esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mathematical Model
#### 3.2.4 The Effect of the Milligram Equivalent of Other Ions (E)  
Figure 5 presents the effects of the milligram equivalent of other ions (E) under toluene mineralization on carbonate dissolution and precipitation. This section assumes that nitrate is entirely transformed into nitrogen, the consumption of microbial growth is 6.5%, and the temperature of the system is 20 \({}^{\circ}\)C.  
Figure 4: Variation in ion concentration with temperature. (**a**) Variation in H\({}^{+}\) with toluene concentration; (**b**) variation in HCO\({}_{3}\)\({}^{-}\) with toluene concentration; (**c**) variation in Ca\({}^{2+}\) with toluene concentration.  
Figure 3: Variation in ion concentration with the proportion of toluene consumed by microbial growth to the total toluene content. (**a**) Variation in H\({}^{+}\) with toluene concentration; (**b**) variation in HCO\({}_{3}\)\({}^{-}\) with toluene concentration; (**c**) variation in Ca\({}^{2+}\) with toluene concentration.  
Based on the ion concentration in groundwater, Figure 5 shows the influence of complete mineralization on calcium carbonate when E is 0 mol/L, 0.001 mol/L, and 0.002 mol/L. According to the given information, it appears that the trend of ion concentration change is the same in all three cases, as the reaction mechanism is identical. As can be seen from the figure, the smaller the value of E is, the greater the concentrations of H\({}^{+}\) and HCO\({}_{3}\)\({}^{-}\) in the corresponding solution at the initial concentrations.  
The amplitude of variation in the H\({}^{+}\) and HCO\({}_{3}\)\({}^{-}\) concentrations increases as the value of E decreases. The change in Ca\({}^{2+}\) concentration is slightly different from that of the above two ions. E affects the initial concentration of calcium ion as follows: the larger the value of E is, the smaller the initial concentration. Moreover, E has an impact on the change trend of the Ca\({}^{2+}\) concentration. When E is 0, the Ca\({}^{2+}\) concentration always increases; when E is 0.002, the Ca\({}^{2+}\) concentration always shows a downward trend; when E is 0.001, the Ca\({}^{2+}\) concentration shows a trend of first decreasing and then increasing, and there is an inflection point near a toluene concentration of \(7\times 10^{-5}\) mol/L. The variation in Ca\({}^{2+}\) concentration is complex, possibly due to the complex relationship between Ca\({}^{2+}\) and H\({}^{+}\) in the model.
# Structural Damage Detection through Dual-Channel Pseudo-Supervised Learning
## 4 Experimental Verification on a Four-Story Steel-Frame Structure
### Experimental Description  
In Figure 8, the experimental model is a four-story single-span steel-frame structure. The model is 320 mm \(\times\) 260 mm in plan size, with an overall height of 672 mm. The height of each floor is 168 mm. All floor slabs in the model are constructed from 16 mm-thick steel plates, while the columns are made of solid round steel with a height of 152 mm and a cross-sectional diameter of 16 mm. All structural elements are made of Q235 steel, with a nominal yield stress of 235 MPa. To induce structural vibrations, a Donghua DH40100 (DongHua Testing Technology Co., Ltd., Jingjiang, China) electrodynamic exciter was used to apply white Gaussian noise excitation along the southeast-northwest diagonal on the first floor of the model, and the duration of the excitation was 800 s. The structural response was measured by the Donghua 1A401E acceleration sensors, and these sensors were instrumented at midspan positions on each of the four edges of each floor plate. The acceleration data were acquired using the Donghua DH8303 data acquisition system. In this study, Gaussian white noise excitation was used as the excitation signal to induce structural vibrations, and the resulting acceleration response of the structure served as the measured signal.  
In this study, three types of columns with different cross-sectional diameters were employed, as depicted in Figure 9. The structural damage was achieved by replacing original columns at the southeast corner of one or more floors with columns that have a cross-sectional diameter of 14 mm or 12 mm. We simulated a total of six different damage scenarios, which are fully listed in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Damage Case** & **Description** \\ \hline D.P.0 & Without damage (the columns at the southeast corner of floors 1–4 all \\  & have a diameter of 16 mm). \\ D.P.1 & Replaced the column on the first floor with a 14 mm-diameter column. \\ D.P.2 & Replaced the column on the second floor with a 14 mm-diameter column. \\ D.P.3 & Replaced the column on the third floor with a 14 mm-diameter column. \\ D.P.4 & Replaced the column on the fourth floor with a 14 mm-diameter column. \\ D.P.5 & Replaced the columns on the first and second floors with 14 mm- and 12 mm-diameter columns, respectively. \\ \hline \end{tabular}
\end{table}
Table 6: Damage scenarios as specified in the experimental structure.  
Figure 7: Confusion matrices of the Conformer+Conformer model at different noise levels.
# Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors
###### Abstract  
We investigate a case of automated energy-budget-aware optimization of the physical position of nodes (sensors) in a Wireless Body Area Network (WBAN). This problem has not been presented in the literature yet, as opposed to antenna and routing optimization, which are relatively well-addressed. In our research, which was inspired by a safety-critical application for firefighters, the sensor network consists of three nodes located on the human body. The nodes communicate over a radio link operating in the 2.4 GHz or 5.8 GHz ISM frequency band. Two sensors have a fixed location: one on the head (earlobe pulse oximetry) and one on the arm (with accelerometers, temperature and humidity sensors, and a GPS receiver), while the position of the third sensor can be adjusted within a predefined region on the wearer's chest. The path loss between each node pair strongly depends on the location of the nodes and is difficult to predict without performing a full-wave electromagnetic simulation. Our optimization scheme employs evolutionary computing. The novelty of our approach lies not only in the formulation of the problem but also in linking a fully automated optimization procedure with an electromagnetic simulator and a simplified human body model. This combination turns out to be a computationally effective solution, which, depending on the initial placement, has a potential to improve performance of our example sensor network setup by up to about 20 dB with respect to the path loss between selected nodes.  
body area networks; wireless sensor network; computer optimization algorithms; computational electromagnetics +
Footnote †: journal: Sensors
## 2 Related Work  
The rapid expansion of 5G-enabled IoT infrastructures has introduced complex security challenges, stemming from the massive number of heterogeneous, resource-constrained devices and their exposure to dynamic, often hostile environments [4; 14]. Traditional cloud-based security architectures face scalability and latency limitations, making them inadequate for real-time protection in latency-sensitive domains such as healthcare, transportation, and industrial IoT (IIoT) [15]. Recent research has highlighted the urgency of decentralized and autonomous approaches for cybersecurity in such contexts [11].
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 5 Discussion
### Individual Performance Metrics  
Individual faults, studied in Section 4.2, witness the most advanced predictions from T2V-LSTM due to its best MIRs and F-scores across most test cases over time. T2V-LSTM has mostly better prediction scores compared to vanilla LSTM, and its resultant F-scores are well adjusted due to its more balanced MIPs and MIRs across Figures 9-12. Regarding the fault studies in Appendix A, T2V-LSTM catches over 86.27% of fault cases and over 88.28% of fault-free cases.  
HPU 2 pump active faults exhibit a satisfactory percentage of caught faults via MIRs, mostly over 80%, as seen in Figure 9. PcsOff faults have both excellent MIPs and MIRs over 89% before \(t-16\), but the predicted MIRs have significant relegations by scoring 0.826087 at \(t-18\) and 0.717391 at \(t-20\), as seen in Figure 10.  
Under T2V-LSTM, all above-mentioned faults have balanced precision and recall scores, but PcsTrip and pitch fatal faults see curtailed predictions over time and greater MIPs than MIRs, resulting in both their F-scores downgraded by poorer MIRs. Fewer PCsTrip faults are correctly predicted over time regarding its maximum MIR (88.88%) and minimum MIR (75.30%) at timestamps \(t-3\) and \(t-18\), respectively, as seen in Figure 11. It is noticeable that the MIRs on pitch fatal faults are even poorer, deteriorating initially from 69.48% to 44.15% over time, as seen in Figure 12. The forecasts over 40 min ahead on pitch fatal faults show poor results with the least MIRs (below 60%) among all individual faults, and over half of the faults cannot be correctly diagnosed after \(t-17\).  
T2V-LSTM under a non-stationary _Tanh_ function shows its peak effectiveness for both overall and individual fault predictions, according to its overall best accuracy, recall scores (both MARs and MIRs), and F-scores. However, the significant mitigations in accuracy, MARs, and F-scores from Figure 8 are mainly reflected by the attenuated MIRs and F-scores from pitch fatal faults since \(t-4\) (40 min) in Figure 12,\(c\).
# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
## 2 Materials and Methods
### Cloud Based Short Term Forecasting Model: Epidemic Analysis  
In this work, we developed a short-term forecasting model that predicts the severity of COVID-19 using various cloud services. The real-time results of this tool are evaluated and broadcasted by our developed web application named COVID-19: Updates, Forecasts and Assistant ([[https://corona.nstu.edu.bd/](https://corona.nstu.edu.bd/)]([https://corona.nstu.edu.bd/](https://corona.nstu.edu.bd/)), accessed on 21 January 2021). However, it is not a static analysis where the training and forecasting cases of this tool have been updated every 24 h. The working pipeline of the short-term forecasting model is given as follows (see Figure 1):  
* Firstly, this web tool gathers the daily cumulative instances of confirmed infection and fatality cases of Bangladesh at the Github repository of the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (see Section 2.1).
* In this work, we gathered the daily cumulative instances from 8 March 2020 to 28 November 2020 where the confirmed infection and fatality cases were investigated to assess the severity of the pandemic in Bangladesh. This whole period was split into several windows using sliding window techniques [40; 41]. The size of sliding window was fixed (32 days) and each of them is called round. Instances were identified from the last 25 days where 85% of the data were used as the training set and 15% of the data were used for the the test set. Thus, it analyzes the confirmed infection and fatalitycases and forecasts them for the next couple of days as a requirement. For instance, we predicted the next 7 days cases from the training period in this work. Besides this, we considered 35 rounds where the first 10 and last 5 rounds were presented for the next 7 days of future forecasting.
* The primary web mining model was executed on the Google Colab platform. Raw data were loaded and applied using different machine learning regression models which are described in Section 2.2. To explore the best results, parameters need to be estimated and the highest outcomes from them need to be found [27]. However, choosing the optimal parameters is a challenging task for any machine learning procedure. In this work, we manually trained models with various parameters and identified the best model from them.
* To evaluate the performance of different regression models, we used several metrics such as MAE, RMSE and \(R^{2}\) values (see details in Section 2.4) for evaluating the test set and identifying the best model for predicting cumulative confirmed infection and fatality cases with the lowest error rate.
* All actual and predicted trajectories have been placed in our web tool which is uploaded by the local cloud host via plotly Chart Studio.
## 2 Data and Methodology
### Research Methodology
#### 2.3.2 NDVI Dynamic Attribution and Driving Mechanisms Analysis  
A multi-scale analytical framework was employed to investigate vegetation dynamics drivers. Spatiotemporal NDVI trends were rigorously assessed through the integrated application of Sen's slope estimator for magnitude quantification and the Mann-Kendall test for statistical significance evaluation [46; 47]. Additionally, the NDVI time series is decomposed into trend (T\({}_{t}\)), seasonal (S\({}_{t}\)), and residual (R\({}_{t}\)) components using the Seasonal-Trend decomposition procedure based on Loess (STL)--a robust method for intuitive exploration of time-series data. This technique, successfully applied in visual analytics for temporal pattern identification, event detection, and trend forecasting, effectively separate the vegetation dynamic characteristics at different temporal scales [48; 49].  
A dynamic attribution model for vegetation cover was developed, grounded in the residual trend analysis framework [50]. Within the East Asian monsoon-influenced Yellow River Basin, precipitation (P) and temperature (T) exhibit high temporal correlation, potentially destabilizing multiple linear regression models. We therefore employed robust PCA regression, which maintains accuracy while enhancing computational efficiency for large-scale per-pixel spatial modeling. Validation on sample pixels confirmed the first principal component typically captures >90% of original climatic variance. Thus, PCA was applied to precipitation (P) and temperature (T) to extract PC1 as the integrated climatic state [32; 51].  
\[PC1=\lambda\cdot P+\mu\cdot T \tag{2}\]  
We then established a Linear Regression Model for NDVI Climate Response to calculate the climate-driven predicted value (NDVI\({}_{\text{pre}}\)):  
\[NDVI_{pre}=a\cdot PC1+b \tag{3}\]  
Then, we calculated the residuals between the observed NDVI (NDVI\({}_{\text{obs}}\)) and the NDVI\({}_{\text{pre}}\).  
\[NDVI_{\text{res}}=NDVI_{obs}-NDVI_{pre} \tag{4}\]  
The residual trend (NDVI\({}_{\text{res}}\)) was attributed to the impact of human activities primarily driven by afforestation projects. Six types of driving scenarios were established based on the combination of trend directions (Table 1). The relative contributions of climate change and human activities were quantified using the slope ratio method, where S\({}_{\text{NDVI}_{\text{obs}}}\), S\({}_{\text{NDVI}_{\text{pre}}}\) and S\({}_{\text{NDVI}_{\text{res}}}\) denoted the Sen's slope values for the observed vegetation trend, climate-driven trend, and residual trend (human-influenced component), respectively [32; 51].
## 3 Results and Discussion
### Results
#### 3.1.2 PCDD/Fs and dIPCBs
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Area** & **Area Description** & \begin{tabular}{c} **Sampling** \\ **(Voxel/Phase)** \\ \end{tabular} & \begin{tabular}{c} **ZFCDD/fs** \\ **(fg m-3)** \\ \end{tabular} & \begin{tabular}{c} **WHO** \\ **(TQ kg m-3)** \\ \end{tabular} & \begin{tabular}{c} **WHO** \\ **(TQ kg m-3)** \\ \end{tabular} & \begin{tabular}{c} **LindPCBs** \\ **(fg m-3)** \\ \end{tabular} & \
\begin{tabular}{c} **ZFAHs** \\ **(mg m-3)** \\ \end{tabular} &
\begin{tabular}{c} **Reference** \\ **Reference** \\ **Reference** \\ \end{tabular} \\ \hline \hline Elenis, Greece & Industrial & 2018-2019P & 145-3027 & 287-2506 & 975-4083 & 53.1-812 & 22.9-532 & 1.3-165 & This study \\ \hline Athens, Greece & Background/Urban & 2002P & 73-426 (P) & 73 (P + G) & & & 1.79-44 & [32] \\ \hline Thessaloniki, Greece & Semirany/Urban & 1999P & 150-12,890 & 4-119 & & 0.5-29.2 & & [53] \\ \hline Porto, Portugal & Urban/Industrial & 1999P-2004P + G & 200-15,000 & 9-817 & & & & [63] \\ \hline Rome, Italy & Urban & 2002-2001P & & 5.43-724 & 0.66-728 & 88.9-372 & & [54] \\ \hline Steel Complex, Korea & Industrial & 2006C & & 6100-61,800 & & & [61] \\ \hline Amban, China & Industrial & 2008C & 23-0790 & & 4560 & 0.3-23 & & [58] \\ \hline Satellite of Seoul, Korea & Industrial & 2003-2008P + G & 300-55,755 & 310-3413 & 678-40,968 & 200-172 & & [64] \\ \hline Bronco, Italy & Urban/Industrial & \(P\) & 380-11,390 & 10-190 & 11,600-708,400 & 4-130 & 92.7-8566 & & [23] \\ \hline Bino, Czech Republic & Urban & 2009-2010P & 43-461 & & 142-614 & & 0.00-14.44 & [56] \\ \hline Aliaga, Turkey & Industrial & 2009-2010P & & & & 1.6-838 & [21] \\ \hline Shanghai, China & Industrial & 2013P + G & 288-4928 & 9.2-423 & 340-7607 & 1.68-55.9 & & [58] \\ \hline Danilik, France & Industrial & 2008-2008P & 718-1070 & 12,990-25,420 & 744-1767 & & 0.29-195 & [31] \\ \hline Isaubel, Turkey & Industrial & 2011-2012P + G & & 0.7-27.9 & & & 0.04-445 & [59] \\ \hline Urum Al-Abi, Krawati & Industrial & 2014-2015P + G & 31,516 & 3.9-36.8 & & [20] \\ \hline Hochimiimi, Vietnam & Urban & 2016-2017P + G & 410-52,700 & 22.2-150 & 1507-8300 & 1.77-44.1 & & [62] \\ \hline Tibet-Qingbai Plaseon, China & Industrial & 2015C- & 1180-2180 & 113-342 & 490-900 & 3.18-71.2 & & [4] \\ \hline Jiangsu, China & Industrial & 2018P + G & 11,500-57,700 & 136-597 & 508-2710 & 0.64-11.3 & 11-18 & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Worldwide concentrations of persistent organic pollutants (POPs) according to literature in comparison with this study. (\(p\) refers to particulate phase and G to gas phase samples).
## 1 Introduction  
Intelligent image and optical information processing have paved the way for the recent epoch of the new intelligence and information era. Certainly, information acquired through the use of various imaging techniques is of tremendous value; thus, an intelligent analysis of them is necessary to make the best use of it.  
The objectives of intelligent processing range from the refinement of raw data to the symbolic representation and visualization of the real world. The image and optical information are acquired through various sources and imaging methods [1]. Thus, extracting and manipulating the descriptive features are essential for such a task [2]. It comes through unsupervised or supervised learning based on statistical and mathematical models or computational algorithms [3, 4]. With recent advances in computing power and learning algorithms, many applications have become more practical and further development is expected [5].  
This Special Issue focuses on the intelligent processing of images and optical information. Various aspects of intelligent processing are investigated for object recognition, image fusion, and optical positioning. Supervised learning based on convolutional neural networks (CNNs) is becoming a popular approach that has revolutionized image classification. CNNs have proven suitable for analyzing visual information due to their ability to learn essential features directly from pixel-level data. Image fusion effectively combines multiple images into a single composite image to upgrade the quality of the pixel. Optical positioning or visible light positioning is a powerful tool that utilizes visible light signals for the precise positioning of an object or device in indoor environments.  
Four research papers and one review paper were verified through a thorough review process. Many valuable and recent technologies are provided to solve the real problems in the selected papers. The third volume of the Special Issue on the topic is closed. It is anticipated that the scope of intelligent processing will be even broader in the future.
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## Materials and Methods
### Gene Electrotransfer and Cell Survival after Gene Electrotransfer  
A volume of 200 uL of cell suspension with different concentrations of pDNA was exposed to electric pulses in electroporation cuvettes with a 2 mm gap (VWR International, Radnor, PA, USA). After pulse delivery, 50 uL of fetal bovine serum (Sigma Aldrich, Darmstadt, Germany) was added to each cuvette, and cells were incubated 5 min at 37 \({}^{\circ}\)C [61; 62]. After incubation, 1 \(\times\) 10\({}^{5}\) 1306 cells and 5 \(\times\) 10\({}^{4}\) C2C12 cells from cuvette were transferred to a 24-well plate (TPP, Trasadingen, Switzerland) and seeded in growth medium for gene electrotransfer determination. From the rest of the cell sample in cuvette, 1 \(\times\) 10\({}^{4}\) C2C12 myoblasts or 2 \(\times\) 10\({}^{4}\) 1306 fibroblasts were seeded in triplicates in 100 uL of growth medium in a 96-well plate (TPP, Trasadingen, Switzerland) to determine cell survival. Both plates were incubated for 24 h (37 \({}^{\circ}\)C, 10% CO\({}_{2}\) for C2C12 and 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) for 1306).  
After incubation, cells in 24-well plates were trypsinized and resuspended in 200 uL of PBS. Percentage of GFP-positive cells and median fluorescence intensity (MFI) of GFP-positive cells were detected using flow cytometer (Attune NxT, Carlsbad, CA, USA) with a blue laser at 488 nm and a 530/30 nm bandpass filter. At every measurement, 10,000 events were recorded. Data obtained were analyzed with the Attune NxT software (version 3.1.2).  
Cell survival was determined with the MTS-based Cell Titer 96 AQqueous One Solution Cell Proliferation Assay (Promega, Madison, WI, USA). After incubation, 20 uL of MTS reagent was added to each well, and cells were incubated for an additional 2 h in the incubator (37 \({}^{\circ}\)C, 10% CO\({}_{2}\) for C2C12 and 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) for 1306). Absorption at 490 nm wavelength was measured with a Tecan Infinite M200 spectrophotometer (Tecan, Switzerland). An average absorption obtained in the samples containing only growth medium was subtracted from the absorption measured in cell samples. To calculate the percentage of viable cells the absorption of each sample was divided by an average absorption of the control samples.