esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 3 How EIT Works
### Forward Problem and Data Collection  
The first step for solving the forward problem is to collect data from the subject's body area under examination. A common EIT system uses a number of electrodes that are connected to the exterior surface of the patient body area under monitoring or examination. Current or voltage is applied to the body and the resulting current or voltage is calculated. Typically, the current is applied from an alternating current (AC) source to a few electrodes and the measuring voltages are calculated to form the remaining electrodes.
## 3 Proposed Methods
### Taillight Detection
#### 3.1.1 HSV Color Thresholding  
In the HSV color thresholding step, the input image is initially converted into an HSV-color space format. Then, the HSV threshold values derived in [12] are applied so that the red points surrounding each candidate taillight are preserved, while the others are filtered out as black, as illustrated in Figure 5. Subsequently, each point that satisfies the above threshold is called an interesting point, while the others are treated as background points.  
Figure 4: Proposed taillight detection method.
## 4 Numerical Examples
### Example 4  
The final example is the modified Degasperis\(-\)Procesi equation [2; 3]. The exact solution is given as follows:  
\[u^{ext}(x,t)=-\frac{15}{8}\text{sech}^{2}\bigg{(}\frac{x}{2}-\frac{5t}{4} \bigg{)}, \tag{28}\]  
The boundary condition and initial condition can be obtained from Equation (28). The following parameters are used: \(a\) = -10, \(b\) = 10, dt = 0.5, \(T\) = 2, \(N_{D}\) = 4, \(N_{T}\) = 37,231, and \(n_{s}\) = 20. The numerical solutions and data are given in Figure 8 and Table 8, respectively. Due to the similarity between the modified Degasperis\(-\)Procesi equation and the modified Camassa-Holm equation, the numerical solutions' behavior is also similar. These comparisons show that the proposed ST-GFDM consistently yields accurate numerical results, as demonstrated in the previous numerical examples. As described earlier, the proposed ST-GFDM effectively solves the shallow water wave problem and demonstrates stability in simulating problems over long time scales.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(x\) & \(t\) & \(u^{ext}\) & **ST-GFDM** & **VIM [2]** & **MAEs** \\ \hline
6 & 0.05 & \(-0.021795977\) & \(-0.021798486\) & \(-0.019790189\) & \(2.508360\times 10^{-6}\) \\
8 & 0.05 & \(-0.002963750\) & \(-0.002960395\) & \(-0.002682964\) & \(3.355673\times 10^{-6}\) \\
6 & 0.1 & \(-0.024074444\) & \(-0.024070611\) & \(-0.019848303\) & \(3.832791\times 10^{-6}\) \\
8 & 0.1 & \(-0.003275195\) & \(-0.003265414\) & \(-0.002684058\) & \(9.781412\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 7: Comparisons of the numerical results of Example 3 with the exact solution and the other numerical methods at various locations at \(t\) = 0.05 and \(t\) = 0.1.  
Figure 7: **(a)** The comparisons of the \(u^{ext}\) and \(u^{mm}\) at different \(t\) and (**b**) the surface plot of \(u^{mm}\) in the entire \(x\)-\(t\) domain of Example 3.
# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 4 Results and Discussion  
### Assessment of Models  
In this study, KNN, SVM, NN, CART, and CHAID models were applied for PR prediction. Each of them has been conducted several times based on their most effective parameters. To evaluate the prediction performance of the mentioned techniques, the established TBM dataset was split into two sections of training and testing. Therefore, 209 cases of the data were separated into 167 and 42 as train and test sections and then the model constructions were conducted to forecast the TBM PR.  
The values of the performance indices for the proposed KNN, SVM, NN, CART, and CHAID models were calculated and are presented in Table 4. These results are based on testing and training datasets and using the simple ranking technique (as described earlier). Table 4 shows that the KNN model has the highest total rate (25) among the models of the training dataset. For the testing dataset, the NN model has the highest total rate, amounting to a value of 24. The total performance ratings of the models for both training and testing datasets are also indicated in Table 5. According to this table, the KNN model obtained the highest total performance rating, which is 41 (25 for training and testing datasets).  
Figure 5: The detailed flowchart of this study in predicting TBM penetration rate (PR).  
16 for testing). The following sub-section provides a more detailed analysis of the KNN model for PR prediction.
## 3 Solution Methodology
### Encoding for Solving the Proposed Optimization Problem  
To address the studied optimization problem, six metaheuristic techniques were implemented, with the PGA as the primary strategy. As previously mentioned, the remaining methods used for comparison included the MC algorithm, PSO, JAYA, GNDO, and MVO. These techniques were selected due to their proven effectiveness in previous research, particularly when it comes to solving nonlinear and multi-nodal problems.  
Figures 3 and 4 illustrate the encoding scheme adopted for the active and reactive power injection of the WTs, respectively. These are per-unit values (p.u.) and are bound by the technical operating limits of both the turbines and their power electronic converters, as defined in Equations (10), (12) and (13). The active power range is determined by the wind profile at each hour and the rated capacity of the turbines, with the minimum value typically set to zero, representing shutdown conditions. The reactive power is constrained by the converter's remaining capacity after satisfying active power requirements. This ensures that the total apparent power does not exceed the converter's nominal rating.  
Figure 5 presents the complete encoding structure used in the optimization process. This structure integrates both active and reactive power decision variables into a single vector of size \(2\times(NH\times NWT)\), where \(NH\) denotes the number of hours in the optimization horizon (24 in this case) and \(NWT\) the number of WTs. In the case under study, this results in a total of 144 continuous variables. Each individual (candidate solution) generated by the metaheuristic algorithm is initialized and constrained within the feasible power limits defined for the WTs and their converters, ensuring that the operational boundaries are respected from the outset of the optimization process.  
This codification is essential for maintaining solution feasibility throughout the process and guiding the search toward realistic and implementable results. Moreover, the nonlinear and high-dimensional nature of the problem arising from the interdependence between power injections and grid constraints makes it particularly challenging to solve using conventional optimization techniques. In light of this, advanced metaheuristic algorithms are employed to efficiently explore the solution space and identify high-quality operational strategies for the MG.  
Figure 4: Reactive power component of the WTs.  
Figure 5: Complete encoding of the optimization problem.  
Figure 3: Active power component of the WTs.  
To assess the performance of the metaheuristic methods used, a comparative analysis was conducted. This analysis evaluates how well each method minimized the operating costs, energy losses, and \(CO_{2}\) emissions of the MG, which helped to identify the most suitable optimization approach for systems of this type and for specific operational scenarios.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Application of SPME Arrow Method for the Analysis of Milk Samples  
The developed SPME Arrow method was employed for the analysis of thermally treated milk samples obtained from the local market in Vienna, Austria. The results are shown in Table 1. Each sample was analyzed in triplicate. As it can be observed, a wide variety of ketones, aldehydes, alkanes, alkanes, carboxylic acids and terpen were detected in the milk samples. The content of each compound was semiquantified and their relative concentrations were determined based on the concentration of the internal standard. The major compounds were acetone, 2-butanone, 2-pentanone, 2-heptanone and 2-nonane. Hexanal, heptanal and nonanal were the major aldehydes that were detected in the milk samples. Most of these compounds have also been previously detected in milk samples [4]. The significant presence of alkanes in the milk samples can be attributed to the thermal treatment that the milk was subjected to in order to extend its shelf-life. The presence of these compounds may increase in high temperatures by hemolytic cleavage of carbon-carbon bonds along the fatty acid chain [19]. Furthermore, terpen that were detected in milk samples were \(\alpha\)-pinene, \(\alpha\)-Cymene,  
Figure 7: Reproducibility comparison for CAR/PDMS SPME Arrow fiber and conventional CAR/PDMS SPME fiber.  
3-carene and \(\alpha\)-humulene. Trepenes have been also previously detected in dairy products [4,19]. Finally, compounds such as styrene can be attributed to potential migration of the packaging [20].
## 2 Test Procedures
### Test and Monitoring Methods  
Direct tensile, tensile-shear, and compressive-shear tests were performed using a self-developed multifunctional test system [24]. This work tested six sets of specimens, and the test conditions are provided in Table 2. A normal tensile force was applied to the specimen in the direct tensile tests, and the specimen was loaded at a rate of \(-0.5\) kN/s until failure. The compressive-shear tests and the tensile-shear tests, which were conducted under constant normal stress of \(5\) MPa and \(-0.15\) MPa respectively, corresponded to roughly \(20\%\) of the uniaxial compressive and tensile strength of specimens, respectively. The usual load was applied at a loading rate of \(0.5\) kN/s. When the normal load reached the preset value, the specimen was loaded laterally with displacement control at a rate of \(1\) mm/min until it broke.  
This study employed a PAC Express-8 AE instrument to monitor the AE signals. The monitoring system can achieve a 16-bit resolution at a sampling rate of up to 10 MSPS. The monitoring system uses an AE sensor (Nano-30) that has an operating frequency range of \(150\)-\(400\) kHz, with a resonance frequency of around \(300\) kHz and a frequency variation of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Uniaxial Compressive** & **Uniaxial Tensile** & **Elastic Modulus** & **Poisson’s** & **Rock** & **Internal Friction** \\
**Strength (MPa)** & **Strength (MPa)** & **(GPa)** & **Ratio** & **Cohesion (MPa)** & **Angle of Rock (\({}^{\circ}\))** \\ \hline \hline
18.29 & 0.77 & 4.76 & 0.18 & 5.45 & 36.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the rock-like materials.  
Figure 1: Rock-like material specimens. (**a**) The preparation process; (**b**) the intact specimen; (**c**) the specimen with a joint persistence of \(50\%\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Type** & **Rock Specim Number** & **Normal Stress/MPa** & **Joint Persistence (JP)** \\ \hline Tensile test & T1 & / & 0\% \\  & T2 & / & 50\% \\  & TS1 & \(-0.15\) & 0\% \\  & TS2 & \(-0.15\) & 50\% \\ Compressive-shear test & CS1 & 5 & 0\% \\  & CS2 & 5 & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test conditions.  
around 20 kHz. The couplers used were those supplied by Physical Acoustics USA, which were ideally suited to the task. The specimen's back surface had an AE transducer attached to it, which touched the specimen through a coupling agent and was held in place by tape. Acquisition of the AE data as well as filtering and postprocessing of the signals were performed on an AE mainframe supplied by Physical Acoustics USA, which has its own filtering capabilities and analytical functions such as AE amplitude and peak frequency analysis. The sampling frequency was 2 MHz, and the threshold value was 40 dB. Figure 2 depicts the test and monitoring equipment.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results  
### Impact of Ground Truth Peak Cleaning: CleanerPPG  
To lay foundation for the rest of the experiments in this paper, we first study the impact and value of evaluating rPPG methods against hand-cleaned ground truth. Figure 5 provides examples of the kind of artefacts that plague raw ground truth PPG/ECG signals resulting in incorrect peak detections. To provide a more qualitative analysis, we measure the agreement between the raw ground truth peaks (obtained via a gradient-based peak detector [49]) and the hand-cleaned ground truth peaks from CleanerPPG. We do this by computing the mean absolute error/difference between their computed HR and HRV values on all datasets. The results of this study can be seen in Table 1.  
Figure 5: Examples of peak detection on artefact-prone raw ground truth PPG signals. Spikes in the amplitude result in false peak detections while some regions of the signal are attenuated resulting in true peaks not being detected.  
On average over all datasets, the heart rate values computed from the raw ground truth peaks deviate from the clean peaks in the range of 0.01 to 4.3 bpm, with almost all datasets containing videos that deviate over 10 bpm. For some datasets (VicarPPG [1&2], UBFC, MAHNOB, PURE, COHFACE, VIPL, DEAP), the raw PPG/ECG signals provided are already of fairly good quality with few artefacts, resulting in a relatively small average deviation from the cleaned peaks (less than \(\sim\)0.5 bpm). On the other hand, many datasets exhibit significant deviations from the cleaned ground truth (MoLi-PPG, StableSet), with some diverging quite heavily (EatingSet, MMSE-HR, ECG-Fitness). Similar but larger deviations are also observed for HRV (RMSSD) calculations, which is much more sensitive to fine errors in peak detection.  
These deviations can be problematic when comparing/benchmarking the performance of rPPG methods against each other. This is especially the case when the gap in performance between methods is small. For example, the raw ground truth of PURE deviates from cleaned ones by 0.36 bpm. While small, this is significant since the top two state-of-the-art methods have a performance gap of only 0.3 bpm on this dataset (as seen in Table 2).  
These results suggest that using the raw ground truth peaks for evaluation can result in substantial miscalculations of error metrics, potentially leading to incorrect conclusions. In all the experiments that follow, the cleaned CleanerPPG peaks are used as the ground truth.
## 4 Discussion
### Other Sources of Uncertainty, Limitation, and Future Prospects  
Additional sources of uncertainty, limitation, and further prospects in simulating GPP are illuminated in the study. First, the quality of the instrument, the randomness of turbulence, the partitioning methods, and gap-filling techniques leading to the random and systematic errors in the flux-derived GPP data is inevitable [75; 76]. Second, the mismatches in scale between Sentinel-3 pixels and tower flux footprints lead to further uncertainties. The limitations exist, such as the slow update of the latest EC data and less reliable Sentinel-3 data during the December-April period due to cloudiness contamination. In the future, improvements in the OLCI products through the use of more robust gap-filling methods and in combination with Sentinel-3B data (only Sentinel-3A data wereused in our study to match the flux tower data) may provide even better GPP estimates [28; 77].  
Although our results showed that GPPOLCI-fapar did not always perform better than GPPMODIS-fapar, it needs to be emphasized that the objective of our study is not to distinguish which FAPAR product is superior in GPP estimation. Our study demonstrated the potential of OLCI FAPAR and OTCI products for GPP simulation and providedmore complementary solutions to estimate GPP. For example, in terms of data fusion, the OLCI FAPAR product with 10m spatial resolution and nearly daily temporal resolution can be generated by blending Sentinel-2 images and the OLCI FAPAR product using downscaling method and spatiotemporal fusion approaches [45; 78]. Then, the same spatial and temporal resolution of the GPP product can be obtained by assimilating FAPAR into LUE models. Moreover, with regard toSIF, the Fluorescence Explorer satellite, which is ESA's eighth Earth Explorer and is proposed as a tandem with Sentinel-3, is expected to be launched by 2022, and it will provide an integrated package of measurements as well as all the necessary auxiliary information to improve GPP assessment together with Sentinel-3 OLCI and the Sea and Land Surface Temperature Radiometer (SLSTR) [79]. Additionally, this study demonstrated that the MODIS GPP algorithm is also suitable for OLCI FAPAR besides MODIS FAPAR. In years to come, attempts in integrating OLCI FAPAR and the SLSTR land surface temperature (LST) product into other LUE models (e.g., CASA and VPM) to estimate GPP at a large scale are also essential.
## 3 Experimental Results and Analysis
### Security Analysis
#### 3.2.7 Key Security  
Key security can be discussed from the perspectives of key space and key sensitivity. In order to resist the brute force attack, the encryption algorithm must have large key space. In addition, the eligible encryption algorithm needs to be sensitive so that any subtle changes of the keys produce a completely different ciphered image. We discuss the key space and key sensitivity in the following:  
* **Key space:** When employing the 2D-SLMM pseudo-random sequence generator, we use three 64-bit double-precision floating-point numbers: \(h\), \(x_{K}\), \(y_{K}\), and a 16-bit integer \(q\) to discard the first \(q\) items to avoid the transient effect. For the encryption algorithm, we adopt five 8-bit integers: \(P_{0,R^{\prime}}\), \(P_{0,G^{\prime}}\), \(P_{0,R^{\prime}}\), \(P_{0,E^{\prime}}\), \(C_{0,E^{\prime}}\) as the initial values. Therefore, the key space is \(2^{248}\), larger than the minimal requirement of \(2^{128}\), thus capable of resisting a brute force attack.
* **Key sensitivity:** We conducted the key sensitivity test for our algorithm as follows: First, we used two keys, \(K_{1}\) and \(K_{2}\), with only one-bit difference. We then encrypted the test image using \(K_{1}\) and \(K_{2}\), thus producing \(EI_{k_{1}}\), shown in Figure 12b and \(EI_{k_{2}}\), shown in Figure 12d. Next, we decrypted \(EI_{k_{1}}\) using \(K_{1}\), thus producing the original  
Figure 11: The results of NPCR and UACI values for statistical hypothesis tests. (**a**) NPCR. (**b**) UACI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **NPCR (\%)** & **UACI (\%)** \\ \hline R & 99.60586548 & 33.46571998 \\ G & 99.62015788 & 33.46139127 \\ B & 99.60901896 & 33.46733462 \\ E & 99.60657756 & 33.46112799 \\ \hline \hline \end{tabular}
\end{table}
Table 9: NPCR and UACI values for the stego encrypted test image, “memorial”.  
image, shown in Figure 12c. In addition, when we decrypted \(EI_{k_{1}}\) using a different key, \(K_{2}\), the decrypted image produced looks like a noise image containing no useful information, shown in Figure 12f. The NPCR and UACI between \(EI_{S1}\) and \(EI_{S2}\) are close to the ideal values, 99.6094 and 33.4635. We conclude that our algorithm provides the benefit of strong key sensitivity features.
# Motion Control System of Unmanned Railars Based on Image Recognition
## 6 Conclusions  
In this research, an integrated unmanned motion control system of railcars based on image recognition is built and verified by experiment successfully. When this system detects a predefined sign, it can generate a new PWM power setting to drive the railcars within 0.3 of a second. The system response is very fast. Using a better computer can further improve the system performance. Since the sensing and control devices of the proposed system consists of a computer, camera and predefined signs only, both the implementation and maintenance costs are very low. Operating in surroundings of fixed ambient brightness, the possibility of malfunction of the proposed system is very small. Even if any malfunction does happen, it should be easy to find the cause and to fix it. Therefore, one contribution of this research is providing a cost affordable and reliable solution for motion control of unmanned railcars. In addition, many signaling systems are radio-based communication systems such as the CBTC system. They have electromagnetic compatibility (EMC) issues and easily suffer from electromagnetic interference (EMI) caused by lighting or intentional radio jamming launched by terrorists. Since cameras sense and convert light waves to images, and light waves are immune to electromagnetic interference, merging the proposed method of image recognition into signaling systems can improve safety at additional low cost. This is another contribution.  
However, the main contribution of this research is to demonstrate the feasibility of graphic predefined signs serving as the commands for a computer-based vision system. In other words, the detections of different predefined signs can trigger different system responses to handle different tasks. With this concept and imagination, people can develop more versatile control systems based on computer vision. For example, the proposed method is also applicable to autonomous vehicles so that one can properly deploy predefined signs on the roadside to control the motions of autonomous vehicles using image recognition technology. Therefore, one can train more predefined signs as commands to accomplish motion control for more complicated tasks in future.  
Conceptualization, Y.-W.T. and R.-C.W.; methodology, Y.-W.T.; software, T.-W.H.; validation, T.-W.H., C.-L.P. and Y.-W.T.; formal analysis, Y.-W.T., C.-L.P.; investigation, Y.-W.T.; resources, R.-C.W.; data curation, T.-W.H.; writing--original draft preparation, Y.-W.T.; writing--review and editing, C.-L.P.; visualization, T.-W.H.; supervision, R.-C.W.; project administration, R.-C.W.; funding acquisition, R.-C.W. and Y.-W.T.  
This research was funded by Ministry of Science and Technology, Taiwan, R.O.C., grant Nos. MOST 106-2221-E-214-008 and MOST 107-2622-E-214-003-CC3.  
The authors declare no conflict of interest.
###### Abstract  
This study investigates the effects of continuous and short-term direct current (DC) stimulation on the growth and nutrient composition of _Nitzschia closterium_ f. _minutissima_, a marine diatom with significant potential in aquaculture feed. We explored the optimal conditions of DC stimulation by applying both short-term and continuous treatments at varying voltages (0 V, 1 V, 3 V, 5 V, 10 V for short-term; 0 V, 0.05 V, 0.5 V, 1 V for continuous). The results demonstrated that short-term DC stimulation significantly enhanced cell density and lipid accumulation, with maximum cell density increasing by 13.14% under 1 V stimulation compared to the control. However, continuous stimulation was less effective and showed growth inhibition in several cases. Nutrient analysis revealed that short-term stimulation, particularly at 3 V, led to a 67.66% increase in lipid content, while moderate continuous stimulation (0.5 V) showed a 39.47% increase in biomass dry weight. These findings suggest that short-term DC stimulation is a promising approach to optimize microalgal growth and nutrient accumulation for large-scale aquaculture production.  
Nitzschia closterium f. _minutissima_; biomass accumulation; direct current (DC) stimulation; photobioreactor