esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Production of Green Hydrogen with Wind Energy and Electrolysis  
Green hydrogen is emerging as a crucial transition technology in the search for environmentally friendly alternatives to fossil fuels. Its production is based on the use of renewable energy sources, such as wind, solar, biomass, and hydroelectric power. Among these, wind energy has gained significant importance in recent years, becoming a primary source for electricity generation through wind turbines, which transform the kinetic energy of the wind [15].  
In the field of wind generation, two main types of wind turbines are distinguished: vertical-axis wind turbines (VAWTs) and horizontal-axis wind turbines (HAWTs). Vertical wind turbines, whose blades rotate perpendicularly to the ground, typically have a lower energy generation capacity and are more compact, generating less noise. These characteristics make them ideal for domestic environments and urban areas with low-speed, turbulent winds. In contrast, horizontal wind turbines, with their blades parallel to the ground, are more efficient at capturing wind energy [16]. Available in both domestic (from 3 m) and industrial (up to 120 m) scales, these wind turbines are capable of generating larger amounts of energy [17].  
Green hydrogen production is based on water electrolysis, a process that requires renewable, emission-free electrical energy supplied through an electrolyzer. During this process, water (H\({}_{2}\)O) decomposes into hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\)) [18]. Some authors provide an overview of the various water electrolysis technologies for green hydrogen production. Among them, alkaline electrolysis (AEL), which operates at temperatures between 30 and 80 \({}^{\circ}\)C, uses an alkaline solution and nickel-coated stainless-steel electrodes (Ni), with asbestos and zirconium oxide (ZrO\({}_{2}\)) diaphragms [19]. This system design is considered favorable for large-scale applications. Another relevant technology is proton exchange membrane (PEM) electrolysis, developed by General Electric Company in 1996. PEM electrolysis stands out for its sustainability and favorable environmental impact in converting renewable energy into high-purity hydrogen. It uses high-activity noble metals, such as palladium and platinum, and operates at lower temperatures (20-80 \({}^{\circ}\)C). Finally, solid oxide electrolysis (SOE), introduced in the 1980s, has gained attention for its efficiency in converting electrical energy into chemical energy and producing ultrapurehydrogen [20]. SOE operates at high temperatures (500-850 \({}^{\circ}\)C) and pressure, using water in the form of steam and O\({}_{2}\) conductors, primarily nickel- and yttrium-stabilized zirconium. The diversity of electrolysis technologies, including alkaline, PEM, and solid oxide, offers distinct advantages in terms of efficiency, hydrogen purity, and operating conditions, allowing production to be tailored to diverse needs and contexts. The continued development and optimization of these technologies is crucial to the consolidation of green hydrogen as a key energy source in the transition to a low-carbon economy [21].
## 3 Results and Discussion  
### Further Characterization of the Selected Catalyst  
#### 3.1.1 XRD Studies  
X-ray diffraction is a non-destructive method used for the qualitative and quantitative analysis of polycrystalline samples. Indeed, it is suitable for identifying the mineralogical composition of clay assemblages. The study of the powder diffractograms of the raw clay RC shows characteristic peaks of the clay minerals of the natural clay as well as the associated minerals (impurities). Raw clay XRD pattern is presented in Figure 2, while relevant data of XRD patterns in collected in Table 1. It displays an intense peak at 3.34 A corresponding to quartz (Q). The peaks observed at d = 4.25, 3.24, 2.89 and 2.57 A are due to silica and quartz, major clay components. Aragonite typical intensities at 3.78, 3.51, 2.37 and 2.28 A are appreciated; they are responsible for its basic behavior. Furthermore, the diffractogram shows peaks at d = 6.37 A due to kaolinite, which is evidence of the heterogeneous nature of this clay. The presence of two distinct reflections at d\({}_{001}\) = 10.06 A (20 = 8.78), d\({}_{002}\) = 6.37 A (20 = 13.89), d\({}_{002}\) = 4.25 A (20 = 20.87\({}^{\circ}\)) indicates that the clay is a smectite. The peaks at 5.02, 2.57 and 3.34 A suggest illite presence.
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
## 3 Results and Discussions
### Cytotoxicity and Hemolysis Assessment of Micorpatrices
In the ZV, the burst cannot be controlled, and the application of a single cycle of US causes an intensive release of DOX which is equal to the total amount adsorbed. The effect of US exposure on the cell viability can be described as direct and indirect: (i) it causes an improvement in the ability of the microparticles to permeate through the cell membrane by sonoporation (direct) [22] and (ii) it causes a release burst of DOX (indirect), reaching high concentrations which are highly toxic for the cells. As can be seen from Figure 6, in the case of bare ZV and core-shell microparticles in absence of US and coating, the cell viability is higher than 80 %, and this persists up to 72 h, indicating the absence of in vitro cytotoxicity for the selected cell lines at the tested concentration. However, due to the nature of the material used, a non-cytotoxic profile was expected. In agreement with our previous studies and others already published [38; 39; 40], cells treated with free DOX solution demonstrate viability of around 40%, confirming the well-known cytotoxicity of the drug. Results reported in Figure 6 prove the beneficial role played by the core-shell carrier in driving the drug inside the cells. According to these results, the presence of the polymer shell could play a specific role in the cell uptake and in controlling the release of the drug. The cell viability tends to decrease over time when exposed to core-shell microparticles, conversely to the observed increase in viability 48 and 72 h after exposure to free DOX, even if the core-shell viability value is maintained as much lower in comparison. Blood compatibility represents a critical parameter for evaluating biological safety when materials are intended for intravenous injection. The hemocompatibility of the prepared carriers was evaluated by hemolysis test as shown in Figure 7. Results provide information about the influence of the surface properties on the interaction with the red blood cells. The presence of the organic layer on the IO reduced the hemotoxicity to lower than 5% for concentrations up to 10 \(\mu\)g/mL. A more interesting observation is that of the hemocompatibility in the core-shell microparticles reaching 60 \(\upmu\)g/mL, 6-fold greater than ZV. The obtained values are in contrast with other studies reported in the literature; previous works have shown that positively charged particles are more reactive than neutral or negatively charged ones, damaging blood cells [41; 42]. Additional experiments are required to better understand the hemocompatibility of the prepared particles and their interactions with the red blood cells.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 4 Data Representation Techniques
### Text Data Representation
#### 4.1.8 Efficacy of Various Embeddings for Detecting Cyberbullying
They trained Word2 Vec Skip-Gram embeddings with encoded linguistic information, as well as using dependency structure-based contexts. Their findings suggested that lemmatization can be an effective preprocessing method for increasing detection efficacy with pre-trained word embeddings. [PERSON] et al. (2018) conducted a study using lemmatization to extract the roots of each word and utilized the TF-IDF embedding technique. They used a UNICEF dataset of tweets categorized into six classes: age, ethnicity, gender, religion, type of cyberbullying, and non-cyberbullying. Their study found an accuracy of 97.10% and an F1 score of 97.12% in classifying tweets of cyberbullying. Overall, different embedding techniques can be effective for cyberbullying detection, particularly when it is used with deep learning algorithms. However, similar to any machine learning model, its efficacy depends on the data quality and the selection of hyperparameters.
## 3 Experiments and Results
### Motion Pattern Recognition Based on LSTM Neural Network
#### 3.2.2 Different Activation Functions and Different Sliding Window Lengths  
This section studies the accuracy of the motion pattern recognition of the LSTM model when using different activation functions and different window lengths. All time and frequency-domain feature parameters were used as the input of the LSTM model in order to compare the accuracy of sigmoid and rectified linear unit (ReLU) as the activation functions of LSTM with the accuracy of tanh as the activation function of LSTM. We modified the tanh in the activation functions of LSTM, which is applied on the cell state, and then multiplied with the sigmoid of the input to provide the output. Different activation functions were used in LSTM, including the sigmoid, ReLU and tanh. The sliding window lengths selected were 256 ms, 512 ms, 1024 ms, and 2048 ms, and the sliding window interval was 50 ms. Seventy percent of the dataset was used for training, and 30% was used for testing. In the 70% of the dataset used for training, 90% of the dataset was used for training, and 10% of the dataset was used for verification. The number of hidden layer nodes is 128, and the final recognition results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Correct Rate (\%)**} & \multicolumn{4}{c}{**Sliding Window Length (ms)**} \\ \cline{3-6}  & \multicolumn{1}{c}{**256**} & **512** & **1024** & **2048** \\ \hline \multirow{3}{*}{**Activation**} & Sigmoid & 75.18 & 86.22 & 94.61 & 97.14 \\ \cline{2-6}  & ReLU & 40.05 & 49.64 & 60.05 & 43.49 \\ \cline{2-6}  & Tanh & 77.75 & 88.84 & 96.57 & 98.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy of the LSTM model with different activation functions and different sliding window lengths. ReLU: rectified linear unit.  
Figure 10: Training accuracy rate of the long short-term memory (LSTM) neural network. (**a**) Changes in accuracy. (**b**) Comparison of classification results with the true motion model.  
It can be seen from the Table 4 that, when the activation function selected the ReLU function, the LSTM model showed a low recognition accuracy of the motion pattern. Even upon increasing the sliding window length, the improvement of the recognition accuracy was not obvious. For the recognition of motion patterns, it is not appropriate to use the ReLU function as the activation function. The LSTM model using the sigmoid function or the tanh function as the activation function has a higher recognition accuracy rate, and the difference between the two is not so obvious. The recognition accuracy using the tanh as the activation function was slightly higher. With the increase of the window length, the recognition accuracy of the human motion pattern also gradually increased. When the sliding window length was 2048 ms, the recognition accuracy of the human motion pattern using the sigmoid function as the activation function was 97.14%, and the recognition accuracy rate using the tanh function was 98.18%.
# Dynamic Load Modelling within Combined Transport Trains during Transportation on a Railway Ferry
## 5 The Discussion of the Results  
To ensure the safe transportation of containers as part of combined trains on railway ferries, we proposed the improvement of the supporting structure of the flat wagon. A feature of the flat wagon is the presence of superstructures, the inner surface of which is equipped with a viscous material with energy-absorbing properties. To substantiate the proposed technical solution, the authors have created a mathematical model that describes the process of moving the supporting structure of a flat wagon with containers during the rolling of a railway ferry. The limitation of the model is that it does not take into account the shock effect of sea waves on the hull of the rail ferry. In addition, the model cannot be used to study the dynamic loading of a tank container. The results of solving the mathematical model made it possible to determine the accelerations as the components of the dynamic loads that act on the supporting structure of the flat wagon with containers. In this case, the acceleration to the supporting structure of the flat wagon was 0.25 g (Figure 4), and on the container was 0.35g (Figure 5).  
To determine the fields of acceleration distribution, as well as to check the results obtained by mathematical modelling, we carried out computer modelling of the dynamic load of the load-bearing structure of a flat wagon with containers (Figure 7).  
We used the F-criterion in order to check the adequacy of the mathematical and computer models developed by the authors. It was found that the hypothesis of adequacy was not rejected. In subsequent  
Figure 8: Dependence of container accelerations on the roll angle of a railway ferry and differences in the results of the theoretical and computer solution in percent.  
studies in this area, it is important to consider the issues of the stability of the railway ferry when transporting trains of combined transport [55], as well as issues related to the energy efficiency of loading and unloading operations [56].  
In further studies of the issues of dynamic loading of containers in combined trains, we plan to conduct a physical experiment. This experiment will be carried out using the electrical tensometry method. The research is planned to be carried out on the railway ferry \"Heroes of Shipki\" while sailing in the Black Sea. The conducted studies are a useful technical basis in the creation of load-bearing structures of flat wagons and containers of a new generation. Moreover, the results obtained will contribute to the expansion of regulatory documents that cover the issues of rail transport by sea.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 3 Related Work  
IoT devices in critical scenarios may generate a huge amount of sensitive data that requires protection from malicious access and tampering. IoT security is an evolving subject in research because of the new threats and apprehension of large-scale disruptions  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack** & **Description** & **CIA Impact** & **Effects on Network Performance** \\ \hline \multirow{4}{*}{Rank Attack} & The malicious node attracts traffic by advertising its fake rank value. &  
due to smart devices' increased interconnectivity. Keeping in view the energy limitation and security aspects of the IoT devices, authors in [40] emphasized the energy-efficient fog computing solutions for massive IoT applications in 6G, and discussed the related challenges and opportunities. Similarly, to analyse the security enhancement, ref. [41] presented the derivation of optimal phase duration for artificial noise transmission based on security analysis.  
Numerous security techniques and solutions [42; 43; 44; 45] have been proposed for IoT security. The methods for addressing RPL attacks can be generally categorized as mitigation mechanisms and IDSs. The mitigation strategies rely on different approaches consisting of the methods based on identification, acknowledgment [46], trust, location, statistics/mathematics, and specifications. In addition, to maintain security, there are trust-based recommendation mechanisms presented [47].  
Similarly, other mitigation methods for IoT routing attacks for RPL typically rely on either combining procedures to RPL or the modifications in current RPL, for instance, revising Objective Function (OF) [48]. Ref. [49] proposed a new OF by merging energy consumption and ETX along with the fuzzy logic method. Although the method demonstrates increased RPL efficiency in terms of packet delivery ratio, network lifespan, latency, convergence time, overhead, and energy usage. However, their work does not focus on addressing routing attacks.
# An Energy-Efficient Routing Algorithm
## 5 Cs-Culm
### Uneven-Layered, Multi-Hop Routing  
It is well-known that the data volume forwarded by nodes close to sink is huge; thus, hot issues appear, which result in the imbalance of energy consumption. To solve this problem, an uneven-layered, multi-hop routing is proposed.  
In real ocean monitoring sensor networks, a fact we cannot ignore is that the sensor distribution span in a horizontal direction is always much larger than that in depth. As a result, rather than layering based on 3-D distance (include depth), we adopt a layering mechanism based on the horizontal distance between the sensor node and sink node. According to the geographical location, sensor nodes initialize their degree. The degree of one node is \(n_{i}\), if  
\[\sum_{i=1}^{n_{2}-1}i\cdot r\leq D<\sum_{i=1}^{n_{2}}i\cdot r \tag{12}\]  
\[D=\sqrt{(X_{i}-X_{\text{sink}})^{2}+(Y_{i}-Y_{\text{sink}})^{2}} \tag{13}\]  
where \(D\) is the horizontal distance of a sensor node to sink, and \(X_{i}\) and \(Y_{i}\) are the actual length and width coordinates. In this way, the degree space is \(r\), \(2r\), and so on, as shown in Figure 1. Though the nodes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Energy Parameters** & \(\eta\) & \(\alpha\) **(bps/Hz)** & \(\text{SNR}_{tgt}\) **(dB)** & \(P_{r}\) **(W)** & \(f_{0}\) **(kHz)** \\ \hline
**Value** & 0.5 & 0.5 & 8 & 0.5 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy consumption parameters. SNR: signal-to-noise ratio.  
close to sink burden higher network load, the shortest single-hop distance can reduce the energy consumption.  
The multi-hop routing algorithm is shown as follows:  
Step 1: If the degree of the current cluster head is 1, forward its packet to sink directly;  
Step 2: Otherwise, find lower depth nodes in the next degree within broadcast radius \(n_{2}r\);  
Step 3: If the number of nodes in Step 2 \(n_{3}\
eq 0\), select the node with maximum \(E_{c}\) as the next-hop node;  
Step 4: The next-hop node merges its received packet with itself if it meets the fused conditions in Section 4.1. Otherwise, forward the received packet simply;  
Step 5: If the number of nodes in Step 2 \(n_{3}=0\), the node will look for lower depth and lower \(D\) nodes in the same degree and repeat Step 3.  
Repeat the steps above until all the cluster headers forward their fused packets to sink node layer-by-layer.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Calculation of Multi-Hop Network QoSSS  
When reducing a complex equation in algebra, specific rules dictate which operations are solved first, thus simplifying to a more easily-solvable equation. The same is true when reducing the complexity of a communications network into an equivalent single-channel, point-to-point network. Through similar decomposition, it is possible to analyze a complex communication network that utilizes multiple intermediary nodes and multiple channels, similar to the example network link nodes A and D as shown in Figure 2, and to quickly compare the QoSS metrics to another network. When data are split across multiple channels, the end-user may remain agnostic to how the data is handled between the end-points within the larger, end-to-end network, in the same manner as an end-user is agnostic to multiple intermediary hops in current network architectures.
## 3 Results
### Results of the Numerical Simulations  
The values of the forces and moments induced by the wind, which act on the investigated scissor lift, are estimated based on the distribution of the static pressure. Mentioned distribution is obtained from CFD simulation. The exemplary distribution of the static pressure is depicted in Figure 9.  
The impact on the values of the forces and moment coefficients are analyzed for different sizes of the volume cells. Table 5 shows the values of the force and moment coefficients, which are obtained for different approximate cell sizes. The finite volume mesh creates the cells of the tetrahedron shape of variable edge length. It is assumed that on the surface of the studied structure, the length of the cell edge is about 1.5, 2.0,..., 3 mm while the length of the edge cells on the channel walls, inlet, and outlet boundaries is equal to about 70 mm. The computations are performed for variant 1, where it is assumed turbulence intensity \(I_{V}=6.32\%\), the hydraulic diameter \(D_{H}=1.712\) m, wind speed \(V=7.872\) [m/s], and angle \(\beta=30^{\circ}\).  
As can be observed, the obtained numerical solution shows a good global convergence, thus, for further computations, we assumed the following mesh sizes: 1.5 mm on the surface of the scissor lift and 70 mm on the rest of the external boundaries.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cell Size** & & & & & \\
**[mm]** & **Nodes** & **Cells** & \(C_{x}\) & \(C_{y}\) & \(C_{MC}\) \\ \hline
1.5 & 2,186,819 & 1,330,171 & 2.202 & \(-0.725\) & \(-2.299\) \\
2.0 & 1,172,976 & 717,266 & 2.206 & \(-0.718\) & \(-2.271\) \\
2.5 & 1,068,763 & 663,681 & 2.208 & \(-0.740\) & \(-2.296\) \\
3.0 & 775,510 & 483,151 & 2.185 & \(-0.708\) & \(-2.262\) \\ \hline \hline \end{tabular} \(\rho=1.225\) kg/m\({}^{3}\), \(\
u=7.872\) m/s.  
\end{table}
Table 5: The force and moment coefficients for different finite volume mesh.  
Figure 9: The exemplary distribution of the static pressure (\(\beta=30^{\circ}\)).  
The influence of the turbulence intensity on the force and moment values and moment coefficients is also investigated. As is reported in Tables 2-4, the estimated value of the turbulence intensity varies in the range from 4% to 10%. The computations are performed for the initial parameters the same as in the previous convergence analysis and for the finally assumed mesh sizes. The obtained results are shown in Table 6. As can be observed, together with the increase in the turbulent intensity, the values of the analyzed coefficients also increase slightly. Therefore, it seems that the choice of the turbulent intensity equal to \(I_{V}=6.32\%\) seems to be reasonable.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
* By enhancing classification accuracy through the precise characterization of contextual information in high-resolution remote-sensing (HRRS) images, we demonstrate the significance of leveraging the powerful capabilities inherent in deep convolutional neural networks (CNNs). The fine-tuned networks play a crucial role in capturing intricate features, contributing to the system's heightened accuracy and reliability in LC classification. * A detailed comparative analysis is conducted to evaluate our land-cover classification system against state-of-the-art models within the field of satellite sensing imagery interpretation. This comprehensive assessment scrutinizes the system's performance, accuracy, and efficiency in comparison to the most advanced solutions, thereby highlighting its significant advancements and competitive edge within the domain of land-cover classification. Notably, the fine-tuned models achieved baseline accuracy comparatively. The structure of the remaining sections of this work is as follows. The land-area classification system's comprehensive methodology is explained in Section 3. Section 4 reports experimental results and comparative analysis with baselines, and finally, Section 5 concludes the article.