esa-sceva/satcom-chunk-collection huggingface dataset:
# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 2 Related Work
### Recurrent Neural Network Learning by Variables Using GRU Cells  
Most data collected in heterogeneous healthcare platforms have the time series characteristic. Accordingly, an RNN that is suitable for the time series characteristic is learned. To learn an RNN, learning data with the same time step are required. In heterogeneous health, variables are collected in different time steps; therefore, it is difficult to design learning data. If the same variable has redundant data, it is difficult to determine which value should be used as the input for the RNN. In this study, by using GRU cells, the RNN was learned according to variables. Each RNN that was learned according to variables has no need to relearn the neural network, even if the situations of the user device are changed. Among the data collected from 26 persons, the data from 150 days were used for RNN learning, the data from 100 days were used for FCN learning, and the data from 50 days were used for testing. The healthcare proof data were divided into five sets according to the device types: A, B, C, D, and E. Learning was repeated five times with each set. Sleep data consisted of 6500 learning data that were based on the time when the sleep ends, with one time step daily. Sleep data attributes were light sleep (min), deep sleep (min), and roll over in sleep (count). The heart rate was the average heart beats per minute and was measured in the morning, afternoon, and evening, consisting of 19,500 data. The step count data collected were based on the steps measured from the time the individual finished work to the time he/she went to bed. Average temperature, highest temperature, lowest temperature, and relative humidity were recorded at 09:00, 12:00, and 18:00. Data collected on the basis of one day can be assumed to have the same time step; however, the collection time differs. Therefore, it was necessary to consider the time point. As a result, through preprocessing, one input for each variable was selected for every time step and applied to each RNN. According to the attributes, seven input values that had continuous time steps were applied, and the values for three time steps were predicted from the time when the last value was input. In other words, this RNN model used a \(\{t-7,\,t-6,\,t-5,\,t-4,\,t-3,\,t-2,\,t-1\}\) matrix to predict \(\{t+0,\,t+1,\,t+2\}\).  
Figure 3 presents GRU cells-based RNN learning according to variables. For a variable with multiple attributes, a many-to-many model was created, and for a variable with one attribute, a one-to-many model was designed. If the current input step is 1 January 2020, three forward points of time present the prediction data up to 3 January.
## 3 Preprocessing Methods
### Non-Metric Multidimensional Scaling-Based Load Profile Representation  
Daily load profiles consist of hourly readings depending on smart meters sampling time. Different from point outliers such as false smart meter readings, maximum and minimum values of daily load profiles vary constantly between peak hours and it is a more reasonable option to consider 24-hour cycles rather than hourly readings at the stage of outlier analysis. However, clustering-based outlier detection algorithms generate more effective solutions on spatial coordinates and the spatial representation of daily load profiles rather than time-dependent representations can contribute to the reduction in processing times of the algorithms. Unlike classical dimension reduction methods such as Principal Component Analysis (PCA), multidimensional scaling (MDS) makes neitherlinear relationships or modal relationships assumptions. Moreover, MDS allows us to use any distance measure to represent relationships between samples, and the determination of optimal subspace is considered a numerical optimization problem. Figure 1 shows the overall steps for the proposed preprocessing method. At the first stage, the dimension of data sets is transformed as illustrated in Figure 1 and obtained the distance (dissimilarity) matrix using Euclidean distance for each data set \([X_{[K,N]^{\prime}}^{(i)},(i=1,2,3,\cdots,100),N=(24,48,96)]\) as in (1) and (2):  
\[X_{a,b} = \sqrt{(X_{a}-X_{b})(X_{a}-X_{b})^{\prime}} \tag{1}\] \[X_{k,k} = \begin{bmatrix}X_{a,a}&X_{a,b}&\cdots&X_{a,k}\\ X_{b,a}&X_{b,b}&\cdots&X_{b,k}\\ \vdots&\vdots&\ddots&\vdots\\ X_{k,a}&X_{k,b}&\cdots&X_{k,k}\end{bmatrix}  \tag{2}\]  
where \(k\) is number of days available for forecasting stage, \(X_{a,b}\) represents the distance between two different daily load profiles, and \(X_{k,k}\) is a square matrix. After calculation of the distance matrix, the MDS algorithm is implemented in the distance matrix using [PERSON]'s non-metric stress criterion [21], as in (3), and obtains a two-dimensional (2-D) subspace projection \((X_{[K,2]})\). \[\text{Stress}-1 = \sqrt{\frac{\sum[f(p_{a,b})-d_{a,b}(X))]^{2}}{\sum d_{a,b}^{2}( X)}} \tag{3}\]  
Figure 1: Modelling framework of the proposed outlier detection. where \(f:p_{a,b}\to d_{a,b}(X)\) is a mapping function of proximities \((p_{a,b})\) until the condition \(f(p_{a,b})\approx d_{a,b}(X)\) is met. The distance matrix \(d_{a,b}(X)\) represents the pairwise relationships between objects in a data set. To explain this approach more intuitively, it can be thought of as using the distances between cities instead of using latitude and longitude information to determine the locations of cities on a map. The stress parameter determines how well-suited these inter-point distances of the output approximate to the original distance matrix. Smaller stress values indicate a better subspace projection. Lastly, the outlier detection process, as illustrated in the pseudo-code of Algorithm 1, is implemented and daily load profiles are labeled as outliers are removed from the data sets.
## 4 Non-Coaxial Image Motion Compensation Method
### Simplified Image Motion Compensation Method  
Considering that the roll rotation angle \(\Delta\phi\) during camera exposure is small, the corresponding compensation angle \(2\kappa\) is also small. Thus, a small-angle approximation can be applied for simplification:  
\[\begin{split}\sin(\Delta\phi)&\approx\Delta\phi, \cos(\Delta\phi)\approx 1\\ \sin(2\kappa)&\approx 2\kappa,\cos(2\kappa)\approx 1 \\ sin(\phi_{start}+\Delta\phi)&\approx\sin\phi_{ start}+\Delta\phi\cos\phi_{start}\\ cos(\phi_{start}+\Delta\phi)&\approx\cos\phi_{ start}-\Delta\phi\sin\phi_{start}\end{split} \tag{19}\]  
Substituting the approximate results of Equation (19) into Equations (13) and (14) yields the following expressions:  
\[\begin{split}\sin\theta&\approx\sin\theta_{start}\\ (\sin\phi_{start}+\Delta\phi\cos\phi_{start})\cos\theta\cos(2 \kappa)+(\cos\phi_{start}-\Delta\phi\sin\phi_{start})sin(2\kappa)\approx\cos \theta_{start}\sin\phi_{start}\end{split} \tag{20}\]  
Neglecting the second-order term \(\Delta\phi sin\phi_{start}sin(2\kappa)\) and solving, the approximate solution is as follows:  
\[\begin{split}\theta&=\theta_{start}\\ 2\kappa&=-\Delta\phi\cos\theta_{start}\end{split} \tag{21}\]  
At this point, the simplified approximate solution has been obtained. Compared to Equation (18), it offers significant computational advantages. However, due to the approximation-induced errors, further simulation analysis is required to verify the compensation accuracy of the simplified approach.
# Design of Road-Side Barriers to Mitigate Air Pollution near Roads
## 2 Methodology
### Validation with Experimental Results  
In order to validate the CFD-based model for the dispersion of pollutants near roads (NR-CFD model), the results obtained from this model were compared with the experimental data obtained in the Near Road Tracer Study (NRTS08), with and without a 6 m tall barrier. Said study was conducted in 2008 by the NOAA, Idaho National Laboratory (INL), Air Resources Laboratory (ARL), and Environmental Protection Agency (EPA) in Idaho Falls, USA [7] and reported by [PERSON] et al. [5] and [PERSON] et al. [14]. Figure 3 illustrates the experimental setup implemented. They released a 1 g s\({}^{-1}\) constant flow of a tracer gas (SF\({}_{6}\)) distributed uniformly along a 54 m line and used an array of bag samplers to measure the 15 min average SF\({}_{6}\) downwind concentrations at 3, 4, 6, 8, 11,15, 20, and 30 times the road width. They adopted an equivalent road width of 6 m in their experiments. Anemometers were placed at several vertical positions to measure wind speed, wind direction, turbulence characteristics, friction velocity, heat flux, and atmospheric stability. The tests were repeated under different atmospheric conditions when the wind flowed nearly perpendicular to the line emission source. Each test lasted around 3 h, resulting in a total of 10-12 measurements for each position considered.  
Even though their interest was the study of the effects of a side road 6 m high straight barrier on the dispersion of pollutants, they also carried out several tests without any barrier to the wind free flow under different atmospheric stability conditions. Time and test average values of both sets of data that were taken in neutral atmospheric conditions  
Figure 3: Illustration of the experimental setup implemented during the Near Road Tracer Study (NTRS08) campaign in Idaho Falls, USA. In this study, results obtained with and without a 6 m straight barrier were used.  
and over the centerline of the crosswind direction downwind the road (see dotted line in front of the line source in Figure 3) were used for a comparative analysis, as it was the line with the highest measured concentration values. A Schmidt number for SF\({}_{6}\) of 0.207 was considered to express their experimental results in terms of C* (Equation (3)).
## 5 Economic Impact of the Integration of SCP Practices
### Increased Income  
MSMEs reported that they experienced an increase in sales and profits after adopting the SCP practices. Due to an improvement in quality and quantity of yields and a reduction in post-harvest losses, MSMEs were able to significantly increase their horticultural income. Table 6 show a correlation between being project beneficiaries that integrated SCP practices and the change of income.  
In this study, the MSMEs surveyed had vegetable production as the main source of income. Their crops included potatoes, tomatoes, cabbage, carrots, lettuce, kale, spinach, and capsicum. During the semi-structured interviews, the MSMEs reported an income increase that ranged between 150% to 400%, with the highest increase stated by farmers who had installed greenhouses and water harvesting and storage systems (such as water tanks and water pans) in their farms. The farmers reported that using greenhouses meant that they did not have to rely on the seasonal rains that were becoming more and more unpredictable and that this led to an increase in yields as a result of them being able to cultivate crops all year round. Moreover, the MSMEs that used certified potato seeds reported an increase in sale prices that ranged from Ksh 500-Ksh 1500 per sack to Ksh 2500-3500 per sack due to an increase in the quality of the potatoes. Other MSMEs involved in tomato production reported an increase of price from Ksh 20 per kg to Ksh 80 per kg also as a result of an increase in the quality of the tomatoes that enabled them to access alternative markets that offer higher prices. The improved quality of products allowed farmers to access new markets and customers. Farmers reported they were able to sell to hotels located at the Naivasha Lake, which offer higher prices as well as a stable demand. As mentioned before, farmers were also able to obtain supply contracts with bulk and reliable buyers such as processing companies (Kinangrop Fries and Cremtop Foods) and supermarkets.  
In the semi-structured interviews, the MSMEs reported that the increased income resulting from the implementation of SCP practices have positive impacts on their daily  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Change in Income** & & & & & \\
**between 2017 and 2019** & & & & & \\ \hline All respondents & 2165.63 & 0 & 157,967.3 & \(-\)22,250 & 29,750 \\ \hline Beneficiaries & 28,389.47 & 5000 & 197,691.7 & \(-\)20,000 & 57,500 \\ \hline Non-Beneficiaries & \(-\)24,058.2 & \(-\)3800 & 98,753.48 & \(-\)24,000 & 21,500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MSMEs’ adoption of SCP practices and change in income.  
lives, which included school enrolment and fees, improved living conditions of their household, increased number of livestock, acquisition of household equipment, employing of additional farm workers, and paying higher salaries. Furthermore, having an increased disposable income led to new business ideas such as adding value to their produce. For example, selling tomato paste and French fries to avoid harvest loss when suboptimal or imperfect vegetables cannot be sold to the markets.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 3 CNN-NLP Architecture Design for LSB Steganography Detection
### Message Classification and Extraction  
After classification, if an image is identified as containing steganography, the hidden message is extracted from the LSBs of each pixel. The message is reconstructed by converting binary to text via ASCII encoding using Equation (12), where \(b_{k}\) denotes each bit sequentially extracted from the image:  
\[Message=\sum_{k=1}^{n}2^{(7-k)}\cdot b_{k},\ \forall b_{k}\in\{0,1\} \tag{12}\]
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 4 Performance Evaluation and Discussion
### BER Improvement by MLSE Demodulation  
To confirm the improvement in communication characteristics via the proposed MLSE method, the case of demodulation by the threshold value and the case of demodulation by the MLSE method were compared. The number of received molecules used in the BER simulation was calculated using Equation (8) by assuming that all channel parameters were known. Figure 11 shows the BER characteristics of the demodulation by the threshold and MLSE methods when the oversampling coefficients were varied as \(s_{L}=5,15,\) and \(40\). The criterion for reliable communication was defined as a BER of \(10^{-3}\). It can be shown that the demodulation by the MLSE method improves the communication characteristics when compared with demodulation by the threshold value. In particular, it is improved by \(s_{L}=40\).
## 3 Proposed Methodology
### Data Sanitation and Preprocessing  
The data sanitation process removes Arabic stop words, punctuation, and extra spaces from the text corpus. Text normalization is not performed as it might alter the meaning of some words, such as \"ball\". After performing data sanitation on the text corpus, the data preprocessing step is carried out to convert the input text sequence to the embedding vectors, which are obtained after merging the sub-word tokenization, segment embedding, and positional embedding into the input sequence. Special tokens [CLS] are added at the beginning of the input sequence, and the [SEP] token is used to separate a long sentence into a meaningful sentence. The input sequences are tokenized with a word-piece tokenizer. The word-piece tokenizer is based on the sub-word tokenization algorithm. Sub-word tokenization techniques are based on the idea that commonly employed phrases must be distinct from shorter sub-words. However, unique words should be deconstructed into meaningful sub-words when tokenizing them. After tokenization and using a word-piece tokenizer, the input sequences are represented numerically using token embedding. The BERT model discriminates between various phrases in a single input segment. Using this embedding vector, the values of each word in a sentence are the same, but the values vary when the phrase is changed. The positional embedding vectors are used to calculate the location of each word or the distances between distinct phrases in the sequences.  
As presented in Algorithm 1, the tokenization algorithm is based on filling the vocabulary \(V_{i}\) with the input text characters. Even if the vocabulary \(V_{i}\) is less than the ideal vocabulary \(IV\), the frequency of each pair of text is calculated, and then the most frequent pair of text \(\big{(}x_{i},y_{j}\big{)}\) is determined. A new unused pair of text is added to expand the vocabulary. The input text characters are divided into word segments \(WS_{i}\) then the process is repeated again for each text entry until the entire text document is finished.  
```
Let \(IV\)\(\leftarrow\) Ideal Vocabulary  Let \(V_{i}\)\(\leftarrow\) Applied Vocabulary  Let \(TC\)\(\leftarrow\) Text Character  Let \(WS\)\(\leftarrow\) Word Segment  Step 1: \(\forall\ TC_{i}\)\(\in\)\(V_{i}\) such that \(V_{i}\) = { \(TC_{1}\), \(TC_{2}\),..., \(TC_{n}\)}  Step 2: \(IF\)\(V_{i}\)\(\leq\)\(IV\)\(THEN\)  Count \(freq\)\(\big{(}x_{i},y_{j}\big{)}\)  Find \(Max\)\(freq\)\(\big{(}x_{i},y_{j}\big{)}\)  Substitute \(\big{(}x_{i},y_{j}\big{)}\) with new \((x_{k},y_{i})\)  Add \((x_{k},y_{i})\) to \(V_{i}\)  Step 3:  Divide \(TC_{i}\) such that \(TC_{1}=\{WS_{1}\), \(WS_{2}\),..., \(WS_{n}\)}  Step 4:  Repeat Step two & Step three  Step 5:  Apply \(V_{i}\)\(=\)\(\{WS_{1}\), \(WS_{2}\),..., \(WS_{n}\)}  Step 6:  Train \(V_{i}\)
```  
**Algorithm 1**Tokenization Process
## 3 Technical Principles, Methodology, Sampling, and Analysis
### Technical Principles
The fundamental principle of this methodology lies in the ubiquitous presence of mercury anomalies surrounding metallic ore deposits, where mercury vapor in soils originates from concealed mineralization bodies [31; 32]. Mercury in hydrothermal systems predominantly occurs as native mercury or mercury compounds within sulfide minerals or migrates along fractures to form primary halos. Mercury vapor ascending through groundwater systems or structural discontinuities becomes adsorbed by soil to form geochemical anomalies. However, these anomalies are frequently associated with tectonic activities rather than directly indicating mineralization bodies, and they are susceptible to anthropogenic interference from vegetation cover or agricultural activities. Consequently, the integration of stepwise thermal release protocols becomes imperative for distinguishing genuine anomalies, thereby effectively delineating concealed ore bodies and fault zones. This methodology demonstrates broader applicability beyond mineral exploration, extending to hydrogeological investigations and engineering geological assessments involving structural fracture systems [33; 34].
# Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data
## 4 Input Normality Measurement
### Sub-Model  
We define the sub-model according to the general classifier structure, which consists of an input layer, several hidden layers, and an output layer. Given a classifier with \(N\) hidden layers, we can construct up to \(N\) sub-models for it. Specifically, the \(i\)th sub-model should include the input layer, the first \(i\) hidden layers of the original classifier, and a newly added output layer, as in Figure 3. The output layer of a sub-model has the same structure as the original classifier, but we need to train the output layer separately for each sub-model. Thus, its weights are different from those of the original classifier.  
Each sub-model is an independent classifier with fewer hidden layers. Its outputs thus reflect the intermediate classification results of the original classifier. A sub-model takes the \(i\)th hidden layer of the original classifier as input and outputs classification results in the same form as the original classifier.  
When training a sub-model, we only need to update its output layer, as it has the same input and hidden layers as the original classifier. We use the training set of the original classifier to train a sub-model. Let \((x,y)\) be a sample and \(f_{i}(x)\) be the value of the \(i\)th hidden layer. We can take \((f_{i}(x),y)\) as a training sample of the \(i\)th sub-model. Therefore, each sub-model has the same number of training samples as the original classifier. Training sub-models can be efficient, as we only need to update a single output layer for each sub-model.  
We propose two hypotheses about sub-models based on our understanding of neural networks. First, a classifier always has many hidden layers to improve its accuracy. Therefore, we think the outputs of sub-models will fluctuate randomly over the first few hidden layers and gradually converge to the same result as the original classifier. Second, classifying abnormal samples is more difficult. Their classification results fluctuate more significantly over multiple classes in the sub-model and they need to go through more hidden layers to reach a stable classification result.  
According to the considerations, we give two hypotheses:  
**Hypothesis 1 (H1)**.: _For both normal and abnormal samples, the closer the sub-models are to the input layer, the more significant the fluctuations in their classification results._  
**Hypothesis 2 (H2)**.: _Abnormal samples need to go through more hidden layers before their sub-models converge to the same classification results as the original classifier._
# Local Convergence Study for an Iterative Scheme with a High Order of Convergence
## 3 Efficiency Index \((EI)\) and Computational Efficiency Index \((CeI)\)
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(LS\)} & \multicolumn{3}{c}{\(CEI=p^{\frac{1}{2\mu+9}}\)} \\ \cline{2-10}  & \(\mathcal{T}^{\prime}\) & **Other** & & & \(\mu=\frac{1}{2}\) & \(\mu=\frac{3}{4}\) & \(\mu=1\) & \(\mu=\frac{3}{2}\) \\ \hline \(M1_{(a=0)}\) & 1 & 2 & 1 & \(\frac{2N^{3}}{3}+4N^{2}-\frac{2N}{3}\) & \(4\frac{6}{4N^{3}+3N^{2}-N}\) & \(4\frac{12}{4N^{3}+48N^{2}+N}\) & \(4\frac{3}{4N^{3}+48N^{2}+5N}\) & \(4\frac{6}{4N^{3}+48N^{2}+5N}\) \\ \(M1_{(a=\frac{a}{3})}\) & 2 & 1 & 2 & \(\frac{2N^{3}}{3}+5N^{2}-\frac{2N}{3}\) & \(4\frac{6}{4N^{3}+3N^{2}-N}\) & \(4\frac{12}{4N^{3}+2N^{2}+N}\) & \(4\frac{2N^{3}+2N^{2}+N}{3}\) & \(4\frac{6}{4N^{3}+48N^{2}+5N}\) \\ Jaiswal & 3 & 1 & 3 & \(\frac{2N^{3}}{3}+7N^{2}-\frac{2N}{3}\) & \(4\frac{6}{4N^{3}+3N^{2}-N}\) & \(4\frac{12}{4N^{3}+48N^{2}+4N}\) & \(4\frac{2N^{3}+2N^{2}+N}{3}\) & \(4\frac{12}{4N^{3}+2N^{2}+N}\) & \(4\frac{12}{4N^{3}+6N^{2}+5N}\) \\ CHJ & 3 & 1 & 2 & \(\frac{2N^{3}}{3}+6N^{2}-\frac{2N}{3}\) & \(4\frac{6}{4N^{3}+3N^{2}-N}\) & \(4\frac{8N^{3}+6N^{3}+N}{3N^{2}+5N}\) & \(4\frac{2N^{3}+2N^{3}+5N}{3N^{2}+5N}\) & \(4\frac{4N^{3}+5N^{2}+5N}{3N^{2}+5N}\) \\ [PERSON] & 2 & 1 & 2 & \(\frac{2N^{3}}{3}+5N^{2}-\frac{2N}{3}\) & \(4\frac{6}{4N^{3}+3N^{2}-N}\) & \(4\frac{12}{4N^{3}+2N^{2}+N}\) & \(4\frac{3}{4N^{3}+2N^{3}+5N}\) & \(4\frac{6}{4N^{3}+48N^{3}+5N}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational Efficiency Index \((CEI)\). Since method (1) depends on parameter \(\alpha\), we consider \(\alpha=0\) and \(\alpha=\frac{9}{8}\), values which eliminate terms of the iterative expression, reducing the cost of solving the linear systems and the added computational effort. Also, let us consider \(\mu=\frac{1}{2},\ \frac{3}{4},\ 1,\ \frac{3}{2}\). All schemes have the same number of functional evaluations of \(\mathcal{T}\) and \(\mathcal{T}^{\prime}\) per iteration, and hence \(d=2N^{2}+N\). With the results obtained from Table 1, we represent in Figure 1 the performance of the efficiency index for the examined schemes. Figure 1 shows that \(M1\) for \(\alpha=0\) has better behavior than all the other methods analyzed. Furthermore, [PERSON] and \(M1\) for \(\alpha=\frac{9}{8}\) are second in efficiency. It is also shown that as \(\mu\) increases, the Computational Efficiency decreases for all schemes. In the next section, some examples are worked out in order to perform numerical tests.