esa-sceva/satcom-chunk-collection huggingface dataset:
# GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
## 3 Chemometric Methods  
### Fingerprint Modeling of GC-MS Data  
Fingerprint modeling and analysis is widely used in food product discrimination or quality control [15]. GC-MS fingerprints are highly complex and the differences between similar samples might be inconspicuous. Therefore, chemometrics modeling of GC-MS data based on machine learning methods could be considered for further sensory data prediction.  
Firstly, odor compounds in food products should be identified as prior knowledge for the calculation. A fingerprint template considering key odor compounds together with other suspected substances that has an effect on sniffing is determined. The compound selection is based on the statistical data of the frequency and intensity in the existing spectrum dataset. Once the template of a specific kind of food is determined, it will be applied to all samples and should not be changed during the whole process. The prior knowledge in the existing dataset is utilized more sufficiently to reduce the uncertain effects in subjective compound selection.  
Secondly, a fingerprint image generation process is introduced to convert the instrument spectrum into structured data. For each sample, the GC spectrum intensity could be filled in the corresponding substance blank in the fingerprint template and the matrix for each sample is unique like DNA. Therefore, the obtained matrix displayed by color scale could be considered as a fingerprint image for a specific sample.  
Then using machine-learning methods, data characteristics stored in fingerprint images are extracted for olfactory results prediction. CNN is applied here as a regression model and the prediction results can be obtained by data reorganization and translation. The process of the whole model is displayed in Figure 1.  
Figure 1: Olfactory prediction process of food product using gas chromatography-mass spectrometry (GC-MS) data.
## 4 Results
### Reduction of Energy Losses for 24 h
#### 4.2.1 Distribution System of 33 Nodes  
Figure 4 shows the results obtained for the scenarios set out in Table 5, in this test system, with a solution vector in terms of location of {14, 24, 30} and sizing of {1.1332, 1.5824, 1.5531} MW. In the hours in which there is active power generation (7:00-20:00) by the PV systems, it shows a reduction in energy losses, while in the hours where there is no injection of active power (1:00-7:00 and 20:00-24:00), scenario (3) is similar to the behavior obtained in scenario (1).  
On the other hand, from 11:00-15:00, scenario (3) presents a higher reduction in energy losses than scenario (2). It indicates that for that period, the most beneficial thing is not to inject 100% of the capacity of the PV systems; therefore, there must be a balance between the energy generated and the demand. If this balance does not occur, the PV systems will contribute energy losses through the electric current injected into the system by \(I^{2}R\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & **Characteristic** \\ \hline (1) & Power losses without PV system \\ (2) & Power losses with constant PV system \\ (3) & Power losses with variable PV system \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scenarios to quantify the reduction of energy losses.  
Table 6 shows the results of the energy losses for the three scenarios proposed in the 33-node system. These results were obtained through the sum of the power losses for each hour of the day. With the DCVSA algorithm, when considering the up-and-down behavior of the PV systems concerning scenario (1), a reduction in energy losses of 23.3643% was obtained. It is also possible to analyze that scenario (2) has a decrease of energy losses of 52.1867 % regarding scenario (1); however, this result is hypothetical since the production availability of PV systems is not ideal and only considers those hours where there is solar radiation.
# Risk Assessment of Soil Contamination with Heavy Metals from Municipal Sewage Sludge
## 3 Research Results and Discussion
The main factor distinguishing the potential environmental risk indicator from the other indicators is the variation in toxicity levels of each heavy metal. Zinc showed the lowest toxicity factor of 1, which resulted in lower risk values according to the PERI when compared to the \(I_{go}\). In most cases, the level of the RAC does not show a high environmental risk (Figure 4). This is due to the low share of heavy metals in the most mobile fraction (F1). The zinc content in the first fraction is 18.41% for S1, 2.86% for S2, and 2.84% for S3. Therefore, it can be concluded that a decidedly greater part of zinc is found in stable fractions which cannot  
Figure 3: PERI of HMs in SS. Figure 2: The geoaccumulation index (\(I_{geo}\)) of HMs in SS. migrate to the environment. This results in a low risk level despite exceeding the values for zinc in relation to the regulation of the Minister of the Environment on municipal sewage sludge [9]. Similar values of the RAC for copper (S1-S3), lead (S1), and zinc (S1) were obtained by [PERSON] et al. [42]. The RAC with a value indicating a low risk of pollution of the environment with copper is also confirmed by the literature data [39]. Copper is an essential component of many enzymes and proteins, but at high concentrations it can be toxic. Many plants are sensitive to the presence of copper, which can reduce yields. The RAC of nickel for all tested sewage sludge samples, and those presented in [42], belongs to the range indicating a medium risk of contamination of the environment with this metal. On the basis of the RAC of sewage sludge analysed by [PERSON] [39], and the tested S1-S3 sludge samples, it was discovered that the tested sewage sludge poses a lower risk of a negative influence of nickel on the environment. The results of the ERF for SS from the analysed facilities are presented in Figure 5. The analysis of the environmental risk factor showed a low level of contamination for all heavy metals from the three facilities, except for zinc for the S1 and cadmium for S2. Zinc in S1 showed a medium risk of contamination. This was due to its increased content in fractions FI and FII. The literature presents the ERF with a high risk of contamination with zinc [39], and a medium risk for cadmium [39]. Analysing the results of the environmental risk determinant, it can be concluded that most metals show a low level of risk (Figure 6). Zinc in all three sewage sludge samples showed a low risk of contamination. The highest indicator level for zinc was obtained for S1. However, the ERD indicator did not show a medium risk when compared to the ERF indicator, due to the fact that the ERF indicator treats the FI and FII fractions equally. Figure 4: RAC of HMs in SS.
## 2 Materials and Methods
### Aroma Compound Analysis
#### 2.4.3 Determination of the Aroma-Active Compounds (AACs)  
The aroma extract dilution analysis (AEDA) method was applied to determine the AACs. In this method, the concentrated aromatic extract was sequentially diluted with dichloromethane in a 1:1 ratio at each step (1:1, 1:2, 1:4, 1:8,... 1:1024) and injected into the GC-MS-O system. The dilution process was terminated once the odor was no longer detectable, marking the completion of the analysis [9]. The flavor dilution (FD) value of an odorant is the highest dilution where it is still detectable at the sniffing port [23].
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 3 Results and Discussion
### Pre-Trained and Scratch-Trained CNN  
To compare transfer learning with network training from scratch, we used the training set described previously (9500 sheets \(\times\) 7 classes) for the ImageNet transfer learning-based network and a network with random initialization parameters for each calculation. Each network was trained for 100 epochs, and the results show that the accuracies of both were almost equal, with 97.51% accuracy for the pre-trained VGG16 and 96.51% accuracy for the scratch-trained VGG16 when classifying seven particles. After 100 epochs of training, all networks showed convergence, with the pre-trained networks showing better performance in comparison, suggesting that the features learned from ImageNet can also assist in the classification of particles. In addition, transfer learning can be used with smaller sample sizes, and is more suitable for dataset collection under real conditions. Finally, transfer learning was effective in reducing the training time. In the scenario described herein, the pre-trained network only needs to train 8.92 million parameters in the fully connected layer, whereas the new training network needs to train 23.63 million parameters, which translates into a training time of 13.5 h for the scratch-trained network compared to 2.5 h for the pre-trained network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy Rank** & **Model** & **Train (Validation) Accuracy** \\ \hline
1 & SVM—Fine Gaussian SVM & 87.70\% \\
2 & KNN—Weighted KNN & 86.60\% \\
3 & SVM—Medium Gaussian SVM & 86.30\% \\
4 & KNN—Three-time KNN & 85.40\% \\
5 & KNN—Cosine KNN & 84.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top five classification performances of machine learning classifiers based on morphological features of images.  
Overall, the transfer learning property of CNNs can be generalized to the classification of subvisible particles. In our study, feature extraction was performed by using the convolutional base of VGG16 pre-trained on ImageNet, and then we used the labeled particle datasets to train the custom classifier and update the weights of the fully connected layer. Our results indicate that the transfer with fine-tuning of features learned from natural images is beneficial for subvisible particle representation, and provided better results than training from scratch. Based on the properties of transfer learning, the method is easier to generalize when compared to other studies. It has the potential to be used directly on the real dataset of industry production process without targeted major adjustments. Transfer with fine-tuning is expected to become a general method for the classification of subvisible particle types in pharmaceutical formulations.
## 3 Varieties of Epoxide-Based Polymer Electrolyte
### Plasticizers and Gel Electrodes
As ionic liquids are comprised of only cations and anions in a molten state, they require some sort of scaffold (such as crosslinked epoxide-based polymers) to function as viable electrolyte materials for use in all solid-state batteries [93]. In addition to requiring solid support, hybridization with a polymer electrolyte allows ionic liquids to overcome low ionic conductivity and low electrochemical stability below 1.1 V [92]. [PERSON] and [PERSON] prepared crosslinked electrolytes from DGEPEG and DGEBA which contained 10 wt% of the ionic liquid 1-ethyl-3-methylimidazolium bis(fluorosulfonyl)imide (EMImFSI) [40]. These polymer electrolytes were thermally stable up to 220 \({}^{\circ}\)C and had an ambient temperature conductivity of 4.3 \(\times\)\(10^{-3}\) S/cm [40]. [PERSON] et al. prepared an ionic liquid-containing polymer electrolyte using DGEBA and Jeffamine D400 with either octadecyl-triphenylphosphonium (O-TPP) or dioctadecyl-imidazolium (DO-Im) as an ionic liquid [41]. Using a Jeffamine as a curing agent meant that the crosslinked electrolyte could be cured at a lower temperature preventing damage to the ionic liquid [33]. This system was thermally stable up to 300 \({}^{\circ}\)C [41]. More recently, [PERSON] et al. swelled a membrane comprised of the epoxide-based polymer DGEBA in a mixture of the ionic liquid methylimidazoliumbis(trifluoromethylonyl)imide (BMIM-TFSI) and LiTFSI to produce a solid polymer electrolyte for use in a supercapacitor [42]. The electrolytes that contained 2 to 8 volume percent of Al\({}_{2}\)O\({}_{3}\) nanowires had a T\({}_{\text{g}}\) of 96 \({}^{\circ}\)C, a room temperature ionic conductivity of 2.5 \(\times\)\(10^{-6}\) S/cm and an activation energy for ion transfer of 0.08 eV as determined via the VTF method [42]. These results suggest that ionic liquid-containing epoxide-based polymer electrolytes are about on par when compared to epoxide-based electrolytes that contain plasticizers. However, as none of the authors cited above measured the electrochemical stability of their films, it is uncertain whether the fact that ionic liquids can be electrochemically stable at voltages as high as 5.3 V vs. Li\({}^{+}\)/Li contributes to improved electrochemical stability in these electrolytes [92].
## 4 Experiments
### Experimental Methodology  
We select two quantitative evaluation components from the object tracking benchmark [7; 8], including precision and success. Precision estimates the center location error between the bounding box of the tracked targets and the bounding box of ground truths. In the precision plot, the center location error in pixel distance varies along the x-axis, and the y-axis represents the percentage of accurately located bounding window per threshold. The threshold is set at 20 pixels [8] for ranking trackers. Success is calculated as the intersection-over-union (IOU) of the tracking bounding window and the labeled ground truths. In the success plot, the IOU overlap rate varies along the x-axis, and the y-axis represents the percentage of accurately located bounding window per threshold. The final ranking is determined by the area under the curve (AUC), computed from the mean of the success rates corresponding to the sampled overlap thresholds from 0 to 1.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 2 Materials and Methods
### Behavior Based Algorithm
#### 2.3.1 Physical Activity Classification
The total archived \(F\)-score accuracy of classification was 91%, as shown in Table 2. The accuracy of the classification was evaluated using the precision (\(P\)), recall (\(R\)), and \(F\)-score metric [32]:  
\[P=\frac{\mathit{truepos}}{\mathit{truepos}+\mathit{falsepos}}, \tag{3}\]  
\[R=\frac{\mathit{truepos}}{\mathit{truepos}+\mathit{falseneg}}, \tag{4}\]  
\[F=\frac{2\times P\times R}{P+R}. \tag{5}\]  
The number of intervals from the class that was correctly classified is _truepos_, the number of intervals from another class that was incorrectly classified as the class is _falsepos_, and the number of intervals belonging to the class that was classified as another class is _falseneg_. The precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Both precision and recall are therefore based on an understanding and measure of relevance [33]. The \(F\)-score is the harmonic mean of accuracy and recall, where the \(F\)-score reaches its best value at 1 (perfect accuracy and recall) and worst at 0 [22; 34].
## 2 Methodology for Seismic Risk Evaluation  
The seismic risk of buildings is usually defined as a function of seismic hazard of the area, vulnerability of buildings, and exposure. The seismic hazard of the area expresses the probability of the occurrence of a certain intensity earthquake in a given area and at a certain period of time. The vulnerability of buildings represents the susceptibility of the structure to suffer damage due to a seismic event of a given intensity. The exposure measures the quality and quantity of elements exposed to the risk.  
In order to completely understand vulnerability and risk of the urban area exposed to seismic action, the comprehensive procedure for seismic risk evaluation and visualization in this paper is structured as follows:  
* Documentation of architectural, structural and material features by examining building codes, historical and archival sources, on-site visual inspection, and thermographic imaging;
* Creation of a database of buildings and visualization of input data by the web map in the GIS environment;
* Geophysical survey of the soil type;
* Definition of seismic hazard for the test site using available seismic hazard maps of Croatia and the results of geophysical survey;
* Seismic vulnerability assessment by vulnerability index method for the sample of the buildings;
* Extrapolation of the results for the seismic vulnerability index for the entire test site;
* Non-linear static analysis of the relevant buildings located in the test site and determination of the peak ground accelerations for damage limitation, significant damage and near collapse states;
* Development of vulnerability-peak ground acceleration curves for three limit states (damage limitation, significant damage, and near collapse) for the test site;
* Development of vulnerability curves that establish relations between damage, vulnerability and peak ground acceleration for the test site, and serve to estimate the structural damage for a given seismic action;
* Risk evaluation in terms of seismic damage for three return periods;
* Risk evaluation in terms of the index of seismic risk for three return periods;
* Visualization of hazard, vulnerability indices, damage indices and indices of seismic risk of the buildings in the web map.
## 5 Proposed Solution
### Multivariate Temporal Correlation  
The temporal correlation happens due to the fact that the sensor node gathers correlated data from one or more variables at a given time. This type of correlation is observed due to the nature of physical phenomena [2] (e.g., the environment temperature changes slowly according to time). The simple linear regression function is able to work over temporal correlation, but it is not able to work over the multivariate temporal correlation (more than one variable). We propose the use of multiple linear regression function to work over the multivariate correlation.  
Our data reduction solution occurs in a distributed way, where each sensor node calculates coefficients \(\beta\) and \(\alpha\) from the multiple linear regression function (Step #2). Then, it only sends \(\beta\) and \(\alpha\) if there is no multivariate spatial correlation with other neighbor sensor node.  
Coefficients \(\beta\) and \(\alpha\) are not calculated by the simple linear regression as the amount of independent variables is greater. The multiple linear regression is described below:  
\[\beta=\begin{pmatrix}\beta_{0}\\ \beta_{1}\\ \vdots\\ \beta_{j}\end{pmatrix}\text{, }X^{\prime}=\begin{pmatrix}1&1&\cdots&1\\ x_{11}&x_{12}&\cdots&x_{1i}\\ \vdots&\vdots&\vdots&\vdots\\ x_{j1}&x_{j2}&\cdots&x_{ji}\end{pmatrix}\text{, }X=\begin{pmatrix}1&x_{11}&\cdots&x_{1j}\\ 1&x_{21}&\cdots&x_{2j}\\ \vdots&\vdots&\vdots&\vdots\\ 1&x_{i1}&\cdots&x_{ij}\end{pmatrix}\text{and Y}=\begin{pmatrix}\text{y}_{0} \\ \text{y}_{1}\\ \vdots\\ \text{y}_{i}\end{pmatrix}\]  
with:  
\[\beta=(\text{X}^{\prime}\text{X})^{-1}\text{X}^{\prime}\text{Y} \tag{6}\]  
where \(\beta\) represents the vector of coefficients of the multiple linear regression function. We use \(\beta_{0}=\alpha\) for simplicity and compatibility with \(\beta\) and \(\alpha\) coefficients of the simple linear regression. \(X\) is one multidimensional vector, which represents the samples window of the independent variable, together with its transpose vector \(X^{\prime}\). \(Y\) is the one-dimensional vector, which represents the samples window of the dependent variable. \(i=1,...,n\) and \(n\) is the number of samples, and \(j=1,...,k\) where \(k\) is the dimension of vector \(X\).
## 2 Experimental Section
### Volatile Emissions Screening with SPME and GC-MS  
SPME extractions were performed with 75 \(\upmu\)m Carboxen/PDMS fibers (Supelco, Bellefonte, PA, USA). Carboxen/PDMS coating is very efficient at extracting volatile organic compounds (VOCs) with molecular weight between 35 and 220. Other good SPME coating choices are PDMS/divinylbenzene (DVB) and DVB/Carboxen/PDMS. Screw-capped vials (40 mL), sealed with a PTFE-lined silicone septum, were used for storing soybean leaves and for HS-SPME sampling. Vials were placed at 25 \({}^{\circ}\)C and SPME sampling started within 5 min from delivery from the greenhouse or field. Headspace sampling time was 24 h and it was selected to increase the extracted mass of compounds of interest and the number of compounds that could be identified. After extraction, the SPME fiber was removed from the vial and immediately inserted into the GC injection port for complete thermal desorption and analysis.  
Multidimensional gas chromatography-mass spectrometry-olfactory (MDGC-MS-O) (from Microanalytics, Round Rock, TX, USA) was used for all analyses. The system integrates GC-O with conventional GC-MS (Agilent 6890N GC / 5973 MS from Agilent Inc., Wilmington, DE). The system was equipped with a non-polar pre-column (12 m \(\times\) 0.53 mm ID (SGE BP-5+, SGE, Austin, TX, USA) and polar column (25 m \(\times\) 0.53 mm ID polar analytical column (SGE BP20, SGE) in series, as well as system automation and data acquisition software (MultiTraxtm V. 6.00 and AromaTraxtm V. 6.61, from Microanalytics and Chemstationtm, from Agilent). The general run parameters used were as follows: injector, 260 \({}^{\circ}\)C; FID, 280 \({}^{\circ}\)C, column, 40 \({}^{\circ}\)C initial, 3 min hold, 7 \({}^{\circ}\)C/min, 220 \({}^{\circ}\)C final, 10 min hold; carrier gas, He. _m/z_ range was set between 33-280. Spectra were collected at 6 scans/s. The MS detector was auto-tuned weekly.
## 4 Experiments and Discussion
### Landmark Detection Results
#### 4.3.2 Our Dataset  
We use our trained model(use ISBI training dataset) to test our own dataset directly. We also register all images first. Since there are some different landmarks labeled in our own dataset, in the experiment, we only test same parts' landmarks with ISBI dataset. The results are presented in Tables 4 and 5.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{5}{c}{**Our own data from Shandong University**} \\ \hline
**Anatomical Landmarks** & **2 mm (\%)** & **2.5 mm (\%)** & **3 mm (\%)** & **4 mm (\%)** \\ \hline
1. sella turcica & 68.0 & 71.0 & 76.0 & 82.0 \\
2. nasion & 83.0 & 90.0 & 95.0 & 99.0 \\
3. orbitale & 40.0 & 58.0 & 74.0 & 88.0 \\
4. porion & 48.0 & 56.0 & 66.0 & 72.0 \\
5. subspinale & 32.0 & 39.0 & 50.0 & 67.0 \\
6. supramentale & 33.0 & 43.0 & 55.0 & 71.0 \\
7. pogonion & 68.0 & 77.0 & 81.0 & 90.0 \\
8. menton & 82.0 & 86.0 & 95.0 & 99.0 \\
9. gnathion & 91.0 & 97.0 & 99.0 & 100.0 \\
10.gonion & 51.0 & 63.0 & 75.0 & 86.0 \\
11.lower incisal incision & 68.0 & 76.0 & 78.0 & 90.0 \\
12.upper incisal incision & 75.0 & 86.0 & 90.0 & 97.0 \\
18.anterior nasal spine & 67.0 & 74.0 & 81.0 & 85.0 \\ \hline Average: & 62.0 & 70.5 & 78.1 & 86.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of 2 mm, 2.5 mm, 3 mm and 4 mm accuracy on our own dataset, since we do not have label for landmark 13, 14, 15, 16, 17, 19, we test other landmarks exclude these ones.  
Figure 8: Green points: predicted landmarks’ locations. Red points: ground-truth of landmarks’ locations.