esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Circuit Description  
The working principles of forward converters have been mentioned in previously published articles and books [13; 32; 33; 34]. To briefly describe the circuit, the signal coming from the PWM controller closes the switch inside itself and the current flows through the primary inductance. During this time, since the secondary inductance has the same polarity as the primary inductance, it transfers its energy to the load through the secondary diode. When the PWM signal is pulled to zero, the MOSFET opens, and, in this case, no current flows through the primary side, so the energy transfer to the secondary side stops thanks to the diodes on the secondary side. One of the points that should be taken into consideration is that when the transformer enters saturation, there is only a certain amount of energy transfer. Therefore, it is necessary to operate the transformer in the linear region. Figure 1 shows the basic circuit of a three-output forward converter, and Figure 2 shows the waveforms of the currents and voltages of this circuit.  
Figure 1: Basic circuit of three-output forward converter.  
As can be seen from Figure 1, rectifier diode \(\rm D_{R}\), flywheel diode \(\rm D_{F}\), and magnetic-reset clamping diode \(\rm D_{M}\) are fast switching speed diodes. \(\rm M_{1}\) is the power MOSFET. L1, L2 and L3 are couplings among themselves. The inductances shown for the winding are couplings among themselves. The output capacitors are electrolyte capacitors.  
The point we need to pay attention to here is the appropriate design of the reset winding, as can be seen in Figure 2. This issue is touched upon in Section 3. The reset winding must be smaller than the primary winding; otherwise, it is not possible for the circuit to work correctly.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 2 Materials and Methods
### Adsorption Capacity of Biochars for \(\alpha\)-Tocopheryl Acetate  
When referring to vitamin E, the most active form, \(\alpha\)-tocopherol, is typically implied. Due to the poor storage stability and low availability of \(\alpha\)-tocopherol, the most widely used source of vitamin E in animal nutrition is the DL-stereoisomer of \(\alpha\)-tocopherol, the synthetic acetate ester DL-\(\alpha\)-tocopherolyl acetate [52]. \(\alpha\)-tocopherolyl acetate protects the phenolic group from oxidation caused by light, heat, or oxygen [53]. In contrast to natural \(\alpha\)-tocopherol, \(\alpha\)-tocopherolyl acetate only acts within the animal body, and not in the feed fat, as the antioxidant effect is only released after enzymatic removal of the acetate residue [54]. As a vitamin E derivative, DL-\(\alpha\)-tocopherol acetate (C\({}_{31}\)H\({}_{52}\)O\({}_{3}\)) (CAS: 7695-91-2, BASF, Ludwigshafen, Germany), with a molar mass of 472.8 g/mol and content of at least 94%, was used.  
To transport the fat-soluble vitamin E to the pores for subsequent adsorption, a solvent is necessary, which also affects the adsorption capacity of the adsorbent [29; 34; 49]. Studies have shown that non-polar \(n\)-heptane, polar \(n\)-butanol [29; 34], or polar ethanol can be used [25; 49]. [PERSON] et al. [49] reported that the vitamin E uptake from \(n\)-heptane is higher than that from \(n\)-butanol or ethanol, since the latter is hydrophilic. This results in a lower adsorption of the solvent to the adsorbent, which is due to the fact that the competition with the adsorptive vitamin E is lower. On the contrary, polar solvents form hydrogen bonds between the active hydroxyl ether group of vitamin E and the hydroxy group of the solvent. [PERSON] et al. [34] declared that even though the vitamin E uptake from \(n\)-heptane is significantly higher, and generally, non-polar solvents are required to adsorb large amounts of vitamin E, an ethanol solution is more feasible in medical applications [49]. Thus, ethanol (CAS: 64-17-5, absolute, Merck KGa, Darmstadt, Germany) was used to transport \(\alpha\)-tocopherol acetate into the pores of the biochar.  
As vitamin E is a photosensitive vitamin, during the procedure, the beakers were wrapped in aluminium foil to prevent photodegradation due to light exposure. In addition, the beakers were closed with screw caps. The analysis was carried out in triplicate.
## 4 Results of the Study
### Numerical Modeling of Uncentered Surfaces
#### 4.2.3 Different Grouting Pressures in Unreinforced Soil  
In order to investigate the effect of different grouting pressures on the amount of surface settlement in the shield tunnel during shield tunnel excavation under the unreinforced state of the ground surface, five working conditions, as shown in Table 6, are set up, and numerical simulation is carried out to draw the variation in surface settlement, as shown in Figure 9.  
By analyzing Figure 9, when the grouting pressure is less than 152.2 kPa, the maximum settlement value of the ground surface decreases with the increase in the grouting pressure. When the grouting pressure is too high, the maximum ground settlement value increases with the increase in grouting pressure. A certain degree of surface uplift occurs at a location three times the tunnel diameter from the tunnel centerline. In addition, the maximum and minimum difference in the surface settlement values gradually increase with the gradual increase in the grouting pressure. When the grouting pressure is 152.2 kPa, the change in surface settlement is relatively small. According to the numerical simulation cloud diagram and the change in the surface settlement curve, the optimum grouting pressure for this section should be selected near 152.2 kPa. Combined with the field data, the average value of grouting pressure in this interval section is about 152.2 kPa, and the numerical simulation and field data match well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Programming** & \
\begin{tabular}{c} **Depth of burial of** \\ **Tunnels (m)** \\ \end{tabular} & **Radius of the Tunnel (m)** & **Grouting Pressure (kPa)** &
\begin{tabular}{c} **Pressure on Soil** \\ **Chamber (kPa)** \\ \end{tabular} \\ \hline Scenario 1 & 16 & 3.1 & 100 & 146 \\ Scenario 2 & 16 & 3.1 & 130 & 146 \\ Scenario 3 & 16 & 3.1 & 152.2 & 146 \\ Scenario 4 & 16 & 3.1 & 180 & 146 \\ Scenario 5 & 16 & 3.1 & 200 & 146 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Shield parameters at different grouting pressures in Unreinforced Soil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Programming** & \
\begin{tabular}{c} **Depth of burial of** \\ **Tunnels (m)** \\ \end{tabular} & **Radius of the Tunnel (m)** & **Grouting Pressure (kPa)** &
\begin{tabular}{c} **Pressure on Soil** \\ **Chamber (kPa)** \\ \end{tabular} \\ \hline Scenario 1 & 16 & 3.1 & 152.2 & 120 \\ Scenario 2 & 16 & 3.1 & 152.2 & 135 \\ Scenario 3 & 16 & 3.1 & 152.2 & 146 \\ Scenario 4 & 16 & 3.1 & 152.2 & 165 \\ Scenario 5 & 16 & 3.1 & 152.2 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Shield parameters at different soil compartment pressures in Unreinforced Soil.
In many cases, E-noses are combined with gas chromatography-mass spectrometry for aroma detection, aiding in quality identification, grading, and traceability of food products [16; 17]. [PERSON] et al. used an electronic nose to analyze the aroma intensity of Arabica coffee beans with three roasting degrees, and determined the intensity of coffee blend aroma in real time, which is of great significance for objectively evaluating the aroma intensity changes during coffee roasting [18]. Currently, the most commonly used technique for extracting and detecting tea aroma components is headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) [19; 20]. Compared to gas chromatography-mass spectrometry (GC-MS), GC-Q-TOF/MS offers the advantage of high-resolution time-of-flight mass spectrometry. This allows for the accurate determination of the relative molecular mass of compounds, coupled with the high separation ability of gas chromatography. As a result, GC-Q-TOF/MS can efficiently, rapidly, and accurately detect the aroma of samples. This technique is particularly useful for extracting target compounds in complex systems, thereby reducing the risk of misdetection and omitting detections [21; 22]. The GC-Q-TOF/MS technique has also been increasingly utilized for the detection of tea aromas [23; 24]. In this study, the aroma of four types of Liupao tea samples was analyzed using GC-Q/TOF-MS and an E-nose. The findings aim to establish a theoretical framework for regulating the flavor of Liupao tea, fostering innovation and product development, which is conducive to enhancing consumers' perception of Liupao tea.
# Magnetic Field Penetration Depth in Various Materials and Applications
## 3 Results
### Areas in Which the Studies Were Applied  
The application areas that were found in the review to have the most studies are nanoscience, quantum physics, and materials, with applications of superconductors with quantum technologies and advanced quantum electronic devices, such as nanotechnology. The second-most experiments were performed in the areas of materials physics, with the application of superconductors for telecommunications, energy storage, and multiple types of sensors. It should be noted that in the areas of medical engineering, imaging, and physics, the application of the magnetic field is for medical diagnostics, magnetic resonance, medical imaging, tissue studies, and pre-clinical studies. Likewise, the area of telecommunications and geophysics is applied for communication in underground mines and geological and deep water mapping. Also, Figure 7 shows that in the area of electronic and electrical engineering, studies reveal that it is applied in the protection of medical equipment against magnetic fields, dielectric fabrication and underground networks. However, the areas of bioengineering, neuroscience, and civil engineering are alien to these reviewed studies, with only a smaller number of experiments.  
Most studies focus on type II and natural superconducting materials due to their relevance to the electronics industry.
## 3 Results
### Pressure Prediction  
**Partially known data**  
The model demonstrated a strong reliability for pressure prediction in partially known experiments, as illustrated in Figure 8, which compares measured (M) against predicted (P) trends. For experiments with varying filter cycles (Exp-1 to Exp-4, Figure 8a), the predicted pressure profiles closely matched the measured values, with minor deviations observed at higher pressures during later stages. Similarly, experiments with varying concentrations  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Experiment** & **Concentration [g/L]** & **Filter Plate Number** & **End Pressure (bar)** & **Cycles** \\ \hline
1 & 12.5 & 2 & 10 & 2 \\
2 & 12.5 & 2 & 10 & 7 \\
3 & 12.5 & 2 & 10 & 35 \\
4 & 12.5 & 2 & 10 & 36 \\
5 & 6.25 & 2 & 8 & 29 \\
6 & 25 & 2 & 10 & 23 \\
7 & 12.5 & 1 & 10 & 4 \\
8 & 12.5 & 3 & 10 & 2 \\
9 & 12.5 & 2 & 10 & 5 \\
10 & 25 & 1 & 10 & 24 \\
11 & 25 & 3 & 10 & 25 \\
12 & 25 & 4 & 10 & 26 \\ \hline \end{tabular}
\end{table}
Table 2: Experiment data with diverse configurations and filter cycles. The experiment is partially known to the model since it was split into 80% training and 20% validation data.  
Figure 7: Depicted is the RAW data of a pressure trend in (**a**) and the MA pressure trend in (**b**) with CI90% bounds.  
(Exp-5, Exp-3, and Exp-6, Figure 8b) showed high accuracy, although Exp-5 exhibited less precision at maximum pressures due to the absence of training data for a concentration of 6.25 g/L. Figure 8c,d highlight the model's consistency across different configurations, with slight discrepancies near peak pressures. Quantitative metrics are summarized in Table 4. The errors for partially known experiments ranged from MSE 0.006 to 0.178, RMSE 0.103 to 0.352, and RL2N 1.7% to 9.8%. The best performance was observed in Exp-5 (RL2N 0.17%, PIB 97%), while Exp-6 had the highest error (RL2N 9.8%, PIB 64%). Overall, the model achieved an average MSE of 0.048, RMSE of 0.185, and RL2N of 5%, with 82% of the predictions falling within CI90% bounds and minor deviations for the remaining 18%. The pressure drop around 230 seconds in Exp-6 (Figure 8d) illustrates the model's ability to follow expected trends despite limited training data for configurations with three or four plates.  
Figure 8: Comparison of measured (M) and predicted (P) pressure values for the experiments from the Table 2. (**a**) shows the increase in operation time with higher cycle number. (**b**) shows the increase in operation time with different concentrations and similar filter cycle. (**c**,**d**) show the operational time with different numbers of filter chambers.
## 1 Introduction
### Importance of the Research  
Malicious nodes create issues such as content alteration, non-trustable content delivery, and information flow. Malicious nodes are detected through machine learning, which enables data management operations in VANET. DDoS is one of the most critical and advance level threats for VANET. In this research, we plan to handle DDoS attacks in a real-time scenario. More specifically, when we talk about real-time traffic and content sharing in VANET, the content shared data is difficult to handle whether the real-time data is secure or not. We took the idea from machine learning, and VANET combined the handling of different incoming network traffic for the DDoS attacks. The VANET and machine learning collectively secure real-time content sharing among all the vehicles.  
Although many research works have been implemented and provide secure content delivery in VANET, the real-time detection of the malicious nodes in VANET is still the main idea to be solved. This proposed work solves the malicious node's detection issues with real-time monitoring through VANET emergency packet delivery. Malicious nodes are detected using real-time monitoring schemes through VANET. A reliable mathematical model is evolved through a machine learning malicious node detection policy. VANET is considered one of the most demanding smart environments in the recent technological era. VANET faces multiple issues such as content security and privacy, and smart V2V environment. In this research, we considered malicious node detection during the flow of information in VANETs.  
This research evaluates the below objectives:* Detection of malicious nodes through machine learning.
* Enhance the throughput of the network through the detection of malicious nodes.
* True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), and False Negative Rate (FNR) classification for malicious node detection.
* This research will contribute to secure the lives of patients when medical data is secured.
* This research enhances the security of special coveys and government data.
## 6 The Proposed Clustering Routing Protocol  
This section presents our proposed energy-efficient clustering routing protocol (EECRP) based on the modified GA and the improved BPNN that is used for data fusion. Referring to the LEACH protocol [27], the EECRP has three phases: CHN selection, cluster formation, and data transmission. In every cluster, CMNs transmit data to the CHN through one hop. Once the CHNs receive the data, they perform data fusion by using the improved BPNN algorithm and transmit the processed data to the SN through multiple hops. The relay nodes are other CHNs and the optimal multi-hop transmission paths are determined through the improved GA.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 7 Main Progresses, Perspectives, and Prospects
### The Increasing Importance of RS Data in DSM
#### 7.2.1 Use of RS Products as Surrogates for In Situ Measurements  
As developed in Section 3.4, and already highlighted by [PERSON] and [PERSON] [240], in some cases, \"the estimations of soil properties at each pixel of a hyperspectral image is assumed to be precise enough to be considered as a measurement of a soil property and to be used as such for feeding DSM models\". The huge advantage of these situations is that the spatial sampling is much denser than what is usually available for the soil data to train DMS models. A better coverage of spatial soil variability, especially short-scale variability, which is often not captured by existing soil data, can be expected.  
Moreover, there are still large spatial gaps in the available soil information to run DSM models around the world [13]. Therefore, these myriad surrogates for in situ measurements pave the way for both filling gaps in learning data and revealing finer spatial structures. These surrogates have proven to be efficient for some soil properties in the case of airborne hyperspectral imagery (e.g., [124; 126; 132; 240; 282]). However, we need to consider that these surrogates have uncertainties, and that the relationships that enabled the estimation of these surrogates should not be applied outside of their domain of validity. This is a big challenge, as soil data are still missing in large parts of the world.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 4 Experimental Results  
### Testing Conditions  
The HEVC reference encoder HM14 [16] is used as the anchor to estimate the Bjontegaard Delta Bitrate (BD-R) [17] estimated bitrate savings and relative encoding time to compare the various methods.  
We used the code from [PERSON] et al. [10] to compare our proposed method with the existing state of the art. We also used their implementation of the gradient method from Reference [9] and a 3-parameter variant of the gradient approach that uses the same entropy coding as their method.  
We used the same code for the entropy coding and transform calculations. We wrote the parameter estimation of the proposed method to replace theirs. This allows us to compare the parameter estimation process without other variables making the comparison difficult.  
We compare our method to Reference [10], their implementation of Reference [9], and the 3-parameter variant of the gradient method.  
For the encoding settings, the same settings as Reference [10] are used: The encoding mode is set to Low Delay P, and the quantization parameter (QP) values are \(22,27,32,37\).  
A total of seven sequences that show various motions were encoded with HM14 [16], Tsutake [10], Heithausen [9], the 3-parameter gradient and the proposed method. The sequences used are from two datasets, the ITE/ARIB Hi-Vision Test Sequence 2 nd Edition [18] and [PERSON]'s collection [19]. Table 1 lists the sequences that were used, with the sequence number for the sequences from Reference [18]. To compute the encoding time, we used the following formula:  
\[\Delta T=\frac{T_{target}-T_{HM_{14}}}{T_{HM_{14}}} \tag{14}\]In the following tables, the encoding time shown is the average over all QP values.
## 3 Results
### Performance of ANN for Specific Energy Requirement Prediction  
A neural network model with a 3-5-1 architecture was formulated to predict specific energy requirements of \(\mathrm{R_{VA}}\) using three independent variables: DO, CI, and the \(u/v\) ratio and Levenberg-Marquardt back-propagation learning. Figure 8a shows the training state of formulated ANN, providing insights into the variation in performance gradient, the Levenberg's damping factor (\(\upmu\)), and the validation check numbers. Notably, the gradient value impressively reduced to 0.0010525 by the 10 th epoch, with the \(\upmu\) factor reached \(1\times 10^{-5}\) at the same epoch. In order to prevent any potential overfitting, the network training was wisely terminated at the 10 th epoch, following six validation checks, subsequent to achieving the best validation performance at Epoch 4. As shown in Figure 8b, the best validation performance was obtained at the fourth epoch that yielded the lowest \(MSE\) of 0.0024612. The regression plots in Figure 9 depict consistency between the predicted and actual SPE requirements from the ANN deployed in this study on training, validation, test, and overall experimental datasets. The correlation coefficients (\(R\)) were consistently high, i.e., 0.98666, 0.96029, 0.99294, and 0.97721 for training, testing, validation, and overall datasets, respectively, signifying higher model prediction accuracy.  
Figure 8: (**a**) Performance curves of formulated ANN model during training and (**b**) curves showing mean square errors during training, testing and validation phases (best performance for MSE of 0.0024612 at 4 th training epoch).  
Figure 9: Regression plots of the developed ANN model for different phases.
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 2 Contributed Papers
### Flood Simulations and Predictions
Theirresults indicate that the exponential velocity distribution method outperforms the logarithmic method. Under high flood events (>20 m\({}^{3}\)/s), the proposed method can achieve model errors less than 10%. Under a low streamflow condition (1 m\({}^{3}\)/s), the accuracies are relatively low. Nevertheless, this study provides an alternative way to calculate streamflow in unaggged rivers, which are particularly useful for peak flood estimation. The proposed approach is easy to apply and has potential for large-scale application, considering the quick advance of the UAV technology.
# A Survey of Machine Learning Methods for Time Series Prediction
## 6 M5 and M6 Forecasting Competitions
### M5 Forecasting Competition  
The M5 Forecasting Competition, held in 2020, focused on predicting retail sales using real-world data which consisted of 42,840 time series of Walmart unit sales. The competition had two components: the Accuracy Competition and the Uncertainty Competition, each with a prize pool of USD 50,000. These incentives attracted thousands of participants, creating a large dataset for analysis.