esa-sceva/satcom-chunk-collection huggingface dataset:
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation  
In this section, we evaluate LESS in two real intertidal WSN systems and with a large-scale trace-driven simulation. We first compare the performance of LESS with standard interpolation approaches. Then we investigate the reconstruction accuracy of link quality and topology in real systems. We also analyze the energy consumption of LESS with different sampling rates.
## 2 Study Area and Past Monitoring Analysis
### Structural and Geomechanical Characterization  
Several studies were made on Madonna del Sasso site from the early 1990s when some evidence of instability appeared and the first countermeasures, like the rockfall wall, were realized.  
[PERSON] et al. [18; 22] made a geomechanical characterization of the site. The detailed investigation carried out also using geophysical methods by [PERSON] et al. [20; 21; 27] allowed a good improvement of the knowledge of this case. In particular, [PERSON] et al. [21] identified an open and pervasive fractures system delimiting two prone-to-fall sectors (A and B) having estimated volumes of 6000 and 6300 m\({}^{3}\), respectively (Figure 3). The two sectors are separated by the sub-vertical fracture K2 (355 dip direction/80 dip) having an approximate open depth of 15 m and are truncated at the base by the low-dipping fracture K3 (153/15). Fractures K1 (113/65) and K4 (52/80) (Figure 3B) separate the back of the two compartments from the stable cliff. In particular, fracture K4 shows an evident displacement step on the panoramic square located at the top of the cliff. The investigation found that the fracture is open up to a depth of about 16 m [21].  
The studies of Colombero also allowed the identification of quite low seismic velocities in the whole unstable sector that suggest the probable widespread presence of dry cracks and secondary fractures [20].  
Figure 2: (**A**) shows an aerial image of the Sanctuary site in which are visible: (i) the location Sanctuary and the courtyard (ii) the ancient quarry deposits, (iii) a rockfall wall designed to protect the elements at risk; (**B**) and (**C**) report the view of the Sanctuary and of the unstable slope from North and South, respectively. The picture in panel (**D**) shows a 3D view from the South of the unstable cliff.
## 3 Results
### MS-bp and WBR-bp Comparison  
An overall comparison between the environmental performance shown by the two analyzed alternatives for glucose production is summarized in Figure 9. Also, values of ReCiPe mid-point indicators are detailed in Tables S3 and S4 of the Supplementary Information. Results indicate that the WBR based-process involved an improvement in the six environmental impacts under study. The categories showing the largest differences in the environmental impacts caused by both processes were human toxicity (HT), particle matter formation (PMF), and terrestrial acidification (TA), showing with a relative contribution below 30% for the WBR-bp case as compared to the MS-bp option.  
Figure 8: The relative contribution of process stages and inputs to the evaluated impact categories in LCA results obtained for the conditioning stage in glucose production from woody biomass residues. (**a**) Impact aggregation by sub-process; (**b**) impact aggregation by input.  
Figure 9: Relative contribution to the studied environmental categories of MS and WBR based processes.  
On the other hand, fossil depletion (FD) and ozone depletion (OD) categories present the lowest relative gap between the two options, being calculated a 39% for the woody biomass system versus 61% for the starch case.  
Regarding climate change (CC), a promising reduction of carbon dioxide emissions was achieved by using woody biomass residues as the glucose source instead of maize starch: 0.8 kg CO\({}_{2}\)-eq/kg of glucose versus 1.76 kg CO\({}_{2}\)-eq/kg (see Tables S3 and S4); this evidences the enormous potential of glucose production from lignocellulosic woody biomass as the most reliable option for decreasing CO\({}_{2}\) emissions.
## 3 Research Methodology
### Classification Algorithms
#### 3.5.3 Linear Regression  
One of the most common algorithms for performing regression tasks is Linear Regression. This type of algorithm assumes that the input and output variables have a linear relationship. It can be performed by fitting a line or hypervariable plane to minimize errors between the predicted and actual values [28; 29; 30; 31; 32; 33].  
Although Linear Regression is straightforward to implement and use, its performance can be limited by the relationship between the variables' underlying values. It can also be sensitive to outliers. Despite these limitations and disadvantages, it is still widely used due to its effectiveness and simplicity in performing tasks where a linear relationship exists [28; 29; 30; 31; 32; 33].  
Linear Regression is a type of statistical model that shows the relationship between a given variable and another variable. It uses a linear equation to arrive at its formula.  
\[y=\beta_{0}+\beta_{1}x+\varepsilon \tag{7}\]  
The same formula for multiple independent variables, or multiple Linear Regressions, is performed as follows:  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n}+\varepsilon \tag{8}\]  
where \(\beta_{0}\) is the constant, \(\beta_{1}\), \(\beta_{2}\), \(\beta_{n}\) are the coefficients for each independent variable, \(x_{1}\), \(x_{2}\), \(x_{n}\) are the independent variables, \(y\) is the dependent variable, and \(\varepsilon\) is the error term, accounting for the difference between the actual and predicted values [28; 29; 30; 31; 32; 33].  
Linear Regression aims to minimize the sum of squared errors, which are calculated using the following formula:  
\[SSE=\sum_{i=1}^{n}\ (y_{1}-\beta_{1})^{2} \tag{9}\]  
where \(y_{1}\) is the actual value, and \(\beta_{1}\) is the predicted value [28; 29; 30; 31; 32; 33].
## 4 Experiment
### Performance Measurements  
The mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and average absolute position error (APE) were chosen as measurements to compare the performance of each method between the predicted track and the real track. MAE is the average of the absolute values of the deviations between all predicted values and actual values, calculated by Equation (10). Larger error represents worse model performance.  
\[MAE=\frac{\sum_{i=1}^{n}|P_{i}-O_{i}|}{n} \tag{10}\]  
Figure 5: The loss of AE-GRU. (**a**) The train/validation loss of latitude. (**b**) The train/validation loss of longitude.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Value** \\ \hline AE-GRU & Training method (for the prediction model) & Adaptive moment estimation (Adam) \\  & Number of AE layers & 3 \\  & Number of hidden layer nodes of AE & 2 \\  & Number of GRU layers & 3 \\  & Number of each GRU nodes & 100 \\  & Learning rate & 0.001 \\  & Batch size & 64 \\  & Loss function & Mean squared error (MSE) \\ \hline GRU [10] & Number of GRU layers & 3 \\  & Number of each GRU’s nodes & 100 \\ \hline LSTM [24] & Number of LSTM layers & 3 \\  & Number of each LSTM’s nodes & 100 \\ \hline RNN [16] & Number of RNN layers & 3 \\  & Number of each RNN’s nodes & 100 \\ \hline AE-RNN [44] & Number of AE layers & 3 \\  & Number of hidden layer nodes of AE & 2 \\  & Number of RNN layers & 3 \\  & Number of each RNN nodes & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training parameters of each deep learning model.  
where \(n\) is the number of records in the testing set, \(P_{i}\) is the predicted value, and \(O_{i}\) is the actual value.  
RMSE is the square root of the deviation between the predicted value and the true value and the ratio of the number of observations. The mathematical formula is below.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(P_{i}-O_{i}\right)^{2}}{n}} \tag{11}\]  
MAPE represents the average error rate for actual values. It takes into account the error between the predicted track and the real track and the ratio of the error and the real track. The calculation is given in Equation (12). When the MAPE value is 0, indicating a perfect model, while when the value is greater than 1, this indicates a poor model.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{P_{i}-O_{i}}{O_{i}}\right| \tag{12}\]  
APE is calculated through Equation (13), where \(R\) is the Earth's radius, \((Lat_{\text{real}},Lon_{\text{real}})\) indicates the latitude and longitude of the actual tropical cyclone location, and \(\left(Lat_{pred},Lon_{pred}\right)\) is the predicted location. The APE can measure the distance between the real location and the predicted location in kilometers. A smaller value of APE indicates a better model.  
\[\begin{split} APE=R\times Arccos\left(\sin\left(Lat_{real} \right)\right.&\times\sin\left(Lat_{pred}\right)\times\cos \left(Lon_{real}-Lon_{pred}\right)\\ &+\cos\left(Lat_{real}\right)\times\cos\left(Lat_{pred}\right) \right)\times\pi/180\end{split} \tag{13}\]
# Numerical Heat Transfer Simulation of Oil Shale Large-Size Downhole Heater
###### Abstract  
Downhole heaters are critical for effectively achieving in situ oil shale cracking. In this study, we simulate the heat transfer performance of a large-scale helical baffle downhole heater under various operational conditions. The findings indicate that at 160 m\({}^{3}\)/h and 6 kW the outlet temperature can reach 280 \({}^{\circ}\)C. Controlling heating power or increasing the injected gas flow effectively mitigates heat accumulation on the heating rod's surface. The outlet temperature curve exhibits two phases. Simultaneously, a balance in energy exchange between the injected gas and heating power occurs, mitigating high-temperature hotspots. Consequently, the outlet temperature cannot attain the theoretical maximum temperature, referred to as the actual maximum temperature. Employing \(h/\Delta p^{3}\) as the indicator to evaluate heat transfer performance, optimal performance occurs at 100 m\({}^{3}\)/h. Heat transfer performance at 200 m\({}^{3}\)/h is significantly impacted by heating power, with the former being approximately 6% superior to the latter. Additionally, heat transfer performance is most stable below 160 m\({}^{3}\)/h. The gas heating process is categorized into three stages based on temperature distribution characteristics within the heater: rapid warming, stable warming, and excessive heating. The simulation findings suggest that the large-size heater can inject a higher flow rate of heat-carrying gas into the subsurface, enabling efficient oil shale in situ cracking.  
oil shale; in situ conversion; downhole heater; numerical simulation +
Footnote †: journal: Applied Sciences
## 3 Model Training, Optimization and Explainability
### SHAP for ML Explainability  
As discussed in the Introduction, feature importance is calculated globally and does not provide a local explanation of how every single prediction is made by the model. Therefore, SHAP value is adopted in this study for local explanation. Being an additive explanation approach, SHAP value fairly attributes the final prediction at a single sample point to each of the participating features [19]:  
\[g\big{(}z^{\prime}\big{)}=\phi_{0}+\sum_{i=1}^{M}\phi_{i}z_{i}^{\prime} \tag{1}\]  
where \(g(z^{\prime})\) is a pointwise model prediction, \(M\) is total number of features, \(z_{i}^{\prime}\in\{0,1\}\) represents the feature being missing (\(z_{i}^{\prime}=0\)) or not (\(z_{i}^{\prime}=1\)), and \(\phi_{i}\) is the SHAP value of  
Figure 4: Multi-SiteBoost (MSB) flow chart.  
feature \(i\). Referring to [19], the SHAP value can be calculated as follows, which ensures a fair contribution of feature effects among all features and a close approximation to local prediction value:  
\[\phi_{i}=\sum_{S\subseteq M\backslash\{i\}}\frac{|S|!(M-|S|-1)!}{M!}[f_{x}(S \cup\{i\})-f_{x}(S)] \tag{2}\]  
where \(S\) is a subset of features, \(M\{i\}\) the set of all features, \(f_{x}(S\cup\{i\})\) is the model prediction with all features in \(S\) and the feature \(i\), \(f_{x}(S)\) the prediction with features in \(S\) only. SHAP value method is further extended by [32] by decomposing SHAP value of each feature into a main effect and interaction effects, so that:  
\[\phi_{i}=\phi_{i,i}+\sum_{j\
eq i}\phi_{i,j} \tag{3}\]  
where \(\phi_{i,j}\) is the main effect of feature \(i\), and \(\phi_{i,j}\) is the SHAP interaction value of a feature pair \((i,j),j\) is a feature different from \(i\). Each SHAP interaction value \(\phi_{i,j}\) is calculated by:  
\[\phi_{i,j}=\phi_{j,i}=\sum_{S\subseteq M\backslash\{i,\,j\}}\frac{|S|!(M-|S|- 2)!}{2(M-1)!}\
abla_{ij}(S) \tag{4}\]  
\[\
abla_{ij}(S)=f_{x}(S\cup\{i,j\})-f_{x}(S\cup\{j\})-f_{x}(S\cup\{i\})+f_{x}(S) \tag{5}\]
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 3 Results
### Data Preprocessing  
Because the neural network is very sensitive to data distribution, we first optimized the input energy consumption data before training the model. We used the data preprocessing strategy to remove outliers and missing values, and standardized the input data. We evaluated the model on AEP and IHEPC datasets. On the AEP dataset, we standardized the energy consumption data to limit the data distribution to a specific range. The mathematical expression of the standardized transformation operation is as follows:  
\[Y=\frac{X-\mu}{\sigma}, \tag{8}\]  
where \(X\) represents the actual input data, \(\mu\) is the mean value, and \(\sigma\) is the standard deviation. Each data characteristic value is converted through the min-max scaling operation. The mathematical expression of the min-max scaling operation is as follows:  
\[Y=\frac{X-X_{min}}{X_{max}-X_{min}}, \tag{9}\]  
where \(X_{min}\) and \(X_{max}\) are the minimum and maximum values of the features in the dataset respectively. The input of a deep learning network requires a specific size, so the original training and test sequences are processed using sliding. As shown in Figure 4a, the overlapping sliding method is used to increase the number of training samples. Assuming the length of the training sequence is \(z\), a sliding window with a length of \(n\) and a step size of \(u\) (\(u<n\)) is used to slide along the original sequence and obtain \(\frac{z-n}{n}+1\) training samples. Similarly, as shown in Figure 4b, the non-overlapping sliding mode is used for sampling. Assuming the length of the test sequence is \(h\), \(\frac{h}{n}\) test samples are obtained. Here, \(m\) represents the start of sliding, \(n\) represents the size of the sliding window (i.e., the dimension of the network input), and \(m:m+n-1\) represents the start of sliding to the end of the sequence.  
Figure 4: Schematic diagram of sliding input. (**a**) Overlap sliding; (**b**) Non-overlapping sliding.  
In this paper, \(MSE\), \(MAE\), \(RMSE\), and \(MAPE\) indicators are used for performance evaluation. The mathematical expressions of the three indicators are as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i}), \tag{10}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}|, \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}, \tag{12}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}|\frac{y_{i}-\hat{y}_{i}}{y_{i}}|, \tag{13}\]  
where \(y_{i}\) is the label data and \(\hat{y}_{i}\) is the predicted data. The model was trained by GeForce RTX 2080 Ti GPU, equipped with an Intel i5-processor, 64 GB-RAM, and the Ubuntu-system. The model was built based on the Python framework, and the Adam optimizer was used for gradient update.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology  
### Research Framework  
Detecting damage using conventional methods is challenging due to the complexity of the responses. The 1-D CNN approach can be practical to overcome this challenge. The 1-D CNN method trained using vibration data (i.e., time-history and SVD responses) is applied to automate the feature extraction process and to facilitate real-time monitoring.  
Figure 1 illustrates the CNN deep learning model scheme for caisson-foundation system using vibration data. This scheme comprises three phases: (1) data acquisition via the vibration technique, (2) construction of the 1-D CNN model for the caisson-foundation system, and (3) evaluation of the 1-D CNN model. In Phase 1, the databanks corresponding to foundation damage on the caisson system are generated for deep learning models. The foundation damage simulation involves removing a certain amount of sand and gravel from the existing foundation. The vibration data (i.e., time-history and SVD responses) are acquired by accelerometers under man-made excitation. The time-history responses obtained from several accelerometers are concatenated to form one continuous response. The SVD responses are derived from the time-history responses using the frequency domain decomposition (FDD) method. The vibration data are processed with data augmentation to form a databank and used for the CNN model in the subsequent phases.  
In Phase 2, a 1-D CNN model is developed to detect damage in the caisson-foundation system. The databank is augmented by introducing noise to the vibration. The 1-D CNN model processes the vibration data through several hidden layers to classify the damage levels in the foundation. In Phase 3, the effectiveness of the 1-D CNN model is evaluated for noise-contaminated and untrained damage cases.
# Green Extraction of Valuable Compounds from Rubber Seed Trees: A Path to Sustainability
## 7 Green Extraction of Rubber Seed Oil
### Microwave Assisted Extraction (MAE)
#### 7.2.2 Current Extraction on Rubber Seed Trees by MAE
The rubber tree is primarily cultivated for its latex, which holds significant economic importance described by [PERSON], [PERSON] and [PERSON] [89]. However, an often-underutilized byproduct of this tree is its rubber seeds, which have not found major applications. Even the natural production of these seeds remains underutilized. In this study, researchers explore the use of microwave-assisted Soxhlet extraction as an environmentally friendly alternative to extract oil from rubber seeds. The extraction is carried out with a reaction time of 90 min and a microwave power of 300 W. The primary objective of this study is to evaluate the effects of various processing conditions, including drying time, temperature, solid-solvent ratio, and the choice of extraction solvent, on the yield of rubber seed oil. Additionally, the study examines microwave-assisted aqueous extraction (MAAE) under acidic conditions. The results indicate that n-hexane is the most effective solvent for extracting rubber seed oil, providing the highest yield when using an optimized seed-hexane ratio of 1:20 at a temperature of 72 \({}^{\circ}\)C. This yield is compared with the traditional Soxhlet extraction method and the acidic MAAE. Furthermore, the chemical characteristics of the extracted rubber seed oil reveal certain key properties. The oil exhibits a relatively high percentage of free fatty acids (% FFA) ranging from 1.15% to 7.61%. Additionally, the oil's iodine value (IV) ranges from 100 to 150, indicating that it is a semi-drying oil. This characteristic makes rubber seed oil suitable as an ingredient in surface coatings and in the formulation of products where the presence of unsaturation is essential. In summary, this study explores the potential of using microwave-assisted Soxhlet extraction to extract oil from rubber seeds, an underutilized byproduct of rubber tree cultivation. The choice of n-hexane as the solvent under optimized conditions results in highest oil yield. The chemical characteristics of the extracted oil, including its semi-drying nature, make it suitable for various industrial applications, particularly in products requiring the presence of unsaturated oils, such as surface coatings. This research highlights the potential value of rubber seed oil and offers an eco-friendly extraction method for its utilization. In broader terms, MAE offers advantages such as rapid extraction, minimal solvent use, and maintained compound integrity. It proves to be effective in extracting various compounds, including phenolics, antioxidants, and bioactive molecules from diverse sources like rubber seed trees. MAE is an evolving technique with a substantial potential impact on various industries, including food, pharmaceuticals, and cosmetics. As researchers continue to refine parameters and optimize conditions, MAE's utility in extracting bioactive compounds from natural materials remains promising.
# Interconnected Government Services: An Approach toward Smart Government
## 5 Discussion  
The proposed model aims to improve the efficiency of interconnected government services. To validate the proposed model, we have used a child-benefits procedure as a case study, which reflects inefficiency and points to problems in the work of public administration. In many countries, collecting and submitting documents is relegated to citizens. For example, on average, there are some 550 applications for child benefits daily in the Republic of Serbia, or 16,500 applications monthly, including some 12% or 2000 new applicants [7]. Each new user needs at least three days to prepare the certificates, with significant expenses for both applicants and the government (taxes, paper consumption, government labor force, and time waste). The citizens will spend between 7 and 15 days acquiring all of the necessary documents.  
This activity occurs during work hours, meaning the citizen must be absent from his/her workplace. According to available data sets of statistical indicators published by the Statistical Office of the Republic of Serbia, Serbia's average hourly labor cost is 3.92 EUR [39]. This means that only the costs of absence from work for eight working hours amount to 31.36 EUR per day or 470.4 EUR per request. An estimate of monthly costs for these purposes goes up to 7,761,600 EUR, including taxes and other expenses. Anything more than insignificant non-material expenses, including office work with applications, decision making, mailing the documents, employee's education, and an applicant's satisfaction, is impossible to estimate.  
From the mathematical model, which considers the time required for a user to complete his/her request, and which is described in subsection in Section 4, it becomes easy to identify the key benefits that the model brings to citizens and to the public administration itself. Advantages include faster (timesaving) and better service; increased transparency; increased accuracy; data safety; privacy protection; and financial benefits. We want to emphasize a significant increase in social and population governmental policy effectiveness as a crucial benefit.