esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulated Altitude Training and Sport Performance: Protocols and Physiological Effects
## 2 Hypoxia Environment Training Protocols
### Exposure to a Hypoxia Environment in the Recovery and Sleep States  
The recovery hypoxia training protocol considers exposure to a hypoxia environment during the recovery state or outside the training time, and further observes physiological and sports performance changes. Exposure to a hypoxia environment during the recovery state was also considered during the training period. In a study conducted by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [38], 15 runners in middle- and long-distance training, both in the normoxia group and the hypoxia group, were exposed to submaximal exercise in a normoxia environment with a VO2 max of 75%, while the hypoxia group was exposed to a hypoxia environment only during the recovery state in a tent with a hypoxia environment of 12.3% oxygen concentration. The results of the study showed that the recovery hypoxia exposure group showed a 7.0% improvement in maximum oxygen uptake during the recovery state, with a significant correlation at 3000 m. However, no significant changes were observed in hematological parameters.  
Similarly, studies related to hypoxia exposure were conducted outside the training time, e.g., [PERSON] et al. [39] and [PERSON] et al. [40], where the participants were specially trained long-distance athletes, cyclists, triathletes, and long-distance skiers exposed to 14.3% oxygen in a hypoxia environment for 20-23 days of sleep. [PERSON] et al. [40] showed that training time with progressive 20-day sleep exposure to a hypoxia environment resulted in a 12.4% improvement in oxygen uptake and improved running benefits in long-distance athletes. In the other study, 23 days of sleep exposure to 14.3% oxygen concentration in a hypoxia environment resulted in a significant increase in peak oxygen uptake (VO2 peak) and a significant increase in oxygen uptake benefit at submaximal training intensities in trained athletes. In contrast to the recovery state and sleep exposure to hypoxia environments, the oxygen benefits of submaximal training intensity were significantly increased [39]. [PERSON] et al. [41] set the time of daytime rest and relaxation for trained swimmers and long-distance runners, who were exposed to hypoxia environment for 3 h per day, 5 days per week, for 4 weeks, showed a 0.02% improvement in VO2 max. Hypoxia exposure during the recovery and sleep states reduces the interference of training intensity, which means that the training intensity and technical training can be maintained during the training period.
## 3 Results and Discussion
### Effect of Thermal and High-Pressure Pasteurization
#### 3.3.2 Lysozyme Activity  
The percentage of lysozyme activity retention found in donkey colostrum after TP and HPP is presented in Figure 2, with the results showing a significant decrease (\(p<0.05\)) in lysozyme activity of 37% after TP treatment, a lower reduction compared to that reported for human colostrum (44 to 85%) using the same TP conditions [7; 28]. For HPP, using pressure conditions of 400 and 550 MPa, losses from 25 to 34% of lysozyme activity were observed. For the highest pressure/holding time conditions of 625 MPa for 10 and 30 min, a reduction of about 42% was observed, which was not statistically different (\(p>0.05\)) from TP treatment. In human colostrum, changes to lysozyme activity at pressure levels of 200-600 MPa/2.5-30 min were not observed [7]. Thus, HPP preserves equal to better lysozyme activity compared to TP.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Protein** & **Pressure (MPa)** & \(k\) **(min\({}^{-1}\))** & **R\({}^{2}\)** & **D-Value (min)** \\ \hline IgG & 550 & \(2.31\times 10^{-2}\pm 7.93\times 10^{-3}\) & 0.910 & 99.68 \(\pm\) 18.01 \\  & 625 & \(7.57\times 10^{-2}\pm 3.83\times 10^{-3}\) & 0.997 & 30.42 \(\pm\) 1.57 \\ IgM & 550 & \(6.05\times 10^{-2}\pm 5.60\times 10^{-3}\) & 0.992 & 38.06 \(\pm\) 3.56 \\ \hline \end{tabular}
\end{table}
Table 2: HPP inactivation kinetic parameters of IgM and IgG in donkey colostrum.
## 3 Related Work  
In this section, we discuss different GDOP and WGDOP techniques. The positioning accuracy depends on the suitability of the selection of optimal measurement units. Several methods based on GDOP and WGDOP have been proposed to improve the GPS positioning accuracy [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]. These methods need matrix inversion to calculate GDOP and WGDOP. However, the conventional matrix inversion method is rather time consuming and presents a significant computational burden. It increases the computational complexity and is not applicable to real hardware requirements. The handheld GPS devices and mobile phone with GPS chip possess only limited processing ability and power dissipation capability. In order to reduce the computational overhead and improve location performance, many researchers utilize the approximate method or simplify the matrix to avoid the matrix inversion calculations.
## 5 Classification of Smart Grid Cyberattack
### Data Secrecy Attacks
#### 5.2.8 Sniffing  
With this attack, data on communications between a sender and a receiver can be unlawfully intercepted. Examples of sniffing attacks may include packet sniffing and password sniffing. Sniffing is a network-based attack which, due to its spy-based nature, can be used as a preparatory step for other more aggressive attacks such as an FDIA. In smart grids, sniffing AMI (Advance Metering Infrastructure) networks allows attackers to tap into a communication line or protocol to read smart meter important data illegally. The main targets of sniffer attacks include PMUs (phasor measurement units), TCP/IP packets, and smart meters. Without efficient protection such as encryption, sniffing can collect crucial information about a victim's smart energy system. To reduce secret key/password theft possibility, it is advisable to replace them frequently while using stronger credentials [90; 101].
## 2 Problem Formulation  
Body coordinate system and geographic coordinate system should be established as the reference coordinate system for attitude change. The coordinate system determined by the body axis of the carrier is the body coordinate system \(O_{b}-X_{b}Y_{b}Z_{b}\) and the coordinate system determined by east-north-sky direction is geographic coordinate system \(O_{g}-X_{g}Y_{g}Z_{g}\). These two coordinate systems are shown in Figure 1. The accelerometer can obtain linear acceleration and the gyro can obtain angular velocity in the body coordinate system. The angular attitude and angular velocity in the geographic coordinate system can be obtained by solving the output data of these sensors. Roll angle \(\gamma\) is the angular attitude rotating around \(X_{g}\)-axis, pitch angle \(\theta\) is the angular attitude rotating around \(Y_{g}\)-axis and yaw angle \(\psi\) is the angular attitude rotating around \(Z_{g}\)-axis.  
The measured value of gyro is the angular velocity of body coordinate system. It has the advantage of fast response and low cost. However, it suffers from drift due to accumulated errors by integration to obtain angle information. The measured value of accelerometer is the linear acceleration of body coordinate system which does not suffer from zero drift. However, the yaw angle \(\psi\) cannot be measured by accelerometer due to the rotation plane of \(\psi\) is orthogonal to gravity. Therefore, it is necessary to use the signal processing method of fusing multiple sensor data to improve the accuracy of angular attitude measurement.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 2 The Development of Neural Networks
### Deep Neural Network (DNN)  
Since a single-layer perceptron cannot solve linear inseparability problems, it cannot be used in industry. Scholars have expanded and improved the perceptron model by increasing the number of hidden layers and corresponding nodes to enhance the expression ability of the model, and thus the deep neural network (DNN) was created. The DNN is sometimes called a multi-layer perceptron (MLP). The emergence of the DNN overcomes the low performance of a single-layer perceptron. According to the position of different layers in the DNN, the neural network layers inside the DNN can be divided into three types: the input layer, hidden layer, and output layer, as shown in Figure 2. Generally speaking, the first layer is the input layer, the last layer is the output layer, and the middle layers are all hidden layers. Layer to layer is fully connected, that is, any neuron in layer n must be connected with any neuron in layer n + 1. Although the DNN appears complicated, from a small local model, it is the same as the perceptron, namely, a linear relation z = \(\sum\)W\({}_{1}\)\(X_{1}\)+ b plus an activation function \(\sigma\)(z).  
With the deepening of the layers of neural networks, the phenomena of overfitting, gradient explosion, and gradient disappearance has become increasingly more serious, and  
Figure 2: Typical model of a DNN.  
the optimization function is increasingly more likely to fall into the local optimal solution. In order to overcome this problem, scholars propose convolutional neural networks (CNN) based on the receptive field mechanism in biology.
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems
### Application Example: IoT System for Monitoring Air Quality
#### 4.2.1 User Story: Integration of IoT Devices for Air Quality Monitoring  
Consider a scenario in which a homeowner, concerned about environmental health, seeks to implement an IoT-based system to monitor indoor air quality. Such a system would enable proactive measures to improve air quality and maintain a healthy living environment for the home. The key features of this system include the ability to receive customized alerts and seamless integration with other smart home devices, ensuring informed decision making and improved indoor air management.
# Performance Evaluation of Bedding Sand and Load Transfer Characteristic of Concrete Block Pavements
## 3 Results
### Evaluation of Joint Performance
#### 3.2.2 Rotational Interlocking of Block  
The load is distributed in the vertical and horizontal directions at the point where the load is applied owing to interlocking between blocks. In this case, the larger the force distributed in the vertical direction is, the greater the block rotation. Rotational interlocking effects based on the block shape were compared only when the blocks were laid in a stack pattern. Figure 9 shows the rotation angles based on the block shapes and degrees of joint filling. Depending on the degree of joint filling, the rotation angles of the blocks decrease linearly, and when 100% of the joint is filled the rotation angles appear to converge between 0.6 and 0.7\({}^{\circ}\). In addition, as the change in rotation angle according to the degree of joint filling is small for U-shaped blocks and Dasuri blocks, it is expected that they will contribute to stable pavement performance if laid in areas where the joint sand loss is likely.  
Figure 8: LTE according to the degree of joint filling.  
Figure 9: Rotational angles according to block shape and degree of joint filling.  
Figure 10 shows the relationship between the shape factor and the rotation angle, which is found to be inversely proportional. This suggests that the rotational interlocking of a concrete block pavement is influenced by the block shape. In Figure 11, the change in the rotation angle depending on the degree of joint filling is seen to be the greatest in the stack pattern. It appears that the basket weave and herringbone patterns have a similar change in gradient, and the change in rotation angle is small. In addition, the rotation angle according to the block construction pattern and degree of joint filling is initially in the range of 1.0-1.2\({}^{\circ}\) depending on the block shapes or construction patterns, but it converges to approximately 0.6\({}^{\circ}\) if the joint filling is 100%. This indicates that the degree of joint filling has a greater influence on the rotation angle of a concrete block pavement than block shapes and lying patterns. Similar to the load transfer characteristics, rotational interlocking also has a greater impact on the degree of filling of joint sand than the other two. Figure 12 shows the rotation angles by placing a parameter only on the degree of joint filling regardless of the block shape and lying pattern. A linear regression analysis shows a regression coefficient of 0.61; when 75% or more of the joints were filled, the rotation angle of the block was not significantly different from when 100% of the joints were filled. Therefore, if the joint is required to be filled 75% or more, the rotation of the block must remain at 0.6\({}^{\circ}\) or below. This means that if the joint is not filled or interlocking forms between blocks, the rotation should not exceed 0.6\({}^{\circ}\).  
Figure 11: Rotational angles according to lying pattern and degree of joint filling.  
Figure 10: Relationship between rotation and shape factor (joint filling = 100%).
# A Wireless EEG System for Neurofeedback Training
###### Abstract  
This paper presents a mobile, easy-to-maintain wireless electroencephalograph (EEG) system designed for work with children in a school environment. This EEG data acquisition platform is a small-sized, battery-powered system with a high sampling rate that is scalable to different channel numbers. The system was validated in a study of live z-score neurofeedback training for quantitative EEG (zNF-qEEG) for typical-reading children and those with developmental dyslexia (DD). This system reads and controls real-time neurofeedback (zNF) signals, synchronizing visual stimuli (low spatial frequency (LSF) illusions) with the alpha/ theta (2-\(\alpha\)-\(\uptheta\)) score neural oscillations. The NF sessions were applied during discrimination of LSF illusions with different contrasts. Visual feedback was provided with color cues to remodulate neural activity in children with DD and their cognitive abilities. The combined zNF-qEEG and training with different visual magnocellular and parvocellular tasks (VTs) compensated for the deficits in the temporal areas affecting the occipitotemporal pathway more in the left-hemispheric ventral brain areas of the post-training children with dyslexia in the low-contrast LSF illusion and dorsal dysfunction in the high-contrast LSF illusion. The better \(\alpha/\uptheta\) scores for postD in the temporoporarietal and middle occipital regions can be associated with an improvement in special frequency processing, while the better scores in the precentral and parietal cortices were due to an advancement in the temporal processing of the illusion. The improvements in the reading speeds were twice as high after 4 months of qEEG z-NF-VT training, with three times fewer omitted words and errors.  
EEG system architecture; alpha/theta oscillations; neurofeedback procedure; developmental dyslexia +
Footnote †: journal: Applied Sciences
## 4 Discussion
This allows us to achieve a higher number of transfected surviving cells at lower plasmid DNA concentrations (see Figure 1A). It is interesting to note that equivalent electrotransfection efficiency can be gained either by increasing the number of pulses and, accordingly, the electrophoretic force, or increasing the plasmid concentration and consequently decreasing the distance between electroporated cell and the nearest plasmid. This indicates that an increase in plasmid concentration can compensate the lower number of electric pulses and vice versa. Here, we also show the cell viability decrease when a high concentration of plasmid DNA is in the extracellular medium while electric fields are applied. It is known that during the application of an appropriate electric field on cell suspension containing plasmid DNA, DNA/membrane complexes form as a result of electroporation process. After the formation of DNA/membrane complex, the plasmid DNA enters the cytoplasm via endocytotic processes [15; 36; 37; 38]. Different types of endocytosis have been implicated in the plasmid internalization, including macropinocytosis, clathrin mediated and caveolin/raft mediated endocytosis [39]. Therefore, plasmid DNA can be found in various endosomes: early, late, recycling endosomes, and lysosomes. Some of these plasmids successfully enter nucleus and expression of encoded foreign gene starts. However, endocytosis mediated plasmid DNA entrance can also trigger cell defense mechanisms against bacterial or viral infections, leading to programmed cell death [40; 41; 42]. This, in turn, may lead away from the desired process of electrotransfection to the programmed cell death [43]. Our results indicate that higher concentration of plasmid DNA in cell suspension leads to a significantly higher expression of foreign gene, hence it can be assumed that higher amount of plasmid DNA has entered into the cell. Having that in mind, the cell defense mechanisms are also more likely to be triggered, since higher amount of naked DNA is present in the cell cytosol. This interpretation allows the explanation of both higher cell death and higher transfection efficiency when a large number of plasmids are present in the extracellular medium. Based on our experimental data, two HV pulses with 200 ug/mL plasmid concentration are optimal conditions in order to achieve good transfection efficiency while maintaining the cell viability.
## 3 Classifications of Routing Protocols in UAV Networks
### Routing Based on Network Structure
#### 3.1.1 Static Routing Protocol  
Static routing protocols have a static routing table that is predefined and uploaded to the UAV before operation, and they cannot be changed. Static routing protocols often have low fault tolerance and limited adaptability in dynamic environments [15]. Below is a description and analysis of some prominent static routing protocols in recent years.  
The Load and Carry Delivery routing scheme (LCAD) is specifically designed for UAV networks. It allows flight path establishment from the ground preparation stage. The authors in [36] have developed a full duplex (FD) UAV relay network based on LCAD. The UAV acts as a mobile relay device to transmit information based on the FD technique. Specifically, the UAV will fly in a straight horizontal line at a constant height H with a fixed speed v. The relay service time for the UAV to complete the information exchange between S and D is divided equally into two stages: the first stage (FS) and the second stage (SS), as shown in Figure 9. Data will not be transmitted until the channel is favorable (or the UAV is closer to a node). The simulation results show that it is superior to the half-duplex and static schemes in terms of energy efficiency.  
The Data-Centric Routing (DCR) is a static multipoint routing solution to increase the operational efficiency of UAV networks with cluster-based architecture. It is capable of supporting a variety of applications in a defined UAV deployment environment. DCR is deployed when multiple UAVs simultaneously request the same data packet. Based on the advanced publish-subscribe model, DCR allows automatic connection establishment between data publishers and data subscribers. When data is transmitted from the source, target UAVs can receive it either directly from the source or indirectly through intermediate nodes. In the study [37], the authors proposed a UAS (Unmanned Aircraft Systems) model for deployment in firefighting, natural disaster, or search and rescue situations (Figure 10).  
The UAS platform enables rapid deployment of small, pre-configured UAVs to establish a wireless network in a short time. Mission-specific UAVs are equipped with additional communication devices to act as access points, while the others act as network routers. In this research scenario, the UAS provides a solution based on the Data Distribution Service (DDS) standard. It builds a distribution path to connect publishers with subscribers. However, the actual distribution path depends on the services provided by  
Figure 9: Full-duplex UAV relay network based on LCAD scheme.  
the underlying network. The experimental results of UAS based on heterogeneous hardware devices demonstrate the feasibility and advantages of using DDS in a multi-UAV deployment scenario.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation
### Clusters Building Time  
Cluster construction time refers to the time necessary to construct the clustering. The suggested method takes into account the various input values. Following these input values, the CH is picked, their cluster members are also produced, and the CH is selected. In this case, the time required between receiving the input and creating the output is called cluster formation or building time. This is also referred to as the algorithm's computational complexity or algorithmic complexity. As unmanned aerial vehicles (UAVs) have limited memory and power, long cluster formation times negatively influence their performance. It will also shorten the lifespan of UAVs since they would consume more energy. The suggested technique is evaluated compared to the current methods, including LEACH, LEACH-C, PSO-NSA, and SEED. The PSO-based approach requires less time throughout the construction phase than leach. This is because LEACH and LEACH-C are based on randomization and repeatedly converge, so they are used. As seen in Figure 6, an increase in the number of nodes will increase the time required for clusters to form. On the other hand, the suggested model employs clustering, which creates just one solution and updates it sequentially to progress towards a globally optimal solution. It is well-known for its lack of temporal complexity. The time required for route discovery is reduced due to the low complexity of the services and provisioning. It also helps to keep nodes' energy usage low.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening
### Current Research
#### 4.2.2 Terminal Epoxy Group Hyperbranched Polymers  
In 1996, [PERSON] et al. [84] first introduced hyperbranched polymers with terminal epoxy groups. The epoxy end groups exhibit good compatibility with epoxy resin systems, and by adjusting the content of these epoxy end groups, the phase separation process and mechanical properties of the modified epoxy resins can be controlled.  
[PERSON] et al. [85] used an epoxy-functionalized hyperbranched polymer (HBP) as an additive in an epoxy-anhydride resin system. Their research demonstrated that, within the additive concentration range of 0 to 20 wt%, HBP significantly increased the fracture toughness of the epoxy resin, with minimal adverse effects on the processing and durability of the cured resin system. However, the addition of HBP resulted in approximately a 30% reduction in both the flexural modulus and stress, and a decrease of about 10% in the glass transition temperature (Tg).  
[PERSON] et al. [86] synthesized a novel terminal epoxy hyperbranched polyether sulfone (EHPES) using a one-step method. They used EHPES to modify bisphenol A diglycidyl ether (DGEBA). Their study found that the copolymer containing 7% EHPES achieved tensile strength, flexural strength, and impact strength of 87.75 MPa, 112 MPa, and 41.70 kJ/m\({}^{2}\), respectively, showing improvements of 9.24%, 10.70%, and 19.38% over unmodified DGEBA.