esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### SERS Measurements  
3.1 SERS Detection of Benzidine in Organic Solvents Using Spherical Gold Nanoparticles as a Substrate  
First, a solution of the above spherical gold nanoparticle (2.0 mL) was thoroughly mixed with 1 mL of acetone and 3 mL of anhydrous methanol. The mixture solution was centrifuged at 10,000 rpm for 5 min and then the dark precipitate was collected and re-dispersed in 1.0 mL of chloroform under ultra-sonication. This washing process was repeated twice, and the final precipitate was re-dispersed in 300.0 \(\mu\)L of chloroform under ultra-sonication for later use.  
To a SERS spectrometer sample vial were added 300.0 \(\mu\)L of the above Au nanoparticle solution, 145.0 \(\mu\)L of methanol, and 155.0 \(\mu\)L of 0.8 mM benzidine in chloroform. The SERS spectrum was recorded at an integration time of 10 s, and each spectrum is an average of three independent readings.  
3.2 SERS Detection of 3,4-Benzopyrene in Organic Solvents Using Spherical Gold Nanoparticles as a Substrate  
First, a solution of the above spherical gold nanoparticle (2.0 mL) was thoroughly mixed with 1 mL of acetone and 3 mL of anhydrous methanol. The mixture was centrifuged at 10,000 rpm for 5 min, and then the precipitate was collected and re-dispersed in 1.0 mL of chloroform under ultra-sonication. This washing process was repeated twice, and the final precipitate was re-dispersed in 300.0 \(\mu\)L of chloroform.  
To a SERS spectrometer sample vial were added 300.0 \(\mu\)L of the above Au nanoparticle solution and 300.0 \(\mu\)L of 0.3 mM 3,4-benzopyrene in chloroform. The SERS spectrum was recorded at an integration time of 10 s, and each spectrum is an average of three independent readings.  
3.3 SERS Detection of Malachite Green Using Long Gold Nanorods or Silk-Like Ultralong Nanowires as a Solid Substrate  
First, a solution of the above-prepared long gold nanorod (5.0 mL) was added drop by drop to a depression area of a concave glass slide at room temperature. After the solvent had completely evaporated, a layer of dark gold nanorods (5 \(\times\) 5 mm\({}^{2}\)) was obtained. Afterward, the slide was carefully dipped into 10.0 mL of acetone for 1 min, dried at room temperature, and then the layer of gold nanorods was used as a solid substrate for further SERS detection. Then, 20.0 \(\mu\)L of 1.0 mM malachite green in chloroform was added to the center of the gold nanorod layer by a microsyringe. The laser irradiation from the SERS spectrometer was adjusted to focus on the surface of the center ofthe layer. The SERS spectrum was recorded at an integration time of 10 s, and each spectrum is an average of 10 independent readings. The SERS detection of malachite green using silk-like ultralong nanowires as a solid substrate was completed in a similar manner except that the gold nanorod solution was replaced with the above-prepared ultralong gold nanowire solution.
## 3 Feature extraction and dimension reduction
### Traditional multivariate analysis methods
The coefficients of an overly-deep decomposition level might not have enough signal information, while those of a low decomposition level may still contain much noise. Determining how many levels of wavelet coefficients are needed and whether the coefficients in both low and high frequency domains are useful is case-dependent.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 3 Theoretical Model
### Phase Error Model : Multipath, Phase Standard Deviation and Radiation Pattern
#### 3.3.2 Two Types of Phase Error
Figure 4 presents a 2D schematic view of the measurement distributions from phase space to displacement space. We see that the phase distribution is scaled and translated in phase space, compared to the centered distribution that was set in Equation (8). In displacement space, this gives a specific localization spot with covariance \(C_{r}\), translated from the true LOS measurement by vector \(dr_{b}\). The specific values of \(C_{r}\) and \(dr_{b}\) are discussed in Section 4.2.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 4 Experiments  
We experimentally analyzed the crop disease classification performance. We compared the result from both image and environmental data with that from crop image data alone. We also compared the classification performance of the proposed multimodal mixup data augmentation method with that of unimodal mixup data augmentation on image data. We utilized the dataset from the Korean AI Challenge, which is introduced in Section 3.1. We divided the dataset into a ratio of 5:3:2 for training, validation, and testing sets, respectively. Due to the imbalanced distribution of data samples, we oversampled for the classes with insufficient sample size during the training phase. The macro \(F1\) score was employed as the performance metric for crop disease diagnosis prediction. This is commonly used for multi-class classification and was also employed as a performance metric in the AI challenge. The \(F1\) score can be expressed as shown in Equation (3), where \(TP_{i}\), \(FP_{i}\), and \(FN_{i}\) are, respectively, the number of true positive samples (correctly classified positive samples), false positive samples (incorrectly classified positive samples), and false negative samples (incorrectly classified negative samples) for a given \(i\)-th class, and \(n\) is the number of classes.  
\[\textbf{{F}}1=\frac{1}{n}\sum_{i}^{n}\frac{\textbf{TP}_{i}}{\textbf{TP}_{i}+( \textbf{FP}_{i}+\textbf{FN}_{i})/2} \tag{3}\]  
All experiments were conducted in an environment with an Intel(R) Core(TM) i9-10940x CPU@ 3.30 GHz, DDR 128 GB, and NVIDIA RTX3090. The implementation was done using PyTorch version 1.13.0. In all experiments, we used a minibatch size of 8. Also,  
Figure 3: An example of multimodal mixup (**a**) Paprika-Deficiency (P)-Early’; (**b**) Paprika-Normal’; (**c**) Multimodal mixup result. Multimodal data (**a,b**) are linearly interpolated to generate augmented data (**c**). The label of augmented data (**c**) is also generated by linearly interpolating the labels of (**a,b**).  
we used an Adam optimizer [29] to train the deep learning model over 300 epochs, where the initial learning rate was set to 0.001, and the learning rate scheduler was the cosine-annealed warm restart learning scheduler. Adam optimizer is one of the most commonly used in deep learning. It adaptively adjusts learning rates based on the first and second moments of the gradients, which allows for faster convergence and robustness to sparse gradients.  
All image data were resized to 256 \(\times\) 256 and normalized using the mean and standard deviation values used in ImageNet training [28]. Environmental data were normalized using min-max normalization so that the data values could fall between 0 and 1. Then, zero-padding or cropping was performed so that the length of all data could be 320. Augmentation methods including scaling, rotation, translation, flipping, and coarse dropout were only applied to image data.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 4 Performance Assessment and Validation
### Experiment Results
#### 4.2.2 Integration Validation Using U-Blox Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **GNSS** & **KF** & **AKF** & **FGO** & **AFGO** \\ \hline
2D RMSE [m] & 26.39 & 70.18 & 66.84 & 24.35 & 19.35 \\ \hline
2D STD [m] & 22.30 & 61.72 & 59.19 & 18.71 & 14.09 \\ \hline Number of epochs with available solutions & 852 & 920 & 920 & 920 & 920 \\ \hline Average computational time [s] & – & \(7.93\times 10^{-3}\) & \(8.13\times 10^{-3}\) & \(7.38\times 10^{-1}\) & \(7.52\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall evaluations on GNSS solutions and GNSS/INS integration, including KF, AKF, FGO, and AFGO, for U-Blox data.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 2 Structural Power Content Analysis
### Delay-and-Multiply Operation  
The GNSS signals transmitted by each satellite are based on a PRN code, comprising a number of chips, which repeats at a given rate. For example, the GPS L1 C/A code has a chip rate of 1.023 MHz, a code length of 1023 chips, and repeats once every 1 ms [8]. In the spectral domain, the signal has a comb-like structure, with 1 kHz spacing relating to the code repetition rate and a broad amplitude envelope relating to the chip rate. The L1 signal is modulated on a carrier frequency of 1575.42 MHz but, due to the Doppler shift, is offset in frequency in the receiver's reference frame. Furthermore, this offset is generally different for each satellite due to their different relative motions with respect to the receiver, meaning that a single frequency translation cannot simultaneously align all the received PRNs in the spectral domain. In order, then, to concentrate the power of all the received GNSS signals (of a given constellation and signal type) in the spectral domain, the first step is a delay-and-multiply (DAM) operation.  
In the DAM operation, the signal is multiplied with a delayed and complex conjugated version of itself, resulting in four signal components: the multiplication of PRN codes with their delayed conjugates, the cross-correlation of different PRN codes, the correlation of PRN codes with noise components, and the correlation of the noise components with their delayed conjugates. For each PRN code multiplied with a delayed version of itself, the result has two key characteristics. One is that the phase rotation caused by the Doppler shift is canceled, because the applied delay is of the order of one chip period and typical Doppler shifts (5 kHz or lower) are much smaller than the chip rate (1.023 MHz for GPS L1). The other is that multiplying the PRN with an integer chip period delayed version of itself produces another PRN code with a similar line spectrum with 1 kHz spacing (for GPS L1), due to the properties of the PRN codes [7]. This results in the alignment of each satellite signal's spectral content on the same frequencies as the other signals. The delay is chosen to be a single chip period, which ensures that the signal in the DAM output has the same chip rate as the original signal and that the distribution of the signal power in the spectral domain is preserved.  
Let the received signal after digitization with sample period \(T_{s}\) be denoted as \(r[n]\) and the one-chip-delayed signal be denoted as \(r[n-k]\), where \(k\) is the number of chips delay. For simplicity, we assume that the sample period divides the chip period \(T_{c}\) exactly, that is \(T_{c}=\mathrm{I}T_{s}\). Then, the output of the DAM operation \(y[n]\) can be written as follows:  
\[y[n]=r[n]\times r[n-k]^{*}, \tag{1}\]  
where * indicates the complex conjugation operator.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 4 Algorithm-Based Energy Sustainability in WSNs
### Algorithm-Based Methods for Energy Sustainability in WSNs
#### 4.3.3 Energy Efficient Routing
Protocols of this kind achieve routing with enhanced fidelity [74];
* Multipath-based protocols route data from nodes to sinks via various paths, in order to perform load balancing, overcome node failures and congested paths, and decrease end-to-end delay [51].
## 3 Proposed Methodology
### Sensitivity Analysis  
The emphasis is on investigating the effectiveness of the global weight on the ranking of the results in order to verify the suggested approach [69]. Presume that the variable representing the global weights is \(W_{j}=(W_{1},W_{2}\ldots W_{K})\) When the total of the normalized global weights is \(1\).  
\[\sum_{j=1}^{k}w_{j}=1 \tag{17}\]  
If the global weight of the MCDM methodology's criteria and sub-criteria varies as \(c\) using the following Equation (18). The following Equation (19) are used to lower the remaining global weight values by a predetermined amount, reducing the total number of global weights to \(1\).  
\[W_{c}^{\prime}=W_{c}+\Delta c \tag{18}\]  
\[W_{j}^{\prime}=\frac{1-W_{c}-\Delta c}{1-W_{c}}*W_{j}=\frac{1-W_{c}^{\prime}} {1-W_{c}}*W_{j} \tag{19}\]  
In order to improve the effectiveness of the \(Z\)-number-based MCDM techniques, this research offers \(Z\)-number-based fuzzy DEMATEL with TOPSIS referred to as (ZF-DEMATEL-TOPSIS) an integrated fuzzy DEMATEL and Fuzzy TOPSIS with \(Z\)-numbers to address the information reliability, subjectivity, and uncertainty in this research design concept assessment. The entire \(Z\)-number-based GMCDM process is divided into three stages, as shown in Figure 4. The first stage is constructing the decision makers' judgment into matrices and using \(Z\)-number-based fuzzy DEMATEL, used for criterion and sub-criterion to determine the global weights; the second stage is \(Z\)-number-based fuzzy TOPSIS for highly sustainable packaging circular model ranking, and the third stage is to validate the suggested approach. First, a team of three professors from the CE background are picked as the experts. They choose the evaluation components (criteria, sub-criteria, and alternatives) and provide impact analyses during the expert interview. Excel and Origin Pro are used for the data analysis and validation. The direct relation matrix in the \(Z\)-number-based Fuzzy DEMATEL evaluations and the z-number-based Fuzzy TOPSIS decision values in the circular model ranking are to be evaluated independently by each expert. To reflect expert impact estimates and accumulative group evaluations, a \(Z\)-number-based GMCDM is created. The global weights of the criterion and sub-criteria will then be determined using a fuzzy AHP based on Z-numbers. A fuzzy TOPSIS based on \(Z\)-numbers is provided to rank the parameters. The rank of the parameters is validated via a sensitivity analysis. The effect evaluations' dependability, uncertainty, and subjectivity are well defined by introducing \(Z\)-number into GMCDM.
## 3 Constructing an AT Representation Model Containing Tolerance-Zone DOF Layers
### Semantic Modelling of Geometric Tolerance Information with Tolerance-Zone DOFs  
According to ISO 1101 and the new-generation GPS [36], geometric tolerance components include three types of feature information: geometric tolerance objects, tolerance types, and tolerance zones. In the literature [14], the process of screening the tolerance types of the AFE in a part is constructed by defining the tolerance-zone DOFs and defining the control parameter DOFs of the assembly, and thus the screening of tolerance types is thus realised. Therefore, based on the engineering semantics of tolerance information, a semantic representation model for geometric tolerances based on tolerance-zone DOFs is constructed, as shown in Figure 2.  
Figure 2: Semantic representation model for geometric tolerance.  
The rectangles in Figure 2 represent entities, and the lines connecting the rectangles represent the relationships between the entities (called object attributes). The rectangles connected by small circles represent the value domain of the attribute, and the entities connected by no small circles represent the definition domain of the attribute. The value domains of the data attributes are of various data types.
## 2 Materials and Methods
### Prediction Models Workflow
#### 2.2.3 Hand Labelling to Establish Ground Truth  
Two annotators trained by a certified behavioral analyst used the collaborative behaviors defined in Table 3 to label the participants' behavior as either Engaged, Struggling, or Waiting based on the extracted features discussed in Section 2.2.1 and from watching video recording of the sessions. The annotators labeled 10 min of interactions from each session, for all six experimental sessions individually, resulting in 4976 hand-labeled datapoints. They achieved 98% agreement, and the remaining 2% disagreement was reconciled where both annotators decided on an agreed label through discussion. From the labeled data, the class distributions of the three behaviors were as follows: _Engaged_--19.9%, _Struggling_--28.0%, and _Waiting_--52.1%. The hand-labeled data distribution shows that the behaviors are not equally distributed, and the majority of the labeled behavior was Waiting since the tasks mainly involved turn-taking. When designing the HMM prediction model, the imbalance in the data distribution was taken into consideration to avoid overfitting and bias to the prediction model. To achieve this, we used \(k\)-fold cross-validation to minimize the data imbalanced. This is explained in more detailed in Sections 2.2.5 and 3.1.
## 2 Materials and Methods
### Quantification of Aroma Compounds
#### 2.3.2 Solid-Phase Extraction-GC-Triple Quadrupole MS/MS (SPE-GC-QqQ-MS/MS)  
Aliphatic lactones in wines were extracted with the SPE method according to our published method [8]. A Cleanert PEP-SPE (1000 mg/12 mL) column was first activated by 10 mL of dichloromethane, 10 mL of methanol, and 10 mL of 11% (\(v/v\)) ethanol. Then, 20 mL wine samples were added, followed by 10 mL of deionized water to remove sugar, acid, and macromolecule polar compounds; then, 15 mL dichloromethane, the eluent, was added with 1 g of anhydrous sodium sulfate to remove water as much as possible. Finally, the volatile extract was concentrated to 500 \(\upmu\)L under a steam of nitrogen and filtered with a 0.45 \(\upmu\)m filter membrane.  
An Agilent Intuvo 9000 GC system equipped with an Agilent 7693 autosampler and an Agilent 7010B GC-MS/MS triple quadrupole (Agilent Technologies, Palo Alto, CA, USA) was used for quantitative analysis of aliphatic lactones [8]. A column HP-5 MS UI 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m (Agilent Ultra Inert GC column) was used to separate analytes. The extract (1 \(\upmu\)L) was injected in splitless mode, and splitless time was 1 min. Helium was used as the carrier gas (1.0 mL/min) and the quenching gas (2.25 mL/min), and nitrogen was the collision gas (1.5 mL/min). The oven temperature program was as follows: 40 \({}^{\circ}\)C, held for 1 min, then increased to 160 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and finally increased to 220 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C/min and held for 1 min. After each analytical run, the post-run was performed at 280 \({}^{\circ}\)C for 2 min. A solvent delay of 5 min was performed for each analysis to prevent instrument damage. The temperatures of the injector, ion source, transfer line, and quadrupole 1 and 2 were 250 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. The mass spectrometer was operated in electron ionization (EI) mode at 70 eV with MRM. Data acquisition and analyses were performed using the MassHunter Workstation software version B.10.00, supplied by the manufacturer (Agilent Technologies, Palo Alto, CA, USA). Qualitative and quantitative information for the aliphatic lactones is listed in Table S3.
## 4 Experiments
### Experimental Result  
#### 4.4.1 Review Helpfulness Classification Performance Comparison  
In this section, we first study the effect of changes in vocabulary size and review length on the classification performance of the CNN-BiLSTM hybrid model. To retain the main semantics and suppress noise, first, we performed several experiments that set several vocabulary sizes from 20,000 to 104,702. Then, we select the maximum review length and the average review length for each vocabulary size to conduct experiments. Finally, we set the optimized vocabulary size and review length to train the CNN-BiLSTM hybrid model efficiently. We conducted the experiment five times and reported the mean and the standard deviation of the classification performance. Table 5 indicates the mean  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Class** & **Predicted Class** & **Helpful** & **Unhelpful** \\ \hline Helpful & TP & FN \\ Unhelpful & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix example for evaluating the performance of helpfulness classification.  
and the standard deviation of classification performances of the CNN-BiLSTM hybrid models with several vocabulary sizes and review lengths. We found that the performance worsened when the vocabulary sizes were set too high. Thus, optimal vocabulary sizes should be used to improve the recommendation performance. In addition, we found that maximum review lengths should be used to improve the recommendation performance. As a result, the optimal vocabulary size was 80,000, and the maximum length of the review should be chosen. We set the optimal vocabulary size and review length to compare the classification performance with the baseline model.  
We set the optimal number of words and review length and compared the CNN-BiLSTM hybrid model with the baseline to evaluate the classification performance. We experimented five times and reported that the mean and the standard deviation of the classification performance are shown in Figure 5. The CNN-BiLSTM hybrid model outperforms other baseline models with an accuracy of 86.71% and F1-Score of 86.43%. Although the CNN single model represents an excellent classification effect, the other deep learning models are better than CNN. Compared to CNN, BiLSTM single models, the CNN-BiLSTM hybrid model shows the advantages of combined networks in semantic representation extraction. Because CNN models for word vectors are conducive to reprocessing CNN feature extraction by BiLSTM, we can find that adding an attention mechanism to the combination model can effectively enhance classification performance. The attention mechanism helps the model learning essential features by assigning different weights and learning differences between different features.
## 3 Results  
### Size and Zeta Potential of PRKCA Specific siRNA- and PTEN Plasmid-Loaded NPs  
Prior to cellular transfection, we assessed the size and zeta potential of the siRNA and/or plasmid NPs complexes, and the results were plotted in Figure 1. CA NPs were prepared with 4 mM exogenous CaCl\({}_{2}\) in 1 mL of bicarbonate buffered-DMEM medium with 1 nM PRKCA specific siRNA and/ or 1 ug of PTEN plasmid DNA, followed by incubation for 30 min at 37\({}^{\circ}\)C. Next, 10% FBS was added to stop further siRNA/plasmid-NPs complex formation. Figure 1a,b demonstrated the average sizes and zeta potential of PRKCA+NPs, PTEN+NPs and PRKCA+PTEN+NPs formulations. The sizes of the NPs with loaded PRKCA siRNA, PTEN plasmid and PRKCA siRNA+PTEN plasmid were \(\sim\)112 nm, \(\sim\)196 nm and \(\sim\)168 nm in diameter, respectively (Figure 1a). These nanoscale-sized complexes are advantageous for cellular transfection compared to large sized vector carriers.  
As shown in Figure 1b, the zeta potential of NPs control was observed to be \(-11\) mV. Incorporation of negatively charged siRNA and or plasmid DNA molecules increased the negative surface charge of the siRNA/plasmid +NPs complexes. The zeta potential for PRKCA+NPs, PTEN+NPs and PRKCA+PTEN+NPs complexes was recorded as \(-12\) mV, \(-13\) mV and \(-11.5\) mV, respectively. As our NPs contain both positive and negative ions, such as Ca\({}^{2+}\), PO\({}_{4}\)\({}^{3-}\), OH\({}^{-}\), the negative charge may come from the protein corona formed around the NPs in serum-containing media. In fact, a recent study revealed that at physiological pH, CA NPs attach to both positive and negative proteins; however, the negative proteins outnumbered the positive ones, which could contribute to the negative zeta potential [28]. Negative zeta potential is advantageous in terms of longer retention in blood stream [34]. Comparison of particle diameter and zeta potential of siRNA, plasmid and NP complex showed that large particles (PTEN+NPs, \(\sim\)196 nm) possess slightly more negative zeta-potential (\(-13\) mV) compared to small particles (PRKCA+NPs are \(\sim\)112 nm and PRKCA+PTEN+NPs are \(-168\) nm).