the variables had a mean of 0 and a standard deviation of 1: we chose this method to evaluate their efficiency in simplifying the training and improving the convergence of the LSTM network. Table 2 shows a fragment of the data instance for product 1, dining room, as an example of the result of the normalization process.  
Moreover, we divided each instance into training, validation, and testing sets. The percentages for producing each set in the experimentation were 60% training, 20% validation, and 20% testing.  
Subsequently, we treated each instance set to generate datasets--an essential step, as this paper considered a single-step multivariate approach (Multivariate-Unstep). Thus, we used these datasets to train, validate, and test the proposed LSTM network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Record** & **Year** & **Sine\_Week** & **Cosine\_Week** & **Event** & **Sales** \\ \hline
3 & 2019 & 0.354605 & 0.935016 & 0 & 32 \\
4 & 2019 & 0.464723 & 0.885456 & 0 & 37 \\
5 & 2019 & 0.568065 & 0.822984 & 1 & 74 \\
6 & 2019 & 0.663123 & 0.748511 & 1 & 65 \\
7 & 2019 & 0.748511 & 0.663123 & 1 & 68 \\
8 & 2019 & 0.822984 & 0.568065 & 0 & 24 \\
9 & 2019 & 0.885456 & 0.464723 & 0 & 46 \\
10 & 2019 & 0.935016 & 0.354605 & 0 & 32 \\
11 & 2019 & 0.970942 & 0.239316 & 0 & 31 \\
12 & 2019 & 0.992709 & 0.120537 & 0 & 38 \\
13 & 2019 & 1 & 0 & 0 & 37 \\
14 & 2019 & 0.992709 & \(-\)0.120537 & 0 & 9 \\
15 & 2019 & 0.970942 & \(-\)0.239316 & 0 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Cyclical representation of the weeks of the year.  
Based on preliminary tests performed to create the datasets, we chose 26 weeks for the input datasets and 1 week for the output datasets. In this sense, we created the datasets for each instance and variable, as shown in Figure 2, for the variable sales.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### Evaluation Metrics  
To evaluate the performance of the proposed deep learning-based COVID-19 detector, we employ several essential evaluation metrics: accuracy, precision, recall (also known as sensitivity), and F-measure.  
Accuracy measures the overall performance of the model across all classes. It is calculated as the ratio of correctly predicted samples (true positives and true negatives) to the total number of samples in the dataset. The accuracy can be computed using Equation (8).  
\[Accuracy=\frac{True_{pos}+True_{neg}}{True_{pos}+True_{neg}+False_{pos}+ False_{neg}} \tag{8}\]  
Figure 4: Example of the Grad-CAM visualization of the affected area.  
Precision is a metric that assesses the percentage of positive predictions that are correct. It quantifies the model's ability to avoid false positives and is calculated using Equation (9).  
\[Precision=\frac{True_{pos}}{True_{pos}+False_{pos}} \tag{9}\]  
Recall (also known as sensitivity) evaluates the model's ability to identify all positive instances, thereby measuring its performance in terms of false negatives. Recall is computed as the ratio of true positives to the sum of true positives and false negatives, as shown in Equation (10).  
\[Recall=\frac{True_{pos}}{True_{pos}+False_{neg}} \tag{10}\]  
F-measure, also known as the F1-score, provides a balanced view of the model's precision and recall. It is the harmonic mean of precision and recall and is given by Equation (11).  
\[F-measure=\frac{2*Precision*Recall}{Precision+Recall} \tag{11}\]  
These metrics are computed based on the values of True Positive (\(True_{pos}\)), False Positive (\(False_{pos}\)), True Negative (\(True_{neg}\)), and False Negative (\(False_{neg}\)) obtained during the evaluation of the model.  
The confusion matrix, as depicted in Figure 5, visually summarizes the performance of the detector model. It shows the count of true positive, false positive, true negative, and false negative predictions, providing insights into the model's strengths and weaknesses.  
The results obtained from the evaluation metrics and the confusion matrix play a crucial role in understanding the performance of the proposed COVID-19 detector. By analyzing these metrics, medical practitioners and researchers can assess the model's accuracy, reliability, and potential for real-world application in COVID-19 detection from chest X-ray images.
## 2 Equations Derivation of the Direct Calculation Method (DCM)  
To distinguish whether the stress at the points \(C\) and \(E\) are in the elastic or the plastic regions, this study presents two analysis situations that include that stress state of points \(C\) and \(E\) are both (1) in the elastic region (Case I, \(\lambda_{d}<\lambda_{s}<\lambda_{e}\)), and (2) in the plastic region (Case II, \(\lambda_{e}<\lambda_{d}<\lambda_{s}\)). In addition, \(\lambda_{e}\), \(\lambda_{d}\), and \(\lambda_{s}\) are the confinement loss in the elastic limit  
Figure 1: Graphical concept of the support-ground interaction in the analysis of convergence-confinement method (CCM).  
state, at the moment of installing support (points C), and at the equilibrium state (points E), respectively. Therefore, \(\lambda_{e}\) and \(\lambda_{d}\) can be given as the following [37; 38; 39]:  
\[\lambda_{e}=\frac{\left(K_{p}-1\right)+2N}{K_{p}+1} \tag{1}\]  
\[\lambda_{d}=1-\left(\frac{m}{m+\left(d/R\right)}\right)^{4} \tag{2}\]  
where \(R\) is the radius of tunnel excavation, \(d\) is the unsupported span, \(K_{p}\) is the coefficient of passive lateral pressure, \(N\) is the stability number, and the parameter of the function \(m\) can be found by the regression analysis with the tunnel convergence data. Note that this confinement loss is not only a function of the property geo-material but also of the unsupported span.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 4 A Comparison of Sparse Signal Recovery Algorithms
### Bayesian Inference Algorithms
In addition, as the BCS algorithm highly depends on the statistical distribution of parameters and more available data at receiver can fit the prior distribution better, the sparseness and the number of pilot symbols are key factors that affect the recovery accuracy. Moreover, the BCS algorithm may break out early when the recovery accuracy reaches the threshold. **Remark 1:** The overall computational complexities of three categories of algorithms are compared in Table 1. For convex relaxation such as IR-based algorithm, its computational complexity will grow squarely with the sparseness increase, since there are gradients of \(\theta_{T}\) and \(\theta_{R}\) that need to be calculated in each iteration [9]. For greedy iteration algorithms, its computational complexity will grow linearly with the sparseness due to the number of iterations depends on the sparseness [25]. With respect to Bayesian inference algorithms, its computational complexity will mainly depend on the amount of available pilot symbols at receiver where more available pilot symbols will let the algorithm break out early [30]. In Table 1, we can see that the computational complexity of all algorithms is quite high when deployed on massive MIMO antennas. The CS-based algorithms proposed in the previous works mainly aim at improving the estimation accuracy rather than reducing the computational complexity. However, reducing the computational complexity without significantly decreasing the accuracy is a direction that is worth studying.