esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Large-area atmospheric-pressure hydrogen plasma generated by DCSBDs in a geometry mimicking R2R treatment was studied for fast plasma reduction of graphene oxide film on flexible PET substrate [14]. It was also reported that DCSBD hydrogen plasma can be used for chemical reduction of a native surface CuO/CuO\({}_{2}\) layers [15] as well as for etching of thin SiO\({}_{2}\)/Si films [16] and reduction of diamond nanoparticles [17]. Reducing DCSBD plasma was already used for surface modification [18] and nanostructuring of PMMA, PET and PTFE polymeric substrates [19]. In this paper hydrogen atmospheric pressure plasma generated by a DCSBD with the electrode geometry applicable for the R2R processing was used for etching of thin surface PMMA polymer layer from the top of polymer-metal mesh substrate used as a flexible transparent conductive substrate. The etching was studied by scanning electron microscopy (SEM) and using a laser confocal microscope. A chemical reduction of plasma-modified surfaces was analyzed by energy dispersive x-ray analysis (EDX). The effect of process parameters on the properties of etched surface was investigated too. The selectivity of plasma etching was studied through the etching in mixtures of nitrogen and hydrogen. DCSBD can generate atmospheric plasma in all technically important gases, such as ambient air, nitrogen and hydrogen. Since the use of pure atmospheric pressure hydrogen can be of some safety concern, we also tested the etching capability and selectivity of some other technically and economically helpful plasma gases. The novelty of this work is that the reducing, large-area hydrogen plasma generated at atmospheric pressure was applied to a polymer-metal mesh substrate in order to carry out selective plasma etching together with the optimization of etching parameters, in particular: plasma-substrate distance, etching time and working gas specification. The selectivity of plasma etching is of great importance especially in case of processing of complex polymer-metal composite materials.
## 1 Introduction
### Research Goals and Proposed Solution  
Having in mind shortcomings of traditionally used tools for data visualization and need for efficient data management, our research is aimed towards definition, design and implementation of an efficient new tool. The requirements for such a tool are:  
* Web based interface with good response time in order to ensure high availability to all users accessing from different platforms;
* Efficient data exchange between client and server and balancing of load for data processing;
* Bidirectional seamlessly switching between spatial and temporal dimension in visualization;
* Sharing knowledge possibilities in terms of data validation and expertise updates;
* Integration of data from various sources.  
From the aspect of functionalities the goals to be achieved are:  
* The solution must provide a simple and intuitive user interface. Users must be able to use the application without any special help or learning effort;
* The solution must provide quick and easy insight into spatio-temporal data so that users can extract knowledge from the displayed datasets (state of the environment, trends, special events). At the same time, the user can easily validate the dataset by identifying and marking bad values;
* The solution must be able to be implemented as a real web application and for real use cases.  
To achieve these goals, we need to analyse the process of data management and the visualization of spatio-temporal environmental data. Tools and processes used by environmental experts in their work to manage data, as well as their efforts and problems. At the end, we can propose a methodology and its implementation to find a solution.  
The rest of the paper is organized as follows: in the next section we describe materials and methods used in this study--describe the oceanographic environmental spatio-temporal data, discuss the elements of interface for the proposed solution and design the means of verification. The following section describes the case study implementation. In the results section we describe the demonstration of our visualization solution on two different datasets and discuss the performance and capabilities. In the conclusion we outlined the advantages of the proposed solution and discuss the future work that is enabled by the approach we offered.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Data Insert and Update** & **Data Visualization** & **Graphs and Maps** & **Web Interface** \\ \hline
**General purpose** & Yes but file based and & & One or another, not & No \\
**software** & unstructured & & connected & \\ \hline
**Ocean data view** & No, only insert, file & & & No (Web interface have \\  & based & & & limited preloaded datasets) \\ \hline
**WISE Marine** & No, only visualization & Yes & Yes, no interactivity & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main properties of related solutions.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 2 Application Scenarios  
In the following subsections, three different scenarios where UAVs have a major impact on system design are briefly outlined. We do not consider those applications wherein network optimization issues are less challenging, e.g., UAVs used as static data collectors/aggregators or gateways.
## 4 Experiments and Results
### Experiment 2--Evaluation of the Rating Accuracy of MPUSMs  
Test the accuracy of MPUSMs with the DRBM-Social surrogate on different vector dimensions, and they represent the accuracy of multiple local models and a single overall model, respectively. In each dataset, a user with different numbers of categories is selected. The IDs of the three users are 'A9Q28 YTLYRE07' (Music), 'A2582 KMXLK2P06' (CDs), and 'A320 TMDV6 KCFU' (Kindle), and their corresponding number of categories are 187, 233, and 149, respectively. To enable a reasonable comparison, the local model of MPUSMs has the same parameters as DRBM-Social and the same parameters in different vector dimensions. As the user has been configured to perform ten interactions, there are ten historical item sets and item pools. Using historical item sets as a basis for rating items in the item pools. Figure 5 shows the situation for three users. Multi and Solo in the figure denote MPUSMs and DRBM-Social.  
In Figure 5, three scenarios emerge in terms of predicting rating results: Better performance from MPUSMs (Figure 4(a)), Similar performance of MPUSMs and DRBM-Social (Figure 4(b)), and Better performance from DRBM-Social (Figure 4(c)). It can be seen that the accuracy of MPUSMs rating is not directly related to the vector dimension size. The better performance of MPUSMs is due to the fact that it is constructed by dividing user preferences in a manner similar to an attention mechanism. In this way, we can simulate the  
Figure 4: Effect of six gene operator combinations on the number of non-repeating individuals in the population.  
Figure 5: RMSE performance of MPUSMs and DRBM-Social on three users with different vector dimensions during 10 interactions.  
behavior of users who evaluate items based on only a few of their preferences. Conversely, MPUSMs do not always perform better. Due to the fact that the K-means clustering may not correctly classify user's preferences or the limitations of the local surrogate model, this may sometimes lead to poor prediction. Nevertheless, in general, MPUSMs still have an accuracy comparable to the global surrogate model.
## 4 Observation of Floating-Oil-Slick Evolution Off the Shore of Qatar
### Case #Q2: 5-6 July 2021
At the same time, while the current velocity increased a little, from 0.31 m/s to 4 m/s, its direction changed more significantly, from the east (07:00, 5 July), to the southeast and southwest, and then to the northwest and north (06:00, 6 July). According to this observation, the oil detected by Sentinel-2- and Landsat-8 oil tended to move in the same direction as the wind and current (06:00, 6 July). Figure 11: Collocation of Sentinel-1/2 and Landsat-8 images (Case #Q2, 5–6 July 2021) for observations of oil-slick evolution after about (**a**) 4 h (between Sentinel-1/2), (**b**) 24 h (between Sentinel-2 and Landsat-8), and (**c**) 28 h (between Sentinel-1 and Landsat-8). Case #Q2 indicated that a wind speed of 5-8 m/s was an optimal condition for detecting oil slicks, especially for Sentinel-1 SAR (Figure 10a). Regarding their evolution, the small-scale oil objects quite significantly changed after several hours with the normal met-ocean conditions, i.e., wind speeds of 5-8 m/s and current velocities of 0.26-0.31 m/s. Evaporation was assumed to be the principal factor explaining the disappearance of the small-scale oil and the reduction in the size of the moderate-scale oil (after one day of observation). Compared to Case #Q1, the impacts of the wind and current on the (assumed) movement of the oil detected in Case #Q2 could be more easily observed, since both wind and current  
Figure 12: Surface wind and current speed and direction (indicated by the arrows), corresponding to the extracted Sentinel-1/2 and Landsat-8 scenes (Figure 10a–c), respectively. **(i)** ERA-5 wind vectors for **(a)** 02:00, 5 July 2021; **(b)** 07:00, 5 July; and **(c)** 07:00, 6 July. **(ii)** CMEMS current vectors on **(d)** 02:30, 5 July; **(e)** 07:30, 5 July; and **(f)** 07:30, 6 July. **(iii)** Mean values of wind and current fields from 02:00, 5 July, to 09:00, 6 July. speeds were relatively stable. Furthermore, the wind and current directions had only a few changes after 28 h (except for the current from 06:00 on 5 July, to 00:00 on 6 July). Additionally, the small and moderate scales of the oil detected might be another factor used to more easily determine the oil-drift direction.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Cluster Building Time  
The cluster construction time refers to the time it takes to form the cluster and choose a CH. A lengthy cluster establishment time indicates a high level of protocol complexity. Figure 14 shows how node density influences cluster build time. In our proposed method, we select CH using ABBOA. We select CH using the fitness value, just like other biomimetic algorithms. ABBOA's computational cost is considerably lower than that of other bio-inspired methods. Most cluster protocols significantly increase the time required to create a cluster when there are many nodes.
## 4 Methodology
### LSTM Classification Model  
In this paper, we use the LSTM network with two layers (LSTM-1 and LSTM-2) of neurons for temporal feature extraction, as shown in Figure 3. Each layer of LSTM includes 64 hidden neurons. The neuron activation function in each layer is represented by the RELU function to perform the nonlinear operations, while the last layer of the LSTM network is a fully connected (dense) layer. The dense layer is tailed by the Softmax layer to compute the probability for each class during the classification process. The learning rate of the Adam optimizer in the LSTM network is 0.003, and the loss functions are the binary cross-entropy and categorical cross-entropy for binary and multiclassification, respectively.  
The classification model receives the input as extracted features from the preprocessing part to perform the classification. Since we seek to classify the time-series data, we use an LSTM classifier that yields classification with significant performance for this type of data. The classification model uses the training dataset in the learning phase by fitting the input features vector \(x\) with its label \(y\). We use the validation dataset to give an estimate of model performance while tuning the model's hyperparameters. The output of the classification model \(f\) will be either benign or attack class in the case of binary classification or several classes that include DDoS attacks and benign. We then evaluate the predictions of the classification model using the common performance metrics: accuracy (ACC), precision (PR), recall (RE), and F1-score.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Binary**} & \multicolumn{2}{c}{**Multiclass**} \\ \hline
**Class** & **Instances** & **Class** & **Instances** \\ \hline \multirow{8}{*}{Attacks} & \multirow{8}{*}{50,006,249} & TFTP & 20,082,580 \\ \cline{3-4}  & & DrDoS\_SNMP & 5,159,870 \\ \cline{3-4}  & & DrDoS\_DNS & 5,071,011 \\ \cline{3-4}  & & DrDoS\_MSSQL & 4,522,492 \\ \cline{3-4}  & & DrDoS\_NetBIOS & 4,093,279 \\ \cline{3-4}  & & DrDoS\_UDP & 3,134,645 \\ \cline{3-4}  & & DrDoS\_SSDP & 2,610,611 \\ \cline{3-4}  & & DrDoS\_LDAP & 2,179,930 \\ \cline{3-4}  & & SYN & 1,582,289 \\ \cline{3-4}  & & DrDoS\_NTP & 1,202,642 \\ \cline{3-4}  & & UDP-lag & 366,461 \\ \cline{3-4}  & & WebDDoS & 439 \\ \hline Benign & 56,863 & Benign & 56,863 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Unbalanced instances of benign and DDoS attacks in CICDDoS2019 dataset.
## 4 Simulation Studies
### Quarter Car Model Simulation  
This quarter car model was selected to compare the frequency responses of three dampers. Table 1 shows the parameters of the quarter car model considering the in-wheel motor weight. The three suspension performance measures (chassis acceleration, tire force variation, suspension stroke) were evaluated by 1/3 octave band RMS values on the frequency axis in Figures 5-7. In these figures, the solid line denotes the double sky-hook case, dashed line denotes the conventional sky-hook, and dash-dot line for passive damper. In these figures, case (a) is for smooth road profile with high speed and case (b) is for rough road profile with slow speed.  
Figure 4: Nonlinear damping characteristics (solid: passive, dashed: max., dashdot: min.).  
Figure 3: Time delay effect of variable damper (solid: with time delay, dashed: without time delay).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Effective weight of wheel (\(m_{u}\)) & 85 kg \\ Weight of sprung mass (\(m_{s}\)) & 563 kg \\ spring stiffness & 32,366 N/m \\ Passive damper coefficient (effective) & 1577 Ns/m \\ Tire vertical stiffness & 248,000 N/m \\ Tire vertical damping coefficient & 322 Ns/m \\ Max. damping coefficient & 3858 Ns/m \\ Min. damping coefficient & 300 Ns/m \\ Sky-hook damper on sprung mass (\(B1_{sky}\)) & 18,015 Ns/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters for the quarter car.  
Figure 5: RMS of the tire normal force variation. (**a**) smooth road; (**b**) rough road.  
Figure 6: RMS of chassis heave acceleration. (**a**) smooth road; (**b**) rough road.  
In this simulation, \(B1_{\text{sky}}\) and \(B2_{\text{sky}}\) have the values of 12,859 Ns/m and 1440 Ns/m, respectively. These values were determined by the trial-and-error steps described in Section 2 to have a lower chassis acceleration, tire force variation, and suspension stroke with the double sky-hook damper than those with the conventional sky-hook and passive dampers in the sprung and unsprung mass resonant frequency regions. Figures 5-7 show the following characteristics by using the double sky-hook algorithm:  
1. The double sky-hook damper generated better suspension performance than the passive damper for all three performance measures except for slight degradation between the sprung and unsprung mass resonant frequencies, even with increased unsprung mass by the in-wheel motors.
2. The double sky-hook damper had better road-holding property, as shown in Figure 5, and smaller suspension stroke, as shown in Figure 7, than the conventional sky-hook damper not only in the unsprung mass resonant frequency region, but also in the sprung mass resonant.
3. The double sky-hook damper had a similar body acceleration as shown in Figure 6 to the conventional sky-hook damper in all frequency regions.  
In conclusion, the double sky-hook damper can achieve better a road-holding property, as intended.
## 2 Theoretical Background and Mathematical Models
### Rusck Closed Formulation and Rusck Coupling Model
#### 2.5.1 Rusck Coupling Model
Moreover, some restrictions should be applied when using Equation (7) only for perfect ground conductivity and a vertical LEMP channel for the ground. These restrictions are applied when the LEMP velocity is close to the velocity of light. Figure 6 shows the Rusck transmission line pattern [28]. The Rusck model mentioned above is superior to other coupling models and ignores the vector potential, focusing solely on the source-dependent portion of the even electric field. Despite this, the Rusck model correctly provides a coupling effect. Figure 6: A transmission line section represents the Rusck coupling model.
## 4 Results
### Climate Change Scenarios (Analysis II)
#### 4.2.1 TCCA and MTO Methods
The rankings of the regions regarding the shallowest (Toronto) and deepest (Winnipeg) frost penetration remains consistent with the MTQ and TCCA methods when analysing the results using MTQ, while the magnitude of the predicted frost penetration depths varies from one method to another. Similarly, Calgary was found to have the smallest percentage of decrease in frost penetration depth in both climate change scenarios (\(-6\%\) and \(-12\%\), respectively), while Toronto had the greatest (\(-19\%\) and \(-29\%\), respectively). On the other hand, in terms of frost heave predictions, the historical values calculated for the selected locations in this study range from 17 to 26 cm, as shown in Figure 9. Similar to frost penetration depth calculations, Toronto showed the smallest and Winnipeg showed the largest frost heave values. Furthermore, the climate change scenarios suggest that Calgary may experience the smallest percent decrease in frost heave (\(-4\%\) and \(-7\%\), respectively), while Toronto may undergo the highest rate of changes (\(-12\%\) and \(-20\%\), respectively). The results of frost penetration depth and frost heave for the clayey silt (ML, ML-CL) soils are presented in Figures 10 and 11, respectively. Figure 10: Frost penetration depth in clayey silt, MTQ. Figure 9: Frost heave in clay, MTQ. Once again, Calgary had the smallest percent decrease in frost penetration depth in both climate change scenarios (\(-4\%\) and \(-7\%\), respectively), while Toronto had the greatest (\(-13\%\) and \(-21\%\), respectively). Figure 11 shows that the frost heave that was calculated based on historical data ranged from 28 to 44 cm. The climate change scenarios show that pavement design implications are not consistently negative throughout all regions. To this end, while there might be slight differences for Calgary, which had the smallest percentage of decrease in frost heave (\(-4\%\) and \(-7\%\), respectively), the change may alleviate some frost heave-related issues in the case of Toronto, which had the largest reduction in heave values (\(-13\%\) and \(-21\%\), respectively). Nevertheless, this observation is focused only on the low temperature extreme of pavement performance, while climate change scenarios may have pronounced adverse impacts with respect to high-temperature behaviour, which needs to be further studies and is out of the scope of the current research. Figure 12 compares the frost heave calculations for the clay and clayey silt soils in the historical scenario. When comparing the frost heave of the clay to the clayey silt soil, it is possible to note a great difference. The frost heave calculated for the clayey silt was on average 69% higher than what was calculated for the CL soil. This can be attributed to the fact that silty soils are considerably more susceptible to frost heave than clays, in general. Figure 11: Frost heave in clayey silt, MTQ. Figure 12: Frost heave predictions for clay and clayey silt soils.
## 3 Proposed Algorithm
### An Improved Dynamic Joint Resource Allocation Algorithm Based on SFR
```  
**Algorithm 1**Single-cell Resource Allocation Solution of DJRA  
Next, we extend the single-cell solution to find the global joint resource allocation scheme for multi-cell scenarios by using the method illustrated in [16]. After resource allocation is achieved for each cell, DJRA takes the resource allocation in cell \(i\) as inputs to its adjacent cells and calculates the multi-cell data rate \(\sum\limits_{i\in\mathbb{C}}R_{i}^{in}+R_{i}^{o}\). It repeats the above steps until the multi-cell data rate \(\sum\limits_{i\in\mathbb{C}}R_{i}^{in}+R_{i}^{o}\) does not change any more. Then the algorithm stops iterating and we get the optimal solution. Finally, DJRA applies the sector partition method in the cell-center to further mitigate interference, and the proportional fair algorithm to guarantee that each user has available resources. Figure 2 shows the sector division in the center region. Each center zone is divided into three sub-regions: A, B and C. Different colors present different resource-using regions. As seen from the figure, increasing the pass loss of interfering signals can eliminate ICI, and thereby improve system performance.
## 2 Materials and Methods
### HS-SPME/GC-MS Determination of Volatile Chemical Composition  
The chemical volatile fraction of the samples was analyzed by using the SPME sampling technique. Approximately 0.5 g of each sample was placed into a 7 mL glass vial with a PTFE-coated silicone septum. Volatiles were extracted using SPME device from Supelco (Bellefonte, PA, USA) equipped with 1 cm fiber coated with 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxosen/polydimethylsiloxane). Prior to use, the fiber was conditioned at 270 \({}^{\circ}\)C for 30 min. The equilibration time for all samples was obtained heating to 35 \({}^{\circ}\)C for 15 min. After this period, the fiber was exposed to the headspace of the samples for 50 min at 35 \({}^{\circ}\)C to capture and concentrate the volatile compounds. The SPME fiber was then inserted into the GC injector, maintained at 250 \({}^{\circ}\)C in splitless mode, for analyte desorption. The analyses were performed using a gas chromatograph coupled with a mass spectrometer Clarus 500 model [PERSON] (Waltham, MA, USA), equipped with a flame detector ionization (FID). A Varian Factor Four VF-5 capillary column was housed in the oven of GC. The programmed temperature applied started at 35 \({}^{\circ}\)C held for 1 min, then increased to 220 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and held for 15 min. Helium was used as the carrier gas at a constant flow rate of 1 mL/min. Components were identified by matching their mass spectra with those from the Wiley 2.2 and Nist 11 mass spectra libraries database and by calculating the linear retention indices (LRIs) using a series of alkane standards analyzed under the same conditions as the samples. The calculated LRIs were then compared with retention data from the literature. The peak areas of the FID signal were used to calculate the relative concentrations of the components, expressed as percentage, without the use of an internal standard or any correction factor. All analyses were performed in triplicate.