esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### ERP Component Analysis  
#### 4.2.1 Heatmap Visualization  
Before building our model, we visualized brainwave changes in the parietal and occipital regions for preference and non-preference stimuli as heatmaps [165]. Because the parietal region is crucial for emotion evaluation and attention, and the occipital region is key for the initial processing of visual stimuli, previous research has shown that observing ERP changes in these areas can provide insights into the neural correlates of emotional responses to architectural stimuli [166; 167; 168].  
Figure 8 presents a heatmap of the average brain activation across all participants, illustrating the differences between preference (a) and non-preference (b) conditions. EEG signals were extracted from selected parietal (Pz, P3, and P4) and occipital (Oz, O1, and O2) channels. In the occipital area, preferred stimuli exhibited a strong positive potential, whereas non-preferred stimuli presented a broader negative potential, indicating differing neural responses to architectural stimuli.  
According to [PERSON] [120] and related research, this pattern reflects approach tendencies for preferred stimuli (positive emotion) versus withdrawal tendencies for disliked stimuli (negative evaluation). In the occipital area, non-preference stimuli yield a pronounced negative potential, suggesting a more intensified selective attention or visual rejection response [129; 168]. Table 12 summarizes the observed activation patterns and corresponding interpretations for parietal and occipital regions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Preferred Stimulus**} & **Non-Preferred Stimulus** & **Interpretation** \\ \hline \multirow{2}{*}{Left frontal/ parietal} & Strong positive activation & Widespread negativity & Reflects positivity versus aversion/avoidance \\ \hline \multirow{2}{*}{Occipital} & Mild positive amplitude & Prominent negative amplitude & Efficient visual acceptance versus rejection \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Parietal and occipital patterns.  
Figure 8: Heatmaps: (**a**) preference heatmap; (**b**) non-preference heatmap.
## 5 Experiments  
We first present the experimental setup in Section 5.1, including the dataset partition and evaluation metrics. Sections 5.2-5.4 contain various experiments that evaluate the effectiveness of our proposed framework, ExoFIA. Specifically, we aim to answer the following research questions:  
* RQ 1. Can our proposed framework, ExoFIA, achieve robust effectiveness in fake news popularity prediction by taking into account multi-modal contents, such as networks, user characteristics, tweets, and trends?
* RQ 2. How critical are exogenous features to improving ExoFIA's prediction performance? Does the use of both diffusion and social networks improve performance?  
Figure 13: Thearchitecture of attention fusion.  
* RQ 3. Can ExoFIA exploit the features extracted from endogenous and exogenous sources to explain why a tweet sharing fake news leads to a massive information cascade? Is there a discrepancy between real news and fake news? To answer RQ 1 and RQ 2, we compared the performance of our model, ExoFIA, to several baselines in Section 5.2.1, followed by a few variants of ExoFIA in Section 5.2.2. In addition, we measured the performance of these models under different observation time windows, as detailed in Section 5.3. To address RQ 3, we explored the differences in feature importance between real and fake news propagation by testing our framework on a dataset of posts sharing'real' news, as seen in Section 5.4.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 5 Analysis  
All four datasets were divided into one-minute (12,000 values) segments for analysis. This resulted in 15 segments for the seizure data, 10 segments for the sleep and sleep to awake data, and 9 segments for the awake data. Of the 15 segments for the seizure data, there were six complete segments preceding seizure onset, one segment containing seizure onset, one segment of seizure, one segment containing the transition from ictal to post-seizure, and six post-seizure segments. The sleep-to-awake data had seven segments of sleep data, one segment containing the transition from sleep to awake and two segments of awake data. For each segment, ARMA-GARCH models were estimated for all 66 channels using MATLAB. The estimated parameters from the models were used to estimate unconditional volatility and half-lives. Confidence intervals for half-lives based on the GARCH coefficients were constructed. Additionally, models were estimated for one-minute segments with randomly selected starting points to see if the models were dependent on the particular segments.
## 3 Materials and Methods
### Feature Extraction Methods
#### 3.3.1 Time Domain  
Most EEG signal acquisition equipment supports time-domain series. These methods mainly consider the geometric characteristics of the EEG signals, leading to a lower loss of information. The time-domain attributes provide valuable information about the statistical patterns in the EEG data.  
The time-domain methods included in this work are statistical functions such as the maximum, mean, standard deviation, variance, skewness, kurtosis, and mean of absolute values of a normalized first and second difference. Entropy functions include Shannon entropy, approximate entropy, sample entropy, permutation entropy, and other functions like energy, average power, root mean square, line length, Higuchi fractal dimension, Petrosian fractal dimension, Hjorth parameters, zero crossing, and higher-order crossing.
# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
###### Abstract
Deep learning is hoped to be a valuable tool in this context, enabling radiologists to handle the growing workload. The goal of this study is to create an image classification model that uses deep-learning techniques to speed up the identification of lung diseases, thereby reducing the effort and time involved in the diagnostic process. This model will aid in effectively identifying patients exposed to COVID-19 and those with pneumonia. The major objective is to reduce mistakes and misdiagnoses in lung disease treatment, thereby improving patient care and outcomes. By enhancing the accuracy and efficiency of disease detection, this study endeavors to contribute to the overall optimization of lung disease management and reduce the potential for mishandling of such conditions. The main contributions of this research are two-fold:  
1. Implementation and selection of the optimal architecture of convolutional neural network (CNN) and long short-term memory (LSTM) deep-learning models for the classification of lung diseases using chest X-ray images. The selection process is based on evaluation metrics and training time, ensuring that the models are efficient and effective in accurately identifying different lung diseases;
2. Addressing the challenge of an imbalanced dataset by applying various image augmentation techniques. Imbalanced datasets, where certain classes have significantly fewer samples than others, can pose challenges in model training. By employing appropriate image augmentation methods, this research aims to improve the performance of the deep-learning models by artificially expanding the dataset and creating a more balanced representation of different lung diseases. The remainder of this paper is structured as follows. Section 2 provides a comprehensive review of the existing scientific literature related to this research. Section 3 outlines the methodology employed in this research, including the collection and splitting of the dataset. Section 4 presents the results obtained from the experiments conducted in this study. Section 5 offers a comprehensive discussion of the findings, conclusions, and recommendations for future research.
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 2 Materials and Methods
### Data Processing Procedures  
We note that among the drift compensation of electronic noses, only one feature optimization is often performed [22], and considering that the drift data may have an impact on the feature optimization itself, we propose a secondary feature optimization in the expectation that the impact of drift on the working state of electronic noses can be further removed with a smaller number of sensors.  
As shown in Figure 3, we compensate the electronic nose based on the way [PERSON] et al. [23] used a BP neural network to compensate the sensors. Specifically, in this experiment, we firstly optimize the original data collected by the electronic nose for the first time, use the random forest to obtain the scores of various types of sensors, and combine the forward selection method to filter out a feature-optimized sensor dataset. This dataset is put into the artificial neural network for compensation to obtain the compensated dataset. Then, the compensated dataset is combined with the original dataset of the remaining sensors from the first feature optimization as a new dataset for the second feature optimization in order to expect to obtain the optimal dataset of the sensor groups and further reduce the influence of the drift factor on the electronic nose. Finally, the results of the correct rate of classification for tea categorization are compared to determine that the approach is effective in improving the classification of the results.  
Figure 2: Neural network compensation flowchart.
Chemical food spoilage, such as lipid oxidation of foods, increases the formation of unhealthy compounds, which adversely affects the sensory properties of foods and contributes significantly to decreased shelf life and food waste. Multiple studies have also shown that oxidized lipids in the diet could affect major diseases (e.g., heart disease and cancer) of food consumers [4; 5]. Various techniques, such as vacuum packaging, modified atmosphere, refrigeration, or inactivation of enzymes catalyzing oxidation, have been developed to control the rate of lipid oxidation in foods. Still, the addition of food additives remains the most effective method to increase the shelf-life of food products without adverse effects on their sensory or nutritional qualities [6]. Synthetic antioxidants such as butylated hydroxyapatite (BHA), propyl gallate (PG), octyl gallate (OG), dodecyl gallate (DG), and butylated hydroxytoluene (BHT) are currently authorized for use in controlling lipid oxidation in foodstuffs. Moreover, sorbic acid and its salts, benzoic acid, and its derivatives are widely used as food preservatives to inhibit bacterial and antifungal growth. However, in recent years, food consumers have been concerned about exposure to synthetic additives in their daily diet, with an increasing preference for natural ones. New sources of antioxidants and preservatives, such as natural extracts and essential oils (EOs), were investigated in the last decades to meet the growing demands of food consumers for cleaner label products [7; 8]. The genus _Nepeta_ (_Lamiaceae_) is native to Asia, Africa, Europe, and North America [9] and includes over 200 species. In Romania, the _Nepeta_ genus is represented by four species: _N. cataria_ L., _N. nuda_ L. ssp. nuda, _N. parviflora_ [PERSON], and _N. urcanica_ L. The last two are rare species located only in the Transylvanian plain (_N. urcanica_ L.) and Dobrogea (_N. parviflora_ [PERSON].) [10]. _Nepeta_ species are EO abundant taxa and are defined by the presence of nepetalactone isomers, the biochemical markers of this genus [9; 11]. Various genus species have been recorded for their biological activities, including antibacterial, antifungal, antioxidative, anti-inflammatory, insecticidal, and antidepressant [9]. However, some of the genus members remain insufficiently explored, such as _Nepeta_\(\times\)_fassensii_ Bergmans ex Stearn, a sterile hybrid of _N. mussimi_ [PERSON], and _N. nepetella_ L. [12]. Until now, no data regarding the antioxidant and antimicrobial properties of the _N. fassensii_ essential oil (NEO) are reported. Based on these considerations, the aim of our study was (a) to investigate the chemical composition and (b) to evaluate the antioxidant and antimicrobial activity of NEO.
# Influence of Metro Track Irregularities on Pantograph Vibration and Its Interaction with Catenary
## 4 Result and Analysis
### Vibration Responses of the Vehicle System  
The vibration responses of the vehicle system and the pantograph are extracted, and the vibration transfer characteristics are analyzed from the frequency domain.  
Comparing the vertical vibration amplitude-frequency responses of the wheelset, bogie, car body, and the pantograph strip (shown as Figure 8a), it can be seen that there are three typical peak values, which are around 0.96 Hz, 1.6 Hz, and 4 Hz, respectively. The wavelength range corresponding to the first peak is 10-20 m. The vibration energy is the largest, and the transmission attenuation from the wheelset to the pantograph is small. The wavelength ranges corresponding to the second and the third peaks are 6-9 m and 3-4 m, respectively. In these two ranges, the attenuation of vertical vibration gradually increases, but the pantograph still has large peak values. Moreover, at higher frequency ranges, although there is larger vibration in the wheelset and bogie, the car body and the pantograph are not affected. The results show that through the energy dissipation of the primary and secondary suspension systems, vibration higher than 5 Hz (corresponding to the wavelength of 2 m) can be significantly attenuated, but the vibrations with the wavelengths of 10-20 m, 6-9 m, and 3-4 m are still prominent in the pantograph. According to Figure 8b, it can be seen that there are two peaks whose wavelengths are 6-12 m and 2-4 m, respectively, in the lateral vibration of the pantograph, corresponding to the first and the third peak of the vertical vibration.  
The dynamic responses when the vehicle runs at 53 km/h were extracted, as shown in Figure 9.  
Figure 8: Vibration responses of the vehicle system at 42 km/h: (**a**) vertical responses; (**b**) lateral responses.  
It can be seen that with the increase in the operating speed, the dynamic responses of the vehicle system increase, and the peak frequency of the vibration also increases. However, for vertical vibration, the wavelengths corresponding to the vibration peaks of the pantograph are still 10-20 m, 6-8 m, and 3-4 m, respectively, which are exactly the same as those at 42 km/h. In terms of lateral vibration, it can be seen that the vibration of the pantograph has increased significantly, and the peaks are concentrated in the two frequency ranges of 9.6-13.4 m and 3-4 m.  
According to the measured track geometry irregularity, it can be inferred that its two broadband peaks at 10-20 m and 3-4 m have a great influence on the vibration of the vehicle system, especially the pantograph strip.
## 2 Materials and Methods
### Evaluation of Volatile Compound Profile  
The VCs of the WW samples were analyzed by gas chromatography-mass spectrometry (GC-MS). A solid phase microextraction (SPME) device with a Stableflex(tm) fibre coated with a 50 \(\upmu\)m PDMS-DVB-Carboxen(tm) layer (Supelco, St. Louis, USA) was used for the analysis. A WW sample was weighed and blended with an aqueous NaCl solution (30% \(w/v\)) in a ratio of 1 g of WW to 3 mL of NaCl solution. For headspace extraction, 8 g of prepared sample was transferred to the 20 mL extraction vial, sealed with a polytetrafluoroethylene septum, and thermostatted at 60 \({}^{\circ}\)C for 15 min before exposing the fibre in the headspace. The fibre was exposed to the headspace of the vial for 10 min and desorbed in an injector liner for 2 min (splitless injection mode). Prepared samples were analyzed with a GCMS-QP2010 (Shimadzu, Kyoto, Japan) gas chromatograph and mass spectrometer. The following conditions were used for the analysis: injector temperature of 250 \({}^{\circ}\)C, ion source temperature of 220 \({}^{\circ}\)C, interface temperature of 260 \({}^{\circ}\)C. Helium was used as the carrier gas at a flowrate of 0.95 mL/min. A Stabilwax-DA capillary column (0.25 mm ID, 0.25 \(\upmu\)m film thickness, 30 m length (Restek, Bellefonte, PA, USA)) was used for the analysis. The temperature gradient was programmed from a starting temperature of 40 \({}^{\circ}\)C (3 min hold) to 220 \({}^{\circ}\)C (6 \({}^{\circ}\)C/min) to 250 \({}^{\circ}\)C (10 \({}^{\circ}\)C/min) (6 min hold). The VCs were identified according to mass spectrum libraries (NIST11, NIST11S, FFNSC2).  
High-Performance Liquid Chromatography Coupled to Triple Quadrupole Mass Spectrometry (HPLC-MS/MS) for Mycotoxin Analysis  
2.7.1. Materials and Chemicals  
Mycotoxin standards were of at least 95% purity. Standards were supplied by Romer Labs (Tulln, Austria) (Ochratoxin A, Fumonisin B1, Fumonisin B2, T2-Toxin, HT-2 Toxin, Zearalenoe, Deoxynivalenol) and Fermentek (Jerusalem, Israel) (Aflatoxin B1). HPLCgrade acetonitrile and methanol (>99% assay), ACS grade formic acid (\(\geq\)96.0% assay) was used. Ultrapure water (18.2 M\(\Omega\)\(\times\) cm) was generated by a Milli-Q system (Millipore, Billerica, MA, USA). QuECHERS buffer-salt extraction kits consisting of magnesium sulphate (4 g), sodium chloride (1 g), trisodium citrate dihydrate (1 g), and disodium hydrogen citrate sesquihydrate (0.5 g) per portion and QuECHERS dSPE kit consisting of magnesium sulphate (900 mg), PSA (primary and secondary amines) (150 mg) and C18E (150 mg) per 15 mL polypropylene tubes were purchased from Phenomenex (Torrance, CA, USA). Unprocessed wheat was used as a blank sample.
###### Abstract  
This paper presents experimental identification and vibration suppression of a flexible manipulator with piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined where the move distance of the closed loop poles is the shortest. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Accordingly, multi-mode vibrations of the manipulator are well attenuated.  
Keywords:experimental identification; multi-poles placement control; piezoelectric flexible manipulator; active vibration control +
Footnote †: journal: Applied
# Thermal Layer Design in Fused Filament Fabrication
## 4 Discussion
### Application and Significance of Findings  
Using TLD on the component to improve the thermal properties in each layer resulted in better heat distribution. However, the peak temperatures were lower due to a longer printing time for the larger layer, providing more even properties through the cross-section of the TLD component compared to the original design, as seen in Figure 10. As the thin-walled sections are well below the low-end temperatures of Figure 5, they would not contribute to the component strength in a way one would expect from an engineering material such as PA6-CF. The initial thin-walled design allows the layers to cool down to the extent that the mechanical advantages of PA6-CF over commodity materials are lost. Although these simple geometry changes did not result in an optimal layer temperature, they clearly show the effects on layer temperature distribution.  
With the increasing availability of both machines and AM-tailored engineering polymers, the prospect of customized or low-volume production with FFF becomes a reality. However, the design of components must account for aspects that are generally not considered when using commodity polymers, such as the layer temperature. By implementing TLD into the DfAM process, uniform properties can be achieved throughout an entire component or critical areas can be strengthened while allowing for faster print speeds in noncritical sections.
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 5 Conclusions  
This paper was devoted to the prediction of cellular network traffic. We conducted in-depth research on the spatiotemporal correlation of cellular networks and analyzed various factors that affect traffic changes, then proposed a hybrid deep learning model for traffic spatiotemporal prediction:  
* This work used DenseNet with deformable convolution to extract the spatiotemporal characteristics of traffic.
* We introduced hour and holiday information to aid traffic forecasting.
* We proposed an attention module based on historical data to adjust the weight of the predicted traffic.  
The experimental results showed that compared with the existing methods, the hybrid spatiotemporal network HSTNet proposed in this paper could better extract the spatiotemporal characteristics of image traffic data and improve the prediction accuracy, thus making more effective traffic prediction.  
There are still many aspects that need improvement in our work:  
* The model did not have a good ability to respond to fluctuations caused by emergencies.
* The forecast performance of the large scale traffic volume (total traffic volume of the entire city) needs to be improved.
* There are many external factors that we did not consider that could have a potential impact on cellular traffic changes.  
In future work, traffic prediction modeling needs to consider not only the use of more sophisticated networks to extract features, but also the analysis and introduction of external data in multiple dimensions. It is worth mentioning that the introduction of our cellular traffic prediction scheme into traffic prediction problems in other similar contexts is also a very worthwhile research direction.  
Conceptualization, D.Z.; data curation, B.Y.; formal analysis, L.L.; funding acquisition, D.Z.; writing, original draft, D.Z. and L.L.; writing, review and editing, C.X. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant Numbers 61402397, 61263043, 61562093, and 61663046.  
**Acknowledgments:** This work is supported by: (i) the Natural Science Foundation China (NSFC) under Grant Nos. 61402397, 61263043, 61562093, and 61663046; (ii) Yunnan Provincial Young academic and technical leaders reserve talents under Grant No. 2017 HB005; (iii) the Yunnan Provincial Innovation Team under Grant No. 2017H012; and (iv) the Youth Talents Project of the China Association of Science and Technology under Grant No. W8193209.  
The authors declare no conflict of interest.