esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Optimization of BS Location
\[L_{0}= \in_{fs}{d_{0}}^{2}=7.569*10^{-8}=-71.20961495\ \mathrm{dB} \tag{18}\]  
To convert the dB value to a normal value the equation will be  
\[L\ (d)=10^{L(d)dB}/_{10} \tag{19}\]  
By placing the path loss, given in Equation (16) the required energy for sending an \(l\)-bit message across a distance d will be computed as  
\[E_{tx}(l,d)=lE_{elec}+l*10^{L(d)dB}/_{10} \tag{20}\]  
```
1. Set-up the coordinates of SNs (\(x_{n}\), \(y_{n}\)) and virtual points \(v\) (\(x_{m}\), \(y_{m}\)) the network
2. Calculate the distance between each live SN and virtual point by Equation (13)
3. Calculate the weights by using Equation (14)
4. Calculate the optimal \(l^{*}\) by using Equation (15)
5. Move the BS to \(l^{*}\)
6. **while** the network is alive
7. **if** a predefined number of SNs are dead
8.  go to step 2
9. **else if**
10.  transmit collected data to RN, and RN will retransmit data to BS
11. **else**
12.  transmit collected data to the BS directly
13. **endif**
14. **end while**
```  
**Algorithm 2** Optimization of mobile BS location in a large area network with a RN
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 6 Experimental Evaluation
### Description of the Real-World Problem Instance Solved  
The experimental evaluation of the proposed MILP model for grain production optimization was performed on the specific instance of the case study described in Section 5.  
According to the values presented in Table 1, the parameters values that define the considered problem instance are as follows:  
* \(V_{I}=\{1,\cdots,7\}\); \(V_{T}=\{8,\cdots,17\}\); \(V=V_{I}\cup V_{T}\) ; \(\tau=2\). The seven inoculated products products of the real case are coded with an index \(1\ldots 7\). The treated products are coded as \(8\ldots 17\). The \(\tau\) parameter specifies the shelf life of the inoculated products and it is set to 2 weeks.
* \(L=\{1,2\}\); \(\pi_{I}=\{6.165,6.5\}\); \(L_{l}=\{6,8\}\), with \(l\in L\). These parameters correspond to the set of machines available, their production capacity, and their cleaning times in hours, respectively.
* \(S_{f}=19\); \(C=2700\); \(N=3\); \(H_{f}=7.5\); \(D_{s}=5.5\forall s\). The real case harvest spans 19 weeks, therefore, the parameter \(S_{f}\) is set to 19. The storage capacity of processed grains (parameter \(C\)) is set to 2700 tons, the maximum number of operator shifts (parameter \(N\)) is set to 3, and the number of productive hours in a shift (parameter \(H_{f}\)) is set to 7.5. The number of working days for each week (parameter \(D_{s}\)) is set to 5 days (i.e., from Monday to Friday and a half day at Saturday).
* The maximum number of withdrawals for each product (parameter \(M\)) is set to 10.
* The number of batches corresponding to the inoculated products is \(B_{v}\)=\(\{6,7,8,4,5,2,2\}\) with \(v\in V_{I}\) and the treated products belonging to a single batch is \(B_{v}=1\)\(\forall vs.\in V_{T}\).
* The parameters \(P\), \(Q\), \(R\) of the objective function are, respectively, set to be 1000, 10 and 15, so that their linear combination properly accounts for the real incurred costs expressed in USD.  
The demands of each product in every week are set based on the clients orders and estimates of the company's sales department. They are summarized in Table 2. The algorithm is fed with this table, where every value corresponds to an amount of a certain product to be handed over to the clients in a certain week. The inoculated products are labeled 1 to 7. The remaining are treated products, i.e., no shelf life limit applies to them. Table 2 allows also to generate the final pair of input parameters: (\(S_{v}^{r}\)) that represent the week in which the \(r\)-th withdrawal of product \(v\) is located, and (\(S_{v}^{r}\)) that represents the demand for product \(v\) in its \(r\)-th withdrawal.  
Several variations of the input parameters are combined building a set of 16 test cases.
## 4 Experimental Results
### Evaluation Metrics and Experimental Setup  
In our research, the primary objective of the LSTM-Attention-BiTCN model, and indeed the baseline methods, is to accurately classify social media posts in terms of suicidal ideation. To assess the efficacy of these models, we employ a suite of metrics commonly used in classification tasks: accuracy, precision, recall, and the F1-score, as represented in Equations (5)-(8).  
\[Accuracy =\frac{True_{positive}+True_{negative}}{Total\ predictions} \tag{5}\] \[Precision =\frac{True_{positive}}{True_{positive}+False_{positive}}\] (6) \[Recall =\frac{True_{positive}}{True_{positive}+False_{negative}}\] (7) \[F1-score =2 \underbrace{Recall\times Precision} \tag{8}\]  
For the experimental setup of our study, we conducted the implementation and evaluation of all baseline methods, encompassing traditional machine learning models, advanced deep learning techniques, and attention-based ensemble models by using the Google Colab Notebook service with Tesla A100 GPU and 84 GB RAM. Our development and testing were carried out using Python version 3.10. For the construction and training of deep learning models, we employed Keras, a powerful and user-friendly open-source library. Additionally, we leveraged the capabilities of the Natural Language Toolkit (NLTK), a comprehensive Python library designed specifically for NLP tasks. A key component of our proposed model included TCN-based layers implemented using the Keras Temporal Convolutional Network library. The LSTM-Attention-BiTCN model was trained utilizing the \"adam\" optimizer with a default learning rate of 0.001 and a \"binary cross entropy\" loss function for a total of 30 epochs and 64 batches. To prevent model overfitting and accelerate the training speed, we applied two callback functions: EarlyStopping and ReduceLROnPlateau. The EarlyStopping callback intervenes to halt the training when there is no improvement in validation accuracy for a specified patience of 10 epochs, thus ensuring the model retains the best weights at the point of maximum validation accuracy. Concurrently, the ReduceLROnPlateau callback dynamically adjusts the learning rate when the validation loss fails to decrease over a patience period of five epochs, with the learning rate being reduced by a factor of 0.1. Table 1 presents the quantities of units and associated configurations, along with the model's architectural design.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Parameters** & **Values** \\ \hline Embedding & Embedding dimension & 300 \\ Spatial Dropout & Dropout rate & 0.1 \\ LSTM & Units, activation function & 100, tanh \\ Bidirectional TCN (1) & Units, activation function, & 128, ReLu, [1; 2; 4], 0.1 \\ Bidirectional TCN (2) & Units, activation function, & 64, ReLu, [1; 2; 4], 0.1 \\ Dense (1) & Units, activation function & 16, ReLu \\ Dropout & Dropout rate & 0.1 \\ Dense (2) & Units, activation function & 1, sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup of the proposed LSTM-Attention-BiTCN.
# Comparison of Lab vs. Backcalculated Moduli of Virgin Aggregate and Recycled Aggregate Base Layers
## 3 Results and Discussion
### Validation of FWD-Based Calibrated NCHRP 1-37A Model  
As mentioned earlier, the multi-load FWD test results were analyzed using the Modulus 7.0 program to estimate the stiffness of the pavement layers, which was later inputted into the KENLAYER program to calibrate the NCHRP 1-37A model coefficients for the unbound layers. Both the Modulus 7.0 and KENLAYER programs are LEA-based analysis tools. Therefore, the nonlinear NCHRP 1-37A model's coefficients were calibrated using a linear-elastic analysis procedure. To validate these calibrated coefficients, the test sections were modeled using the MatFEA program (as a nonlinear pavement response model), and the calibrated coefficients of the NCHRP 1-37A model were used as the inputs for the unbound layers. Later, the modulus of the specific element located at the mid-depth of the unbound layer (9.5, and 20 inches from pavement surface for the base and subgrade layers) and under the loading center was extracted from the MatFEA program and compared to the backcalculated \(E_{FWD}\) from the Modulus 7.0 program. The comparison was performed using the relative difference (\(RD\)) concept, as shown in Equation (7). It is noted that the proximity of the RD index to 1 is an indicator of a lower difference.  
\[RD=1-\left(\frac{M_{R\_MatFEA}-E_{FWD}}{M_{R\_MatFEA}}\right) \tag{7}\]  
where \(RD\) is the relative difference, \(M_{R\_MatFEA}\) is the modulus extracted from the MatFEA program (psi), and \(E_{FWD}\) is the backcalculated stiffness extracted from the Modulus 7.0 program (psi). Table 3 summarizes the relative differences between the base + subbase and subgrade layers of different test sections. According to this table, the relative differences for the base + subbase layer in all test sections were within the 4% range close to 1.0, which can be used as a validation fact for the calibrated coefficients of the base + subbase layer based on FWD test data. In addition, though higher relative differences were observed for the subgrade layer, they were still within a 15% range of 1.0, which is a reasonable range for the relative difference of the materials in the field. One reason for the poorer match in the subgrade layer can be attributed to the significantly lower stress state at the 20-inch depth from the pavement surface, at which the modulus values were small; the tiny differences could have caused higher relative errors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Relative Difference (\(E_{FWD}\))**} \\ \cline{2-3}  & **Base + Subbase** & **Subgrade** \\ \hline Cell 185 & 0.99 & 0.85 \\ (coarse RCA base, sandy subgrade) & \\ \hline Cell 186 & 0.96 & 0.92 \\ (fine RCA base, sandy subgrade) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average relative differences between field-based backcalculated \(E_{FWD}\) values and MatFEA-based \(M_{R}\) values.
## 2 Materials and Methods
### Base Classifiers
#### 2.2.5 Random Forest  
Random forest (RF) is an ensemble classifier based on DTs [27]. It uses the bootstrap resampling technique repeatedly to extract n different samples from the original dataset to create a new training sample set to train the decision trees and then generates n decision tree classifiers. Each decision tree classifier predicts the test samples and integrates n generated decision trees using the bagging method. Here are the main steps of Random forest:  
1. Data Sampling (Bootstrap): For a given training dataset, a random forest performs random sampling with replacement, generating multiple different subsets. This is referred to as bootstrap sampling. This means that certain samples may appear multiple times in one subset, while others may not appear at all.
2. Feature Random Selection: At each node of every decision tree, a random forest does not consider all features for splitting; instead, it randomly selects a subset of features from the feature set. This helps increase the diversity of the model and prevents overfitting.
3. Decision Tree Training: For each bootstrap-sampled subset and feature subset, a decision tree is trained. The commonly used decision tree algorithm is CART (Classification and Regression Trees).
4. Voting or Averaging: For classification problems, a random forest employs a majority voting approach, where each decision tree votes for a class, and the final prediction is the class with the most votes.  
Ultimately, the output of a random forest can be expressed as:  
\[y=majorityvote(f_{1}(x),f_{2}(x),...,f_{n}(x)) \tag{7}\]  
Figure 1: Decision tree model.  
where \(y\) is the prediction of the random forest, and \(f_{i}(x)\) is the prediction of the \(i\)-th decision tree.
# Variable Multiple Interleaved Bi-Directional DC/DC Converter with Current Ripple Optimization
## 2 The Principle of Current Ripple Optimization
multiplicity bidirectional DC/DC power converter is to automatically select the working number of parallel multiplicities according to the current duty cycles of the system. The number of parallel multiplicities that should be used for the minimum current ripple when the duty cycle is in the range of 0.1-0.9 is shown in Table 1. For example, when the duty cycle of the system is 0.25, the quadruple parallel structure should be adopted, and the controller outputs four PWM signals with a 90-degree phase shift in each multiplicity. Figure 5 shows the number of parallel multiplicities that should be taken to minimize the current ripple. For example, when the duty cycle is 0.2, a five-fold parallel structure should be adopted, and the current ripple is the smallest; when the duty cycle is 0.25, a four-fold parallel structure should be adopted. Thus, a curve with the smallest current ripple can be obtained in Figure 5. By calculating the intersection point of the minimum current ripple carve in Figure 5, the corresponding relationship between the parallel multiplicity and the duty range can be obtained, as shown in Table 1.
# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
## 4 Results
### Frequency Analysis  
To further clarify the signal information, an analysis is conducted in the frequency domain. Only the mean value of the ISP is subjected to this analysis, as it is derived from the three-phase signals and inherently contains information from all three. For the analysis, the mean ISP signal is centered on the \(x\)-axis to remove the DC component that would otherwise appear in the FFT spectral analysis but is not relevant for fault detection.  
A load imbalance or an incipient interturn short circuit induces changes in the magnitude and frequency of the ISP, as shown in Figures 5-7. However, in a healthy and balanced transformer, the ISP appears almost as a ripple, as observed in Figure 4. Figure 8 shows the FFT analysis applied to the mean ISP signals of the currents for both the healthy transformer and the transformer with a four-turn load imbalance. A frequency component appears at \(2f=120\) Hz with a magnitude of 0.36, representing a difference of 0.31 compared to the magnitude of the \(2f\) frequency component in the mean ISP for the healthy current. The remaining spectrum does not show significant changes between the healthy transformer and the transformer with a load imbalance.  
Figure 9 shows the spectrum obtained from the FFT analysis of the two-turn short-circuit signal, where the imbalance effect is again evident in the \(2f=120\) Hz component, with a magnitude of 0.34. This represents a difference of 0.29 compared to the magnitude of the \(2f\) frequency component in the mean ISP value for the healthy transformer. Sim  
Figure 8: FFT of unbalanced ISP compared with healthy ISP.  
Figure 7: Measurements of 4-turn short-circuited ISP and three-phase signal.  
ilarly, to the load imbalance case, the rest of the spectrum remains unchanged, with no significant variations.  
The FFT analysis of the mean ISP value for the transformer with a four-turn short circuit is shown in Figure 10. A significant increase in the magnitude of the \(2f=120\) Hz frequency component can be observed, reaching a magnitude of 1.08, which represents a difference of 1.03 compared to the \(2f\) frequency component in the mean ISP value of the healthy transformer. Additionally, in the four-turn short-circuit fault, a \(4f=240\) Hz frequency component appears with a magnitude of 0.09. This component is not present when a four-turn load imbalance occurs, and its appearance can be used as an indicator to differentiate between an incipient interturn short circuit and a load imbalance.
###### Abstract  
Solid dispersions were prepared via a solvent evaporation method, employing ethanol (96%, _v/v_) as solvent, with three different polymers as carrier: poyidone, copovidone, and poloxamer 407. Previously developed reversed-phase HPLC (RP-HPLC) methods were modified and used for the simultaneous determination of acetylsalicylic acid and clopidogrel bisulfate and after release from solid dispersions. Chromatography was carried out on a C-18 column, with a mobile phase of acetonitrile-methanol-phosphate buffer pH 3.0, UV detection at 240 nm, and a run time of 6 min. The method was validated according to International Conference of Harmonisation guidelines and validation included specificity, accuracy, precision, linearity, robustness, limit of detection (LOD), and limit of quantification (LOQ). The method is specific for determination of acetylsalicylic acid and clopidogrel bisulfate. The linearity was provided in the concentration range 0.0275-0.1375 mg/mL for acetylsalicylic acid and 0.0200-0.1000 mg/mL for clopidogrel bisulfate, with a correlation coefficient (R\({}^{2}\) value) of 0.9999 for both active pharmaceutical ingredients (APIs). Accuracy was confirmed by calculated recoveries for acetylsalicylic acid (98.6-101.0%) and clopidogrel bisulfate (100.0-101.6%). The intra-day and the inter-day precision-calculated relative standard deviations are less than 1%, which indicates high precision of the method. The limits of detection and quantification for acetylsalicylic acid were 0.0004 and 0.0012 mg/mL, and for clopidogrel bisulfate 0.0002 mg/mL and 0.0007 mg/mL, respectively. Small variations in chromatographic conditions did not significantly affect qualitative and quantitative system responses, which proved robustness of method. The proposed RP-HPLC method was applied for simultaneous determination of clopidogrel bisulfate and acetylsalicylic acid from solid dispersions.  
c 2020 10 4792 10.3390/app10144792
# Copper Nanowires for Transparent Electrodes: Properties, Challenges and Applications
## 5 Mechanical Properties  
One of the main drivers for the use of nanomaterials as transparent electrodes is their mechanical properties that, unlike ITO, guarantee flexibility upon bending and stretching. Cu NW are no exception to this, and many reports include mechanical testing, mainly bending tests performed over a large number of cycles. Table 1 summarizes the main results reported concerning mechanical testing of Cu NWs. It can be observed that in most cases the resistance of the NW network shows very little change under the length of the testing. In the case of stretching, it is interesting to note that the relative sheet resistance changes, \(R/R_{0}\), reported in Table 1, are obtained after the materials are subjected to a strain ranging from 50% to 250%.  
Figure 8: Welding NWs by Ni electrodeposition. Reprinted with permission from Ref. [63]. Copyright (2020) Elsevier.  
Adhesion to a substrate is often a challenge for NW, which is rarely addressed in the literature. A simple tape test can be performed to address this matter, i.e., a sticky tape is pressed onto the substrate, bearing the NW network, and then peeled off. [PERSON] et al. carried out this test for 10 times on their PU composite noting an _Rs_ change <10% [39]. In this case, the tape had little consequences on the material as the NWs were mainly embedded within the PU matrix. [PERSON] et al. performed peeling tests on Cu NWs on PET substrates [59]. Here, while the adhesion was significantly improved after welding, after 5 cycles of peeling the _Rs_ already increased by 10 times. [PERSON] et al. also performed a tape test on Cu NWs on PET, this time noting little resistance change (hardly quantifiable from the graph shown) after 10 peeling cycles on the network exposed to welding by electroplating [61].  
In summary, this section provided a brief account of the mechanical properties of Cu NW-based network under a variety of testing like bending, stretching, twisting and adhesion.
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 2 Experimental Procedure
### Adsorption Experiments
#### 2.4.4 Adsorption of Anionic Dyes from Mixed-Dye Solutions and Real Water Sample  
A mixed-dye solution was prepared with the cationic dye MB to investigate adsorption behavior in mixed-dye solutions. The concentration of each dye (MO, OG, and MB) was set to 100 ppm before the experiments. For single-dye batch adsorption, 20 mL of a single-dye solution (100 ppm) was mixed with 20 mL of the ACOP suspension (2.0 g/L). In the mixed-dye adsorption experiments, 30 mL of a mixed-dye (15 mL of MO + 15 mL of MB and 15 mL of OG + 15 mL of MB) solution was added to 15 mL of the ACOP suspension (3.0 g/L). After 24 h, the supernatant was collected and quantified, as described in Section 2.4.1.  
To test the applicability of the method in real water, a test was conducted using tap water. The overall experimental scheme was similar to that of the other batch tests while the solution was prepared in tap water. The water quality of the tap water was assessed in the water quality report published by Seoul Waterworks Authority (2021) [45].
## 3 Results
### Microwave-Assisted Extraction Optimization Results
#### 3.1.5 Acid Addition  
During sample preparation, an attempt was made to create an acidic environment, which contributes to the extraction of certain compounds, using 1% acetic acid in all tests. This enables a better extraction of pesticide analytes that usually have stability problems [31]. In order to determine whether the low recoveries of some compounds are due to the creation or lack of a sufficiently acidic environment, a last series of experiments was performed in the absence of acids, in the presence of 5% acetic acid, and in the presence of 1% formic acid. The calculated recoveries are presented in Table S5. According to the results, extraction in the presence of 1% acetic acid showed the best results with acceptable recoveries for 94% of the analytes. The addition of 1% formic acid resulted in acceptable recovery values for 83% of the analytes, while in the absence of acids acceptable recovery values were obtained only for the 34% of the studied analytes. Finally, the extraction with 5% acetic acid gave the lowest recoveries since only 4% of the studied analytes were within the acceptable range. The high recoveries obtained in the presence of acids were in accordance with the results reported by [PERSON] et al. [32] and [PERSON] et al. [27]. The addition of higher acid concentration resulted in the reduction in the percentage of the analytes with satisfactory recovery values. The results are graphically illustrated in Figure 5.