esa-sceva/satcom-chunk-collection huggingface dataset:
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Biochar Characterization
#### 3.1.3 FTIR Analysis  
The FTIR spectra for the commercial biochar, tartrazine, and the biochar with adsorbed tartrazine are presented in Figure 3.  
FTIR spectra of biochar shows an amorphous material that contain hydroxyl (3433 cm\({}^{-1}\)), carbonyl (1700, 1125 cm\({}^{-1}\)), aromatic C=C (1623 cm\({}^{-1}\)), aliphatic (2924, 2857, 1369 cm\({}^{-1}\)), and aromatic (1412, 900-700 cm\({}^{-1}\)) C\(-\)H groups [45].  
The vibrational bands in the FTIR spectrum of tartrazine can be assigned to various functional groups present in the compound as follows: 3436 cm\({}^{-1}\) (OH), 2924 and 2854 cm\({}^{-1}\) (aliphatic C-H), 1744 sh and 1642 cm\({}^{-1}\) (C=O), 1599 and 1192 cm\({}^{-1}\) (COONa), 1562 and 1412 cm\({}^{-1}\) (N=N), 1347 and 1126 cm\({}^{-1}\) (C-H bending and C-H-aromatic ring), 1037 and 1008 cm\({}^{-1}\) (-S=O and C\({}_{6}\)H\({}_{5}\)-S\({}_{2}\)), 768 cm\({}^{-1}\) (-C\({}_{6}\)H\({}_{5}\)-N=N- bonds), 694 cm\({}^{-1}\) (-CCO), 718 and 648 cm\({}^{-1}\) (the -C-C-, -C=C- and C\({}_{6}\)H\({}_{5}\) out-of-plane stretching), 567 cm\({}^{-1}\) (C\({}_{6}\)H\({}_{5}\)- and R-C\({}_{6}\)H\({}_{4}\)- groups) and 528 cm\({}^{-1}\) (out-of-plane bending of the para-substituted aromatic ring containing a sulphoxyl group) [46; 47].  
Figure 1: TEM images of commercial biochar.  
Figure 2: Nitrogen adsorption–desorption isotherms for biochar (blue—adsorption, orange—desorption).  
After adsorption of tartrazine, some changes were observed on the FTIR spectrum of the biochar, as evidence of the adsorption of the dye from the solution on the surface, such as the following: the band of tartrazine from 1642 cm\({}^{-1}\) shifts and appears as a shoulder at 1634 cm\({}^{-1}\) on the band of biochar from 1623 cm\({}^{-1}\); the band of biochar from 1369 cm\({}^{-1}\) shifts to 1375 cm\({}^{-1}\) and appears as a shoulder, the vibrational band of biochar from 1125 cm\({}^{-1}\) and the band of tartrazine from 1126 cm\({}^{-1}\) shift and overlap at 1129 cm\({}^{-1}\); the characteristic vibrational bands of tartrazine from 1037, 718, and 648 cm\({}^{-1}\) are founded in the form of a shoulder and shifted to 1046, 726, and 657 cm\({}^{-1}\), respectively. The vibrational bands of tartrazine from 718 and 648 cm\({}^{-1}\) shift at 726 and 657 cm\({}^{-1}\), respectively, and appear as a shoulder. The observed shifts of the vibration bands of tartrazine when the biochar adsorbed it are probably caused by the weak intermolecular interactions, possibly of the hydrogen bonds, that arise between the functional groups of tartrazine, namely the C=O and -S=O groups, and the surface of biochar.
## 2 Materials and Methods
### Infrared Microimaging  
The IR acquisitions were performed with a Spectrum Spotlight 300 FT-IR Imaging System (Perkin-Elmer, Villebon sur Yvette, France), using a matrix of 16 pixels. Each pixel can be considered as an individual IR detector, analyzing a surface on the sample equivalent to 6.25 \(\upmu\)m \(\times\) 6.25 \(\upmu\)m. Spectra were collected from 400 to 4000 cm\({}^{-1}\) wavelength range, with a 4 cm\({}^{-1}\) spectral resolution and eight accumulations per measurement. The background was recorded on a sample-free CaF\({}_{2}\) area. Paraffin signal was also collected with the same parameters on an area surrounding the TMA spots without tissue.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with Hidden Nodes
#### 5.4.2 CSMA/CA statistics  
Table 4 shows the results of the CSMA/CA statistics obtained with hidden nodes. As with the case with the statistics obtained when there are not hidden nodes, the percentage of successfully transmitted frames decreases as the load and the number of nodes in the network increase. However, in the presence of hidden nodes, the decrease in the percentage of successfully transmitted frames is much more pronounced, becoming virtually 0% with a 200% network load on all scenarios. The sharp decrease of successfully transmitted frames is caused by the rapid increase in the number of collisions, whose percentage turns out to be above 90% with network loads higher than 50%.  
Due to the high percentage of collisions produced with hidden nodes, the whole failures produced in the CSMA/CA algorithm are accounted in the column \"Frame transmission failures\",  
Figure 11: Goodput as a function of network load, with hidden nodes.  
Figure 12: Packet loss rate, with hidden nodes.  
produced when the maximum retransmission number (_macMaxFrameRetries_) is exceeded. This makes a lot of difference with the statistics obtained without hidden nodes, in which all CSMA/CA failures are accounted in the column \"Channel access failures\", produced when the limit of _macMaxCSMABackoffs_ is exceeded.
## 2 System Model
### Optimization Model  
Considering the harvested energy and transmit power constraints, i.e., Equations (7) and (8), the optimization model to minimize the total-MSE of the whole system and to find the optimal source and relay beamformer and the source receiver is described in this section. The objective function and the problem are separately discussed below.  
Using Equation (9), the MSE of \(S_{i}\) can be given by  
\[\mathcal{J}_{i}= \mathbf{E}\left[\left\|\mathbf{W}_{i}\mathbf{y}_{S_{i}}-\mathbf{ \hat{s}}_{i}\right\|_{2}^{2}\right]\] \[= Tr\Big{\{}\mathbf{W}_{i}\mathbf{J}_{i_{B}}\mathbf{J}_{i_{B}}^{H} \mathbf{W}_{i}^{H}+\beta_{i}\sigma_{S_{i}}^{2}\mathbf{W}_{i}\mathbf{W}_{i}^{H} \tag{10}\] \[-2 Re(\mathbf{W}_{i}\mathbf{J}_{i_{B}})+\sigma_{r}^{2}\mathbf{W}_{ i}\mathbf{J}_{i_{A}}\mathbf{J}_{i_{A}}^{H}\mathbf{W}_{i}^{H}+\mathbf{I}_{M_{R}} \Big{\}},\]  
where \(\mathbf{W}_{i}\in\mathbb{C}^{M_{R}\times M_{R}}\) is the linear receiver at \(S_{i}\), \(\mathbf{E}[\mathbf{s}_{i}\mathbf{s}_{i}^{H}]=0\), \(\mathbf{E}\left[\mathbf{s}_{i}\mathbf{s}_{i}^{H}\right]=1\), \(\mathbf{J}_{i_{A}}=\sqrt{\beta_{i}}\mathbf{H}_{RS_{i}}\mathbf{F}_{r}\), \(\mathbf{J}_{i_{C}}=\mathbf{H}_{S_{i}R}\mathbf{F}_{r}\), and \(\mathbf{J}_{i_{B}}=\mathbf{J}_{i_{A}}\mathbf{J}_{i_{C}}\).  
Given the MSE of \(S_{i}\), fixing \(\beta_{i}\), a joint source and relay beamforming and source-receiving optimization problem based on the total-MSE with transmit power constraints and an energy-harvesting constraint can be formulated as  
\[\min_{\mathbf{F}_{r},\mathbf{W}_{i}\mathbf{F}_{i}}\ \ \ \ \ \ \ \ \mathcal{J}_{1}+\mathcal{J}_{2}\] (11a) s.t. \[\ \ \ Tr\left(\mathbf{F}_{i}\mathbf{F}_{i}^{H}\right)\leqslant p_{i}\] (11b) \[\
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 2 Comparison with Related Work  
This section presents some related works. Table 1 summarizes all the compared papers. Fourteen papers were found using the keywords that matched this proposal. The papers were divided, taking into account five aspects: Metrics, capacity variation of the master-slave server, sensitivity analysis, context, and use of component dependency.  
**Metrics--**Metrics help to compute and subsequently understand the behavior of a system. This paper uses metrics of both performance and availability that were not explored together by any other previous article. Among the works that deal with performance at the edge, only this work used the metrics MRT, resource utilization, and drop probability. The work of [26; 27; 28; 29; 30; 31; 32] focused solely on system availability, but as we will see later, only the work of [32] focuses on the edge, as this work.  
This work presents a very unique edge architecture, and because of that, another contribution of this work is to do experiments with **Capacity variation of the master-slave server**. Among the related works, no work used the architecture used here, which provides a greater number of parameters. This work also performs a **Sensitivity analysis** that allows the identification of the key components for the architecture and, consequently, for the model. Among the works found, only the works of [27; 31] performed a sensitivity analysis. However, the work of [27] was in a Cloud context, while the work of [31] presents an edge context, but focused specifically on the area of smart hospitals.  
**Context--**all papers are in the context of remote computing, whether in the cloud, on the edge, or in the fog. Most works focus on edge architecture, however, most of them are aimed at measuring edge energy consumption. Unlike these works, our work uses metrics that have not been explored in this context so far. The work of [32] is the only one related to availability that focuses exclusively on the edge.  
Finally, **use of component dependency** is a unique contribution. The technique is still little explored, but it is essential to ensure the correct functioning of the system as a whole. The technique is applied to Petri net models to ensure that if a component that is the basis for other components falls, it causes a cascade effect to turn off those components that depend on it. Thus, it can be guaranteed that an availability model will be as close to reality as possible, and only this work made use of this aspect of the Petri nets in modeling the availability of the system.
## 4 Results
### Using the Synthetic Database
#### 4.1.3. Neural Networks Using the VV and VH Together (NN3, NN3_dry, and NN3_wet)  
The use of the VV and VH polarizations together as inputs in the neural networks (NN3, NN3_dry, and NN3_wet) to estimate soil moisture provides an accuracy similar to that obtained with the use of VV alone (NN1, NN1_dry, and NN1_wet). As observed in the case of VV alone, the use of both VV and VH with consideration of a priori information reduces the errors on the \(mv\) estimates (Figure 7). As an example, for a reference \(mv\) between 2 and 25 vol %, a bias of 1.2 vol % and an RMSE of 4.0 vol % are obtained with consideration of a priori information on the \(mv\) in the case of dry to slightly wet soil conditions (NN3_dry), compared to a bias of 2.6 vol % and an RMSE of 5.25 vol % with no consideration of a priori information on the \(mv\) (NN3) (Figure 7a,b). Moreover, using both VV and VH, the behavior of errors (bias and RMSE) according to the \(Hrns\), NDVI and \(\theta\), as well as the values of these errors for each of the \(Hrns\), NDVI and \(\theta\) values, are approximately the same when the VV polarization is used alone (Figures 4 and 8).  
Figure 7: The estimated \(mv\) as a function of the reference \(mv\) when the VV and VH are used together. (a) NN3 (no a priori on the \(mv\)), (b) NN3_dry (a priori information on the \(mv\), dry to slightly wet); (c) NN3_wet (a priori information on the \(mv\), very wet).  
Figure 8: Bias (estimated _mv_—reference _mv_) and RMSE on the _mv_ estimates when VV and VH are used together as a function of the _Hrms_ (**a**,**d**), NDVI (**b**,**e**), and \(\theta\) (**c**,**f**) for each of NN3, NN3_dry, and NN3_wet.
# Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
## 3 Results and Discussion  
Different models were built to predict the equivalent diameter \(D_{eq}\) after one time step, \(D_{eq+1}\), after two time steps, \(D_{eq+2}\) and, after three time steps, \(D_{eq+3}\). In the same way, different models were developed to forecast the aspect ratio AR after one time step, AR\({}_{+1}\), after two time steps, AR\({}_{+2}\) and, after three time steps, AR\({}_{+3}\). Based on the input variables, three different models were built for the prediction of \(D_{eq}\) and AR. First, a model taking into account a greater number of variables that can affect the investigated phenomena was developed, then two simpler models were built, based on some relevant parameters for the evolution of the bubbles. Therefore, Model 1 is characterized by the following quantities as input variables: bubble velocity \(V_{b}\), bubble depth below the free surface \(h_{b}\), the bubble Weber number \(We_{b}\), the bubble Froude number \(Fr_{b}\), the bubble Reynolds number \(Re_{b}\), the Eotvos number \(Eo\), the initial equivalent diameter \(D_{eq}0\). Model 2 admits as input variables \(We_{b}\), \(Re_{b}\) and \(Eo\). Model 3 has the following input variables: \(We_{b}\), \(Re_{b}\) and \(Fr_{b}\). In addition, five variants of each model were developed, changing the implemented machine learning algorithm. Table 1 shows a comparison of the results provided by the different models. With regard to \(D_{eq+1}\), all models showed good predictive capabilities. Model 1 showed the best forecast performance, in all its variants. The Bagging algorithm proved to be the most accurate one in this case (\(R^{2}=0.9749\)  
MAE = 0.0974 mm, RMSE = 0.1434 mm, RAE = 13.42%), while the SVR-based variant led to less accurate predictions (\(R^{2}\) = 0.9472, MAE = 0.1418 mm, RMSE = 0.2020 mm, RAE = 20.18%). RF and K-Star showed a comparable performance, outperforming ARDS. Model 2 exhibited forecasting capabilities very similar to those of Model 1. Furthermore, in this case, the Bagging algorithm (\(R^{2}\) = 0.9742, MAE = 0.0973 mm, RMSE = 0.1448 mm, RAE = 13.61%) led to
## 1 Introduction
(2018). Experimental results based on the KDD-CUP99 dataset and NLS-KDD datasets show that an SCDNN generates better accuracy and more robust results than other well-known algorithms, and excels in parallel computing [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The contribution of this study is as follows: First, we adopt SC to capture the features of complex network datasets, for attack types similar to normal access, using clustering to find features that divide the dataset into subsets with different cluster centres. Second, taking advantage of multi-layer DNNs, attack types can be learned at high, abstract levels of network data and complex network intrusion rules can be found. The intrusion process is becoming more variable and easily disguised as normal network activity. This situation is particularly common in large datasets and complicates the relationship between distributed processing protocols. Knowledge of categories in a dataset is sufficient for the SCDNN model to be able to learn. Finally, our experimental results indicate that the accuracy, detection and false alarm rates of SCDNN are better than those of conventional methods. The rest of the paper is organized as follows. The related literature concerning IDS is reviewed in Section 2. Section 3 presents a detailed definition of the proposed approach and describes how the algorithm works. Section 4 describes the experimental dataset and illustrates the data preparation, evaluation criteria, results and discussions of experiments. Finally, the conclusions and suggestions for future work are provided in Section 5.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model
### Workflow of the EPT-VMD-TCN-TPA Model  
In this paper, an EPT-VMD-TCN-TPA short-term electric load forecasting model is proposed. The EPT-VMD-FE-TCN-TPA forecasting model is discussed on the basis of the EPT-VMD-TCN-TPA model. The workflow of the proposed EPT-VMD-TCN-TPA model is shown in Figure 16, and each step is described as follows:  
Step 1: Pre-processing of the raw data.  
Step 2: The pre-processed raw data are divided into load data \(f(t)\) and meteorological data \(m(t)\).  
Step 3: Use EPT to extract the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load data \(f(t)\).  
Step 4: The residual fluctuation components are decomposed into IMF components using VMD according to the predefined \(k\) values.  
Step 5: Each IMF component and Tr(t) component is fused with \(m(t)\) separately and the fused data are normalized to eliminate the effect of different dimensional data.  
Step 6: The data fused in Step 5 are divided into a training set and a test set. The training set is fed into the TCN-TPA network and the hyperparameters of the model are improved and updated by calculating the loss function MSE to learn the complex relationships between the input and output variables in each subsequence.  
Step 7: Each subsequence is predicted using the trained TCN-TPA network, and the prediction result of each subsequence is derived by the fully connected layer.  
Step 8: The prediction results of each sub-series are summed up by the inverse normalization process, and the final accurate prediction result of the load is obtained.  
The EPT-VMD-FE-TCN-TPA prediction model is added with the recombination operation after step 4. That is, the fuzzy entropy (FE) values of each IMF component are calculated and the IMFs with similar values are reorganized to obtain each reorganization component (FE-IMF).
## 4 Method
### Instruments  
For data collection purposes, a pre-test (to assess students' prior knowledge), nine evaluation tests (3 media \(\times\) 3 sessions per medium), and a questionnaire were utilized. The pre-test was administered before the beginning of the project, while the evaluation tests were administered at the end of each session. The questionnaire was administered three times, specifically during the last session in which a given medium was used. The young age of the students, coupled with their nascent reading and writing abilities, necessitated the adaptation of data collection tools to be age-appropriate. However, particular measures were taken to ensure the integrity and validity of the collected data remained unscathed. Firstly, tablets were employed so that, instead of writing, students could \"tap\" their chosen answers. Secondly, all potential answers in the evaluation tests were accompanied by illustrative images to facilitate comprehension; while in the questionnaires, emoticons substituted the five-point Likert-type scale (Figure 2, the left-hand emoticon stands for none/not at all, while the right-hand emoticon stands for a lot/very much). Thirdly, both the evaluation tests and questionnaires were completed individually, with kindergarten teachers reading the questions aloud before the students responded. Considering the young  
Figure 1: Samples from the printed materials and videos. The **left** section of the figure displays a sample of the printed materials, specifically, information about the various body parts of guanas. The **right** section includes screenshots from the videos (the middle screenshot highlights a sample of the hotspots).  
age of the participants, no predetermined time limit for completion was set. Finally, even though the questions were phrased simply, the tests and questionnaires were administered to a small sample of non-participating students. This was essential to ensure the questions were understandable and to make necessary adjustments prior to the main study.  
Each evaluation test consisted of 15 multiple-choice questions, with one or two questions dedicated to each category of information presented in the session materials, as outlined in the Section 4.2. Each question had at least three possible answers. The pre-test had a similar structure, but, as it examined students' prior knowledge of all the animals included in the study, it had 35 questions. The questionnaire incorporated items from a validated, open-access modular scale designed to investigate factors influencing the learning experience in Metaverse's educational applications, including 360\({}^{\circ}\) videos [55]. For the purposes of this study, four factors were selected: motivation (six items), enjoyment/positive feelings (four items), ease of use (three items), and immersion (six items).
## 3 Results and Discussion
### Gc-Ims
#### 3.2.1 Profile Analysis  
As in previous papers, the GC-IMS analysis was generally developed as the fingerprint-based, nontargeted analytical approach for discriminating samples. Therefore, the whole fingerprints were applied to compare the differences in VOCs between samples. The variations of VOCs along wastewater treatment process were assessed by comparing topographic plots of the four groups (IW, PSO, AO and DNO). The profile information was obtained from the peak intensity for all compounds (Figure 4). As seen from Figure 4, the majority of signals located at the zone from the retention time of 100 s-400 s and the drift times of 1 ms-1.5 ms. The retention behavior of analytes was related to boiling point and polarity, so the compounds at the retention time from 100 s to 400 s were considered to be the polar compounds with lower boiling point. In view of signal nature and quantities, the overall VOC's profile of each process stage was different, and the removal efficiency of VOCs was significantly obtained. Comparing two stages (IW and PSO), it was seen that only differences of VOCs in signal intensities were observed. In fact, it was understandable that the stages before PSO were physical processes and would be insufficient to cause distinct transformation of compounds. After PSO, some signals at retention times around 140 s disappeared or the signal intensities were weakened (the area was marked in the red box, Figure 4), which demonstrated those compounds were absorbed or degraded by microorganisms during anaerobic and aerobic conditions in the bioreactors. On the contrary, some signal intensities were enhanced (the area was marked in the green box, Figure 4) which were attributed to the chemical reactions during biochemical process.