esa-sceva/satcom-chunk-collection huggingface dataset:
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
## 5 The Fully Shielded Helical Resonator  
While the procedure described above constitutes the general approach, from now on, we want to focus on the condition \(c/b=1\), that is, with the shield directly superimposed to the helical coil. We shall call this case \"fully shielded resonator\". This is the most interesting situation for plasma production, because in practical situations, especially for devices that are intended to be taken out of the laboratory, one wishes to minimize electromagnetic radiation, both for safety and electromagnetic compatibility reasons. Since a helical antenna, when operating at wavelengths much larger than its size, radiates mainly from the sides [26], the grounded shield should prevent this irradiation (hence its name). Furthermore, since radiation is one major source of dissipation, it is expected that the shielded resonator will have a lower \(\alpha\). Thus, we think that a good construction practice is to always wrap the coil in a conducting grounded shield, with close proximity.  
This situation makes also for easier calculations since, as seen above, no numerical solution is needed: \(V_{f}=\sin\psi\) and \(k=\beta\sin\psi\). This allows to recast in terms of the frequency the previously found expressions, which were given in terms of the wave number \(\beta\). Indeed, it is straightforward to show that in this case, the following holds:  
\[\beta H=\frac{2\pi N}{\cos\psi}kb. \tag{50}\]  
The normalized frequency at which the peak in input resistance occurs is as follows:  
\[k_{0}b=\frac{\cos\psi}{4N}\approx\frac{1}{4N}. \tag{51}\]  
Figure 8: Behavior of the helical resonator at the point of purely resistive input impedance near resonance (continuous line) and at resonance (dashed line), plotted as a function of the tap position \(\delta\), for three different values of the normalized attenuation factor \(\alpha H\). **Left**: input resistance at resonance, normalized to the characteristic impedance \(Z_{\varepsilon}\). **Right**: voltage amplification factor \(|V_{out}/V_{in}|\).  
We see that for a given coil diameter, this frequency is inversely proportional to the number of turns. It is also possible to normalize the frequency \(f\) to the frequency of maximum resistance \(f_{0}\), and write the following:  
\[\beta H=\frac{\pi}{2}\frac{f}{f_{0}}. \tag{52}\]  
It is thus straightforward to replot the graphs in Figures 5 and 6 as a function of \(f/f_{0}\). This is not the case for values of \(c/b\) larger than 1, where the velocity factor is frequency dependent, and the transformation from wave number to frequency is nonlinear. The resonance frequency \(f_{r}\) is related to \(f_{0}\) by the following:  
\[f_{0}=f_{r}(1-\delta). \tag{53}\]
## 3 Methodology
### Model Training by Machine Learning Classifiers
#### 3.6.2 Decision Tree (DT)  
A decision tree works on a rule-based approach and is a supervised ML algorithm. They make decisions, such as human beings based on rules fed to algorithms. They can perform classification as well as regression. A decision tree is also known as a classification and regression tree (CART).  
Equation (3) for DT represents the Gini impurity, which is a matrix used in the generation of DT to identify how the features of a data set should be partitioned into nodes to form the tree. Equation (3) can be defined as a data set D that contains samples from c classes. The probability of samples belonging to class i at a given node can be denoted as P1. Then the Gini Impurity of data set D can be defined as:  
\[\text{Gini}(\text{D})=1-\sum_{\text{i=1}}^{\text{c}}{(\text{P}_{\text{i}})^{2}} \tag{3}\]  
where the Gini impurity is used for the classification of impure data; it is calculated by subtracting the total of the squared probability of each class out of one. It promotes larger partitions that are simple to implement, whereas information gain prefers smaller partitions with different values [30].  
The addition of a new feature adds a new class to the tree. Every new query adds a new node to the tree. The first node is generally called the root node of the tree and the last node after which no further split happened in the tree is known as the leaf node of the tree. The decision tree used for  
* Visualization of the tree;
* Pre-processing is not required;
* All types of data are handled by the tree nicely.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 7 Conclusions  
In this study, we considered an IRS-based wireless communication system and conducted a performance analysis using RNN approaches. We examined both the IRS-reflected signal and the direct signal for channel estimation. To apply machine learning, we specifically focused on RNN techniques, namely LSTM, BiLSTM, and GRU, for estimating the IRS channel. First, each individual RNN technique was studied with single and double layers. In this setup, the BiLSTM with double layers showed the best BER performance compared to LSTM and GRU. In the next step, a hybrid approach was considered by combining each of the RNN techniques. The experimental results demonstrated that individual RNN techniques are capable of learning data patterns from the training process. Among the RNN techniques, the BiLSTM double-layer model achieved the highest accuracy compared to the LSTM and GRU models. In the hybrid approach, the BiLSTM-GRU combination provided the best performance in terms of BER and SER. We also compared our results with previous algorithms, and the outcomes are comparable to other studies. When the CSI and channel parameters are varied, the model must be adjusted to achieve optimal results. Retraining the model with newly generated data is necessary. Additionally, the model architecture may need to be restructured to reduce the BER. This study can be extended to develop larger models that account for multiple IRS reflection scenarios.  
Conceptualization, R.T. and M.A.S.S.; methodology, R.T., M.A.S.S.,and M.A.A.; software, R.T., M.H.R. and M.A.A.; validation, R.T. and M.H.R.; formal analysis, R.T., M.A.S.S., M.H.R. and M.A.A.S.S.; investigation, R.T., M.A.S.S. and M.A.A.S.; resources, H.-K.S.; data curation, R.T. and M.A.S.S.; writing--original draft preparation, R.T.; writing--review and editing, R.T., M.A.S.S., M.H.R., M.A.A. and H.-K.S.; visualization, R.T., M.A.S.S., M.H.R. and M.A.A.S; supervision, H.-K.S.; project administration, H.-K.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government [Ministry of Science and ICT (MSIT)], South Korea, under the Metaverse Support Program to Nurture the Best Talents (grant no. IITP-2024-RS-2023-00254529). It was also supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (grant no. 2020R1A6A1A03038540); and in part by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ITRC (Information Technology Research Center) grant, funded by the Korean government (Ministry of Science and ICT), under IITP-2024-RS-2024-00437191.  
The data will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Tantalum Alloy Welding: Does the Thermal Cycle Influence the Microstructure?
## 4 Discussion  
### Weld Process Temperature Distribution  
Figure 7 shows the temperature distribution of the welding, as a transient non-linear process, in which thermal fields, distributed throughout the pipe, are obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Zone** & **Hardness** \\ \hline Base Metal (FL + 50 mm) & 120 (120–121) HV 0.05 \\ \hline Heat Affected Zone (FL + 2 mm) & 238 (237–238) HV 0.05 \\ \hline Welded Metal & 425 (425) HV 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hardness results and scheme.  
The obtained results correspond to the material deposition process, which establish the temperature evolution during the process, as well as the estimation of the risk of hot cracking of the tantalum alloy under the welding thermal cycle, due to the stresses generated.  
It is observed that the temperature fields are temperature-dependent throughout the simulation process. The isotherms area is kept perpendicular to the axis of the seam, this is justified due to the transfers occurred by convection and radiation to the external environment.  
The results show that the temperature is distributed over the entire diameter of the piece, from the maximum values (red color) to the minimum values (blue color). The material exhibits an elastoplastic behavior and its properties are temperature-dependent, with the dimension of the zone of thermal influence being approximately 40 mm. This evolution of the temperature can lead to the remelting of the material and, in any case, a continuous change in the microstructure is induced during the entire process.  
Visual inspection, non-destructive surface examinations (liquid penetrant test), flexural test, and micro-structural behavior confirmed its properties; no pores and a lack of fusion or cracks were evident in any of the analyzed areas, demonstrating the quality of the welding process performed.
# Potential of Laurel (_Laurus mobilis_ L.) Leaf Polyphenols for Modulation of Body Composition
## 4 Influence of Laurel Leaf Polypphenols on Body Composition
### Influence of Laurel Leaf Polypphenols on Skeletal Muscle
#### 4.2.3 Apigenin  
The only effect of apigenin that was investigated in the published studies included in this review was the suppression of muscle atrophy through different mechanisms. It was shown that apigenin can ameliorate obesity- and sciatic nerve denervation-induced skeletal muscle atrophy by reducing the expression of atrophic genes (MuRF1 and Atrogin-1), increasing mitochondrial function in obese mice and upregulating myosin heavy chain in sciatic nerve-denervated mice [77; 78]. Apigenin also repressed age-related skeletal muscle atrophy in mice by activating antioxidant and mitochondrial respiratory enzymes, enhancing the expression related to mitochondrial biogenesis and inhibiting Bcl-2/adenovirus E1B 19 kD-interacting protein 3 and DNA fragmentation characteristic of mitophagy and apoptosis [80]. Apigenin also inhibited lipopolysaccharide-mediated expression of atrophic genes by inhibiting the JNK-2 molecular pathway in murine C2C12 myotubes, resulting in suppressed muscle atrophy [76]. In addition, apigenin enhanced skeletal muscle hypertrophy and myoblast differentiation in mice through the regulation of the Prmt7-PGC-1\(\alpha\)-GPR56 and Prmt7-p38-myoD molecular pathways, indicating the possibility of prevention of other forms of skeletal muscle weakness and atrophy, such as sarcopenia and dynapenia [79].  
Overall, like the effects on bones, the laurel leaves components' biological effects on muscle need more investigation, as all the studies were performed on animal models or in vitro. However, these are all encouraging leads and beginnings for human studies.
## 2 Materials and Methods
### PV Output Interval Prediction  
#### 2.2.1 LSTM Model  
The long- and short-term memory neural network (LSTM) is an improved version of the traditional recurrent neural network (RNN), and its network structure is shown in Figure 1.  
LSTM realizes the protection and control of information through forgetting gates, input gates, and output gates. The specific input-output relationship is shown in Equation (1).  
\[\left\{\begin{aligned} &\mathbf{f}_{t}=\sigma\Big{(}\mathbf{W}_{f}\mathbf{x}_{t}+ \mathbf{U}_{f}\mathbf{h}_{t-1}+\mathbf{b}_{f}\Big{)}\\ &\mathbf{i}_{t}=\sigma(\mathbf{W}_{t}\mathbf{x}_{t}+\mathbf{U}_{t}\mathbf{h}_{t-1}+ \mathbf{b}_{i})\\ &\mathbf{o}_{t}=\sigma(\mathbf{W}_{o}\mathbf{x}_{t}+\mathbf{U}_{o}\mathbf{h}_{t-1}+ \mathbf{b}_{o})\\ &\widetilde{\mathbf{c}}_{t}{=}\tanh(\mathbf{W}_{c}\mathbf{x}_{t}+\mathbf{U}_{t} \mathbf{h}_{t-1}+\mathbf{b}_{c})\\ &\mathbf{c}_{t}=\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot\widetilde {\mathbf{c}}_{t}\\ &\mathbf{h}_{t}=\mathbf{o}_{t}\odot\tanh(\mathbf{c}_{t})\end{aligned}\right. \tag{1}\]  
where \(\mathbf{f}_{t}\) is forgetting gates; \(\mathbf{i}_{t}\) is input gates; \(\mathbf{o}_{t}\) is output gates; \(\sigma(\cdot)\) represents the sigmoid activation function; \(\mathbf{x}_{t}\) is the current input vector; \(\mathbf{W}_{f}\), \(\mathbf{W}_{i}\), \(\mathbf{W}_{o}\), \(\mathbf{W}_{c}\), \(\mathbf{U}_{f}\), \(\mathbf{U}_{i}\), \(\mathbf{U}_{o}\), \(\mathbf{U}_{c}\) are weight matrices; \(\mathbf{b}_{f}\), \(\mathbf{b}_{i}\), \(\mathbf{b}_{o}\), \(\mathbf{b}_{c}\) are bias matrices; \(\mathbf{h}_{t}\) is the current hidden layer vector; \(\mathbf{h}_{t-1}\) is the hidden layer vector at last moment; \(\widetilde{\mathbf{c}}_{t}\) is the candidate value; \(\mathbf{c}_{t}\) is the updated status; \(\odot\) represents dot product; and \(\tanh\) is the hyperbolic tangent activation function.
## 2 Characteristics of Time Delay Complex Chen System
### Chaos Attractor  
We adopted \(a_{1}=35,\ a_{2}=23,\ a_{3}=1\), and \((1,2,3,4,1)^{\mathrm{T}}\) as the initial conditions. Their different chaotic attractor projections are shown as blue attractors in Figure 1 (\(\tau=1\) s) and Figure 2 (\(\tau=1.5\) s), where the red attractor is a complex Chen system with \(\tau=0\) s. The projection of the time delay complex Chen system is quite different than the original system. The attractor of the former occupies much more space than the latter. Time delay systems produce time-series data with extremely high randomness and unpredictability. The time delay complex Chen system with \(\tau=1\) s was chaotic, while the Chen system with \(\tau=1.5\) s exhibited a limit cycle. This indicates that, under the same parameters and initial values, different time delay factors cause hugely different outputs.  
Figure 1: Chaotic attractor projections for time delay complex Chen system (\(a_{1}=35,\ a_{2}=23,\ a_{3}=1\)). Blue attractor, complex Chen system with \(\tau=1\) s; red attractor, complex Chen system with \(\tau=0\) s). (**a**) \(u_{2}-u_{5}\), (**b**) \(u_{1}-u_{4}\), (**c**) \(u_{1}-u_{3}-u_{5}\), and (**d**) \(u_{3}-u_{4}-u_{5}\).
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### User Experience
#### 8.5.3 Accessibility and Inclusivity in the Metaverse  
_Accessibility_ in the metaverse is another crucial aspect of UX that ensures inclusivity for users with diverse abilities and needs. Ensuring that the metaverse is accessible to all users, including those with disabilities, is a crucial aspect of UX design [294]. The immersive nature of the metaverse provides new opportunities for inclusivity, but it also introduces challenges in accommodating users with physical, sensory, and cognitive impairments. Accessibility should not be an afterthought but a core element of design, ensuring that everyone can participate fully in the metaverse. For users with mobility impairments, developers must provide multiple input methods, such as adaptive controllers, gesture-based inputs, and voice commands [295]. These options ensure that users who may not be able to use traditional controllers or keyboards can still navigate and interact with virtual environments.  
Moreover, environments should be designed to support different movement speeds, seated play, and adjustable reach heights to accommodate users with limited physical mobility. For individuals with visual impairments, text-to-speech (TTS) features can enable the conversion of on-screen text into spoken words, while haptic feedback can provide tactile cues to simulate touch and interaction [296]. Audio descriptions of visual elements, such as describing the environment, actions, or characters, can further enhance the experience for users with limited sight. On the other hand, for users with hearing impairments, integrating speech-to-text (STT) features allows spoken dialogue to be transcribed in real-time, ensuring that users can engage with conversations and activities in the metaverse [297]. Additionally, ensuring compatibility with hearing aids or cochlear implants could be an important consideration for creating more inclusive soundscapes. Cognitive accessibility is another critical area. For users with cognitive disabilities, such as dyslexia, autism spectrum disorders, or attention deficits, virtual environments must be designed to minimize confusion and overload [298]. Features such as clear navigation cues, adjustable text sizes, simple language options, and color contrast settings can make the metaverse easier to understand and navigate for these users. Additionally, customizable UI layouts that allow users to control the complexity and frequency of information displayed could make virtual environments more accommodating to a wider range of cognitive abilities.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 5 Conclusions  
SWIM is an infrastructure for information sharing and transmission of networked next-generation air traffic management promoted by ICAO. Its main function is to quickly and seamlessly communicate a wide variety of air traffic control business data. Therefore, the naming of SWIM data involves whether a large number of data can be accessed and used correctly, which is related to the normal operation of air traffic control services and ensuring flight safety. Based on the analysis of the various advantages and disadvantages of SWIM naming schemes, given their existing problems, this paper adopts the TLC structure and proposes a hybrid SWIM data naming scheme based on the TLC structure. It uses Bloom aggregation technology to provide routing select. Compared with the traditional prefix-based route aggregation, our proposed naming scheme can greatly reduce the resolution rate of routing errors, and at the same time improve the performance of the content name search rate, and meet the uniqueness, durability and reliability requirements.  
Aiming at the problems in SWIM (System-Wide Information Management), such as the variety of data and the inconsistency of data naming methods, etc, [PERSON] proposes a new TLC (type length content) structure hybrid data naming scheme, and puts forward the algorithm and principle of the scheme. According to the proposed algorithm and principle, [PERSON] did experimental simulation to verify the results, and the simulation experiment proved that this scheme is better than prefix-based aggregation in terms of route recognition error probability. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Scientific Research Project of Tianjin Municipal Education Commission (2019 KJ117), and the Fundamental Research Funds for the Central Universities of China (3122020076 and 3122019051).  
The authors declare that there is no conflict of interests regarding the publication of this article.
## 3 Results
### Transceiver Concept
Therefore, the relative relationship between them should be determined during the calibration process, which is performed before sending the transceiver towards the Moon. The calibration of the mutual spatial relationship between the transmitting and receiving antennas is not included in further calculations. For the rest of this research, while not jeopardizing the reliability of the conclusions, it will be assumed that both antennas share the same phase center. The multipath effect is a part of the positioning error budget. It depends on the terrain configuration and physical properties of the environment. The analysis of detailed Shackleton topography and the physical characteristics of regolith is beyond the scope of this paper. It can be assumed that multipath will not be a critical factor for accuracy degradation, since the average Bond albedo of the Moon is 0.11, but further research on this issue will give more precise conclusions. The application of Lunar surface reflectance models [60] or [61] can be used for preparing detailed multipath models of the Shackleton Crater. All measurements from all transceiver nodes are sent in real-time to a master station, where processing is performed. Final coordinates are sent back to each transceiver. The master station is the transceiver at the location visible from all other nodes. When the system is calibrated and fully operational, it operates as a GNSS space segment, sending navigation messages to LRNTS receivers. Then, the requirements for user LRNTS receivers can be similar to classical GNSS receivers, including the usage of crystal clocks.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture
### STO and CFO Synchronization Unit  
The purpose of STO synchronization is to find the right starting point for FFT by removing the GI. For DVB-C2, STO synchronization can be performed by using the correlation characteristics of the GI and OFDM symbol. For DVB-T2, it can also be performed by using the P1 symbol or the GI. The purpose of CFO synchronization is to compensate CFO caused by the Doppler shifts and a frequency mismatch in the local oscillators of the transmitter and the receiver. CFO can be estimated using the output signal of the P1 symbol detector or the GI detector and the CFO synchronization block compensates the CFO by using a numerically controlled oscillator (NCO) and a complex multiplier.  
Figure 10a shows the block diagram of the GI detector. It detects the GI using the correlation between an input signal and a delayed signal by a FIFO memory. Figure 10b shows the proposed GI average unit using an accumulator to reduce H/W complexity. DVB-T2 uses various GI samples in combination with seven GI fractions and six FFT sizes as shown in Table 2. The proposed GI average unit requires 4864 delay units of input complex signals, 35 complex adders and 12 delay units of accumulated complex values to calculate the average of various GIs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**FFT Size**} & \multicolumn{5}{c}{**GI Fraction**} \\  & **1/128** & **1/32** & **1/16** & **19/256** & **1/8** & **19/128** & **1/4** \\ \hline
32K & 256 & 1024 & 2048 & 243 & 4096 & 4864 & N/A \\
16K & 128 & 512 & 1024 & 1216 & 2048 & 2432 & 4096 \\
8K & 64 & 256 & 512 & 608 & 1024 & 1216 & 2048 \\
4K & NA & 128 & 256 & NA & 512 & NA & 1024 \\
2K & NA & 64 & 128 & NA & 256 & NA & 512 \\
1K & NA & NA & 64 & NA & 128 & NA & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DVB-T2, DVB-C2, and DVB-S2 features.  
Figure 10: Block diagram of (**a**) the GI detector and (**b**) the proposed GI average unit.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 3 GARSU: Automatic RSU Deployment
### Representation of Individuals  
Every potential solution to the problem requires being codified into a genotype before applying the genetic operators. Potential solutions of the RSU deployment problem would include the location of all of the available RSUs, which becomes the phenotype. An appropriate genotype would have a clear correspondence with a specific phenotype. Since genes must be selected from a finite alphabet, the number of possible locations for the RSUs is limited, and they can be configured. GARSUD assigns a unique number to every possible location for the RSUs, and the genotype of each solution contains a sequence of numbers representing the location selected for each RSU, as depicted in Figure 1. A reform strategy is applied to avoid assigning more than one RSU to each possible location, improving the performance of the algorithm.