esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Nanodelivery Platform for Vaccine
### Bio-Nanocarriers
#### 4.1.1 Exosomes  
Exosomes are extracellular vesicles ranging from 30 to 100 nm in diameter, which were first identified in sheep reticulocytes in the 1980s. Major exosome components include cell-derived lipids, proteins, glycoconjugates, RNA, and DNA [99]. With increasing studies on the function and mechanisms of exosomes, it was found that in addition to being a way for cells to excrete waste products, exosomes are now also considered as specifically secreted membrane vesicles that can be involved in intercellular communication in various normal and pathological process [100]. Moreover, exosomes have been shown to act as antigen-presenting vesicles and therefore as stimulators of the immune response. Exosomes are natural nanocarriers. They have the advantages of low cytotoxicity, improved targeting, and enhanced immune response [101; 102]. Many types of cells secrete exosomes, including immune and tumor cells. Tumor-derived exosomes (TEXs) exhibit a double-edged sword effect on the immune system, i.e., immunostimulatory or immunosuppressive effects. On the one hand, TEXs can promote cancer cell growth and invasion, expression of immunosuppressive molecules like PD-L1 and TGF-\(\beta\), inhibition of CD8\({}^{+}\) T cell activation and proliferation, etc. TEXs, on the other hand, express multiple MHC I molecules and markers of the tumor, like heat shock proteins (HSPs) engaged in antigen presentation and T cell stimulation, and are able to induce antitumor responses that are dependent on CD8\({}^{+}\) T cells in vitro and in vivo. Moreover, TEXs act as a carrier of TAAs or TSAs to activate antigen-presenting cells (APC) like DC cells. Exosomes were used as a more potent antigen for DC loading compared to traditional tumor lysate. TEXs can be used as \"mini-APCs\" and as a new type of immunotherapeutic agent. Exomes may also be modified as adjuvants in nanovaccines. Autologous tumor lysate dendritic cell vaccine (ADC) has been shown to stimulate T cells with potential antitumor activity. ADC was able to induce tumor-specific T lymphocytes in a clinical phase III trial, was highly tolerated, and had an overall survival benefit in colorectal cancer patients [103]. In addition, it has been reported that tumor cells treated with pharmacological or ablative therapies, such as drug therapy and radiofrequency ablation (RFA), can induce an immune response within the tumor, promoting tumor antigen release and DC maturation [104].
## 1 Introduction
Additionally, Contrasted to BLAST-TAS [15], this presented method still has significant diversity gain at the same spectral efficiency as well as low signal-to-noise ratio. The global combining for antenna arrays that produce the desired radiation pattern [16; 17; 18] is also a nonlinear optimization issue. A number of mathematical algorithms have already been presented on this solution. Those examples of the analytical technologies including the Chebishev [19] method and Taylor algorithm [20], which are both well-known. Nevertheless, the calculus or analytical ways commonly used cannot optimize the power weights and positions of the array elements at the same time. To treat huge nonlinear searching spaces and enlarge the research to include an efficient placement of elements, stochastic methods are essential. The problem-oriented genetic algorithms (GAs) have been extended to utilize some different experimental instances [21; 22; 23]. As for the symmetrical array synthesis, the real-coded GA-based process was presented in the article [24] to optimize the array power weights while sensors are \(\lambda\)/2-spaced. Moreover, the binary genetic method [25] was utilized to address the problem of isophorical array thinning, and the stochastic approach was proposed [26] to optimize isophorical arrays with a certain number of the elements. The methods above consider symmetric arrays to reduce the calculating time. However, then this arrays optimizing [27; 28] have already been researched via considering one higher number of degrees of freedom just like the elements position as well as weighting. From then on, the optimization algorithm based on a simulation annealing procedure was used in order to optimize both the positions and power weights, and in the article [29; 30] to the beam pattern shaping and the global array combinations [31; 32; 33]. The arrangement of the article is organized as the following parts. After authors offer the specific background of this paper in advance. Firstly, the linear dispersion code is reviewed in Section 2. Secondly, a model of the system over the LDC-TAS is introduced, then some assumptions are also offered inside Section 3, and the full description about the antenna selection methods for LDCs based on ZF receiver is shown in detail in Section 4. Thirdly, in Section 5, this paper focuses on proposing that the modular algorithm, according to the genetic algorithm, can combine the linear real-weighted arrays based on different restrictions, including arrays thinning, the side-lobe peak minimization, the beam pattern (BP) shape modeling, and the minimization of the linear dimension. Then, Section 6 includes the numerical computing simulation results and the complete analysis to those results. Finally, Section 7 presents our conclusions.
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 3 The Proposed Model
### Embedding Layer  
As shown in _Embedding Layer_ in Figure 1, the model generates text-based and sentiment-based embeddings to extract the semantic and sentiment features of the tweet. In addition, we combine the character embeddings produced by XLNet with Cantonese word embeddings to learn the contextual information and internal structures of the words, so as to make the model more suitable for the Cantonese rumor detection.  
XLNet is a generalized Autoregressive (AR) pre-training method that combines the advantages of AR and Autoencoder (AE) methods. The architecture of XLNet is developed to work seamlessly with the AR objective, including integrating Transformer-XL and the design of the two-stream attention mechanism. Experimental results show that XLNet achieves substantial improvement over previous pre-training methods on various tasks [23]. In this study, we use XLNet to create embeddings and pre-train the XLNet-Base [24] which contains 12-layer, 768-hidden, and 12-heads.
## 2 Theoretical Model for Water Temperature Field  
### Vertical Profile Temperature Structure of Water  
The scientific team of the Global Ocean Data Assimilation Experiment (GODAE) high-resolution sea temperature test project uses five temperature forms to define the vertical structure of the sea surface temperature [1], as shown in Figure 1. Among them, the water-air interface temperature \(SST_{\text{int}}\) represents the temperature of the water at the exact position of the water-air interface in an infinitely thin layer; the skin temperature of the water surface \(SST_{skin}\) represents the temperature within a thin layer on the water side of the water-air interface, where there is a strong temperature gradient and diffusive heat transfer dominates; the subsurface skin temperature of the water \(SST_{subskin}\) represents the bottom of the temperature gradient layer, where molecular and viscous heat transfer processes dominate. It can be measured by low-frequency microwave radiometers, generally at depths greater than 1 mm; the subsurface temperature of the water \(SST_{depth}\), also known as the \"bulk\" water temperature, refers to the temperature of a layer of water below a certain depth. It is usually located at a specific water depth, for example, \(SST_{5m}\) represents the water temperature at a depth of 5 m. It is greatly influenced by solar radiation and varies on an hourly time scale, with convective heat transfer processes dominating. It can be measured by traditional temperature sensors installed on buoys, profilers, and ships; the bottom temperature of the water \(SST_{fnd}\) represents the temperature of the water at the maximum depth where solar radiation can reach.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 1 Introduction
### Regulatory Activities on the IMT and DTT in the UHF Band
It became evident that operating two such distinct services in the same frequency range--terrestrial television (DTT) and IMT mobile systems--is not feasible without several hundred kilometers of geographical separation or special new techniques used. This separation is mainly necessary to protect the receiving side of the base transceiver station (BTS) (uplink) from interference caused by terrestrial TV stations with very high effective radiated power (ERP). However, some countries plan to implement 5G/6G systems in the lower UHF frequency range (470-694 MHz) due to the favorable IMT coverage and capacity in rural areas, while others prioritize the maintenance and development of terrestrial television in the UHF band. WRC23 decided to allow secondary usage of the UHF spectrum for IMT services in the 470-694 MHz range.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 4 Simulation Results
### Performance Comparison with the Conventional Estimators
This is because the FDNN model has the simplest structure; thus, it could not study the structure of the channel as well as the others. The CNN model, on the other hand, not only could learn more deeply than the FDNN model but also provided robustness in denoising noisy data. Therefore, we can see that the CNN model yields better performance compared to the FDNN model. However, both FDNN and CNN models could not exploit the relation between channels in the same way as the bi-LSTM model. Therefore, we can see a great improvement in terms ofMSE performance due to the bi-LSTM model. To further clarify this, the MSE gaps (dB) between the deep learning-based channel estimation methods and the LMMSE estimation are shown in Figures 11 and 12. From the two figures, it can be seen that the gaps decrease as the SNR level increases. Thus, the deep learning-assisted methods work much better in the low SNR region. Comparing between the two scenarios, due to the change of the pilot density, the performance differences between two scenarios are not significant. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Optimizer & Adam \\ Maximum number of epoches & 100 \\ Mini-bath size & 32 \\ Training error & \(10^{-5}\) \\ Gradient descent accuracy & \(10^{-7}\) \\ Learning rate & 0.001 \\ Maximum validation failures & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters for training deep learning models. We also provide the BER performance of the considered scenarios in Figures 13 and 14 with the different channel estimation methods, respectively. The trend of the BER performance for the examined estimators is similar to that of MSE performance. However, in both scenarios, the BER performance of the FDNN model is slightly worse than the LMMSE  
Figure 11: The MSE gap (dB) between the deep learning-based channel estimation methods and the LMMSE estimation with \(f_{D}=36\) Hz. Figure 12: The MSE gap (dB) between the deep learning-based channel estimation methods and the LMMSE estimation with \(f_{D}=200\) Hz. Figure 9: The MSE of the channel estimate vs. the SNR level with \(f_{D}=36\) Hz. method at \(SNR=20\) dB. This can be explained by the fact that the loss function has been defined to minimize the channel estimation errors instead of the BER metric.
# Effect of Compression Tights on Skin Temperature in Women with Lipedema
## 4 Discussion
### Skin Temperature at Rest  
Lipedema is underdiagnosed due to the lack of knowledge about its physiopathology and the existing similarities among pathologies such asymphedema and obesity [1; 30]. However, there are some differences; for example, lipedema is a symmetrical increase in adipose tissue, whereasymphedema is not symmetrical and does not raise the per  
Figure 6: Maximum skin temperature Variation (Mean \(\pm\) SD) for each region of interest in all groups.  
centage of adipose tissue [1; 30]. From the author's knowledge, although any previous study assessed skin temperature in people with lipedema, lymphaden has been assessed previously [21; 22]. [PERSON] et al. [22] measured resting skin temperature, and they obtained the greatest values of skin temperature in women with advanced lymphedema. Furthermore, the skin temperature of the regions with nodules and fibrosis was lower than regions without it, and this fact probably was produced by a blood flow impairment in the region. On the contrary, [PERSON] et al. [21] measured lower values of skin temperature in people with lymphedema, especially in those with advanced lymphedema. The controversy in the results of these studies shows the necessity of performing new studies to provide more evidence.  
In this sense, in our study composed of women with lipedema, the participants of the control group did not employ compression tights before their inclusion in the study and during the study in comparison with the groups that wore compression tights along the study, which also employed compression garments before the study. However, no differences between groups were observed in rest skin temperature in the first and the second test. These results are in agreement with the results of previous studies that compared in a healthy sample the skin temperature between groups with compression garments and generic garments without compression [31; 32]. In our study, the only difference observed was in the flat knitting group in the popliteal fossa ROI (\(p=0.042\)), where the skin temperature in the second test was lower than the first test. This effect probably was a random effect because it happened only in one ROI and in one group. However, this fact could be produced by the difference in the values of compression between their compression tights and the prototypes of the study. In the development of the prototype, it was paid attention by AITEX to comfort and perspiration, and the values of compression obtained were lower than the own tights of participants that used therapeutical compression tights (Level CCL2). According to the data provided by the Technological Institute of Textile (AITEX) (Figure 3), circular knitting compression tights have the greatest declining compression in the lower leg in comparison with flat knitting compression tights (circular b1: 62%, c 49%; d 46% vs. flat b1: 93%; c: 95% d: 76%). This idea must be analyzed in other studies to identify the effect of the compression level and the percentage of compression in skin temperature.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 5 Comparison with Ray-Tracing Simulations
### Radio Channel Characteristics
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Position** & **Simulated (ns)** & **Measured (ns)** & **Acc. (\%)** \\ \hline
1 & 2.99 & 4.51 & 66.27 \\
2 & 3.41 & 3.86 & 88.28 \\
3 & 3.53 & 4.39 & 80.43 \\
4 & 3.30 & 5.85 & 56.45 \\
5 & 3.53 & 5.02 & 70.42 \\
6 & 3.35 & 4.28 & 78.12 \\
7 & 3.11 & 3.25 & 95.66 \\
8 & 3.57 & 4.04 & 88.33 \\
9 & 2.83 & 4.53 & 62.63 \\
10 & 2.21 & 3.58 & 61.64 \\
11 & 1.73 & 2.36 & 73.22 \\
12 & 0.99 & 1.74 & 56.61 \\
13 & 1.58 & 3.09 & 51.15 \\
14 & 1.78 & 1.78 & 100.0 \\ - & - & Mean & 73.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between the measured and simulated RMS of the DS between 1 GHz and 40 GHz. The delay parameters and PL were also estimated using the simulated PDPs for the WRC'15/19 bands as presented in tables included in the Appendix A.
## 4 Discussion
### Water Management
#### 4.2.3 Tail-End Lakes and Fisheries  
The fate of tail-end lakes in the inland river basins is directly controlled by the upstream inflow and water resource management across the entire basin. For example, the shrinking of the Aral Sea was caused largely by the diversion of water from the Amu Darya and Syr Darya rivers for land irrigation [58, 59]. The water level of Ulungur Lake, the tail-end lake of the Ulungur River, has also followed a continuous shrinking trend because of the increase in local irrigation (Figure 7b), and the lake level began to recover since 2009. Ulungur Lake, the largest lake and fishery production center in northern Xinjiang, will be placed into a more precarious position in the future by the increased extraction from the Ulungur River and evaporation under warmer meteorological conditions.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Copper Nanowires
#### 2.4.3 Patterned Metal Grid  
Using a patterned metal grid is one way to make an optically transparent antenna. This structure features optical transmittance because light is transmitted between the metal grid structures. However, the current still flows because the grids are connected [36]. With metal grids, an advantage is increased optical transmittance compared to continuous metal sheets due to the fact that the area between the grid lines is 100% transparent. However, this increase in transmittance comes at the expense of sheet resistance reduction. If the thickness of the metal grid is greater, the light transmittance is reduced, and therefore a thinner thickness than skin depth is generally required. Thinner than skin depth, however, leads to an increase in sheet resistance, which leads to a decrease in the electrical conductivity and radiation efficiency of the antenna. However, making the line width thinner than the wavelength of light has the additional advantage of minimizing scattering from the film [24].
## 4 Numerical Experiment
### Numerical Analysis Method
#### 4.1.5 Analysis Cases  
Table 2 shows the analysis cases. For the test case, the support condition and the exciting method were set as analysis parameters. In addition to the case in which the ladder sleepers are uniformly supported by the ballast, two cases, Support A and Support B, are defined assuming that the ladder sleepers are laid at the structural boundary described in Chapter 1. In this study, the structural boundaries were assumed to be the boundary between the embankment, abutment, and the girder and the open culvert that crosses the track. Support A, as shown in Figure 12, is a case in which ladder sleepers are laid in front of the structural boundary, whereas Support B is a case in which a ladder sleeper is laid across the structural boundary. The exciting method of the analysis model was static load  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Rail**} & “50 kgN” specified by JIS E 1101 [25] \\  & [PERSON]’s modulus \(E_{S}\) = 200 GPa, Track gauge = 1067 mm \\ \hline
**Rail Pad** & Spring constant \(D_{P}\) = 120 MN/m (nominal value = 60 MN/m) \\ \hline \multirow{4}{*}{**Ladder Sleeper**} & PC steel strand \(\varphi\) = 4.22 mm N = 18 \\  & Length \(L_{L}\) = 5900 mm, Width \(B_{L}\) = 460 mm \\  & Height \(H_{L}\) = 155 mm \\  & Young’s modulus \(E_{C}\) = 33 GPa \\ \hline \multirow{4}{*}{**PC Sleeper**} & 3 PR (JIS E 1201), PC steel strand \(\varphi\) = 2.9 mm N = 12 \\  & Length \(L_{P}\) = 2000 mm, Bottom width \(B_{P}\) = 240 mm \\ \cline{1-1}  & Height \(H_{P}\) = 170 mm (Rail seat section), 150 mm (Center section) \\ \cline{1-1}  & Young’s modulus \(E_{C}\) = 33 GPa \\ \hline \multirow{2}{*}{**Ballast**} & Ballast thickness \(h\) = 250 mm \\  & Young’s modulus \(E_{B}\) = 0.088 GPa \\ \hline \multirow{2}{*}{**Roadbed**} & Ground reaction coefficient \(K_{30}\) = 50 MN/m\({}^{3}\) (Measured value) \\  & Support spring constant \(D_{G}\) = 22 MN/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material constants used in the numerical analysis.  
and train running. The train's running directions were \"step-up\", in which the vehicle was transferred from the ballast section to the rigid support section, and \"step-down\", in which the vehicle was transferred from the rigid support section to the ballast section. The rigid support section was modeled by multiplying Young's modulus of the ballast and the ground reaction force coefficient of the roadbed shown in Table 1 by 1000.  
The stiffness ratio between the ballast section and the rigid support section is an important parameter. However, since the main purpose of this study is to clarify the difference in the pressure on the sleeper bottom plane caused by the structural difference between the ladder sleeper and the PC sleeper, the stiffness ratio is not a parameter and is simply one case. The assumed structure boundary is the one between the ballast section and a concrete structure section such as the concrete abutment with sufficiently high rigidity compared to ballast. Verification of the rigidity ratio on the boundary will be an issue for the future.
# Correlation Analysis and Its Application on an Asymmetry
## 3 Numerical Analysis of the System
### The Correlation Analysis of the Rotor with Overhang  
In this subsection, correlation analysis is conducted as, in design process or control process of a rotor, especially when during balancing process of a rotor structure, not all positions can be measured or adjusted by trial measures. The correlation analysis can help find which position affects most. In this model, the balancing groves are located at node #3 and node #5. The position at node #4 has screw holes. The position at the end of the overhang, which is numbered as node #8, can also be a position to add trial weight. Two velocity sensors are mounted on the supports (#1 and #7). To see the overhang effect, additional eddy current sensor could be applied to measure the displacement of the node #8. The responses of the nodes #1, #7 and #8 in vertical and horizontal directions of the simulation results with pre-set imbalance at nodes of #3, #4, #5 and #8 are given from Figures 3-6 separately.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Node 1–2** & **Node 2–3** & **Node 3–4** & **Node 4–5** & **Node 5–6** & **Node 6–7** & **Node 7–8** \\ \hline Length & 435 mm & 235 mm & 1117.5 mm & 1117.5 mm & 185 mm & 420 mm & 555 mm \\ Diameter & 235 mm & 369 mm & 588 mm & 588 mm & 386 mm & 252 mm & 126 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The shape value of the shaft.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Young’s module \(E\) & \(100\times 10^{9}\) Pa \\ Density \(\rho\) & \(7.85\times 10^{3}\) kg/m\({}^{3}\) \\ Poisson’s ratio \(\mu\) & \(0.211\) \\ Axial force \(H\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The material parameter.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Order of Stiffness** & **First-Order** & **Second-Order** & **Third-Order** \\ \hline \(10^{7}\) & 13.5 Hz & 28.5 Hz & 349.3 Hz \\ \(10^{8}\) & 39.6 Hz & 86.3 Hz & 360.9 Hz \\ \(10^{9}\) & 80.6 Hz & 201.8 Hz & 416.4 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: The first three orders critical speed.  
Figure 4: Responses of the measurable nodes in (**a**) vertical direction and (**b**) horizontal direction with pre-set imbalance at node 4.  
Figure 3: Responses of the measurable nodes in (**a**) vertical direction and (**b**) horizontal direction with pre-set imbalance at node 3.  
Figure 5: Responses of the measurable nodes in (**a**) vertical direction and (**b**) horizontal direction with pre-set imbalance at node 5.  
Figure 6: Responses of the measurable nodes in (**a**) vertical direction and (**b**) horizontal direction with pre-set imbalance at node 8.