esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification  
A comparative experiment was designed to test the effectiveness of the GRW-DBA data augmentation method proposed in this paper combined with the ANN model for prediction. According to the augmentation multiple and model hyperparameters determined in Section 3, in the comparative experiment, the augmentation multiple of the source data was set to 500 times, the number of iteration was set to 800 times, there were 3 hidden layers, and the learning rate of the model was 0.0001. The evaluation indexes of prediction accuracy adopted RMSE, MAE, and R\({}^{2}\).
# Intelligent Reflective Surfaces for Wireless Networks:
The experiments in [9] show that, by deploying an IRS in a building, we can obtain a significant impact on the interference attenuation and the signal strength at the receiver side. The IRSs have also received attention in the industry field. In November 2018, the mobile operator NTT DoCoMo and a startup MetaWave experimented with the use of IRS technology to assist wireless communications in the frequency band of 28 GHz [10]. The idea of controlling the environment in millimeter-wave (mmWave) and Terahertz (THz) setups has been introduced in [1; 8; 11] by employing software-controlled metamaterials. Although this idea of passive reflecting surfaces has been employed for a number of applications in radar systems, remote sensing, and satellite communications, they were not considered for mobile communication because the traditional reflective surfaces with fixed phase shifters were unable to cope with the dynamic wireless channels due to user mobility. Nowadays, the advances in meta-materials (e.g., meta-surfaces) represent a certain guarantee for the reconfigurability of reflecting surfaces by enabling the real-time adjustment of the surface phase shifts [12; 13]. A number of papers providing an overview of IRS technologies can be found in the literature. In [4], the authors present a survey of the use of IRSs for smart environments. Therein, the main applications, the advantages over existing technologies, the hardware architecture and the signal model for IRS-aided systems are considered. In [14] a short review is provided with the aim of covering IRS implementations, future research directions for IRSs and its role in 6G communications as well as a categorization of some research studies related to this technology. In [15], the authors place special emphasis on the potential needs and applications of machine learning (ML) to IRS challenges. In contrast, the authors in [16] mainly focus on the key issues of the design and implementation of IRS-assisted communication systems. Compared to these references, our survey offers a comprehensive and contemporary review of the works related to IRSs by providing details of the metrics employed for the system design and main results achieved. Moreover, recent papers not covered by former overviews [4; 14; 15; 16] are studied and categorized in the present survey.
## 3 Materials and Methods
### Hybrid Transformer-CNN Segmentation Networks  
CNN segmentation networks excel at extracting local features but struggle to capture global representations, limiting their performance in complex segmentation tasks. Compared to CNNs, transformers utilize embedding and self-attention mechanisms, which are very efficient in gathering global information and dealing with long-distance dependencies. Recent studies have shown that a hybrid dual-encoder architecture can significantly improve feature learning for precise image segmentation [15; 44]. This study introduces HTCSNet, a novel deep learning segmentation network that integrates CNN and transformer encoders for polyp segmentation. HTCSNet not only excels in local feature extraction but also effectively captures global semantic information.  
As illustrated in Figure 2, HTCSNet consists of three main components: a CNN-based encoder branch, a transformer-based encoder branch, and an ALL-MLP decoder. By integrating the CNN and transformer in the encoder, the data requirements for the transformer network are significantly reduced. During the decoding stage, feature fusion with MLP and skip connections leverages the strengths of both branches, enhancing learning capabilities. The following sections provide a detailed explanation of these three components.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 2 Hydrogen Tanks Classification
### Composite Tanks of Type III  
Type III tanks have an internal metallic shell covered by a thick composite material layer. The internal part (liner) can be aluminum or steel. Its primary function is to make the system impervious to hydrogen leakage and provide structural support, sustaining at least 5% of the internal pressure load. The liner thickness varies depending on the material type, operating pressure, and desired amount of composite material. A higher internal pressure and alloy strength result in a thicker required liner. On the other hand, a thicker composite coating reduces the load percentage on the liner and, consequently, the necessary thickness [58; 59]. The threaded valve body connection, linking the storage system to the rest of the equipment, is directly integrated into the top of the liner.  
Regarding modern fuel cell vehicle installations, the external structural coating is predominantly made of Carbon Fiber-Reinforced Polymer (CFRP) composite, known for its high stiffness and extraordinary mechanical strength [22]. Alternatively, fiberglass or aramid fibers (Kevlar) could be used, but they would result in an excessively thick structure and; therefore, an overweight tank [60]. This is due to the nature of the materials, generally possessing a lower elastic modulus and, particularly, lower fracture resistance. The carbon fibers are high-strength, with T700 being the most common, offering a good compromise between mechanical strength performance and cost. The typical resin used is epoxy, which is well-suited for the employed production processes and possesses good mechanical properties. Fiber application (roving) usually occurs via the automated filament winding process. After resin impregnation, the fibers are applied to the liner surface with precise orientations until the desired thickness is achieved. A suitable mix of circular wraps, capable of withstanding circumferential stresses, low helix angle wraps (\(\alpha\) of about 10\({}^{\circ}\)) for axial stress support, and high helix angle wraps (\(\alpha\) of about 50\({}^{\circ}\)), providing contributions in both directions, is typically utilized [61].  
The use of the filament-wound-thin-walled process is the most mature technology for composite pressure vessels to store hydrogen. Still, the critical challenge is accurately examining the mechanical behavior under high pressure and creating advanced models to predict the composite damage [37]. Regarding the composite-hydrogen interaction and behavior, several studies have been conducted, in particular adopting Hydrogen Enhanced Decohesion models (HEDE) [62] and Hydrogen Enhanced Localized Plasticity models (HELP). These two combined models can enhance the development of better and safer composite hydrogen tanks [20].
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 5 Challenges and Innovations in Transdermal Drug Delivery System for Poriasis Treatment
### Nanotechnology-Based Transdermal Delivery for Psoriasis Treatment
#### 5.1.1 Metal Nanocarriers for Psoriasis Treatment  
Metal nanocarriers like gold nanoparticles (AuNPs) have demonstrated certain advantages in psoriasis treatment that allow them to effectively penetrate the skin's stratum corneum and act directly on epidermal cells. In psoriatic keratinocytes, high levels of reactive oxygen species (ROS) and inflammatory factors create a vicious cycle of inflammation. To disrupt this cycle, [PERSON] [137] et al. synthesized nanoscale molybdenum particles with an average size of 93.2 nm, which showed no impact on HaCaT cells at concentrations up to 300 ppm and targeted the inflammatory NF-kB pathway by reducing levels of p-p65. In a mouse model of psoriasis induced by imiquimod, scoring and histological staining further indicated the anti-inflammatory effects of this material. [PERSON] [138] et al. developed a novel three-component alkyl-terminated gold nanoparticle featuring a 3 nm gold core and a 1000 Da polyethylene glycol (PEG) shell modified with 30% octadecyl chains. This sub-15 nm nanoparticle maintains colloidal stability within the skin due to the octadecyl chain modification, enhancing epidermal cell uptake. By inhibiting genes associated with hyperproliferation and inflammation within the downstream IL-17 signaling pathway, these AuNPs show potential in preventing psoriasis. Optimized alkylation of the Au core improves cellular uptake and provides a more pronounced inhibition of inflammatory genes, underscoring the therapeutic efficacy of these AuNPs in psoriasis treatment.
# Experimental and Numerical Study on the Low Velocity Impact Behavior of ABR Pipe
## 4 Numerical Modeling  
### Finite Element Model  
The finite element analysis model of drop hammer impact pipeline is established and calculated in ABAQUS Explicit [35]. The schematic diagram of the finite element model is shown in Figure 7. Since the drop hammer in the test can be regarded as an object that cannot be deformed at the time, the drop hammer can be set as a rigid body and simplified to a reference point, on which the initial impact velocity can be defined by using the velocity field. The mesh density affects the accuracy of the finite element model calculation. In general, the larger the mesh density, the closer the result of the model calculation will be to the real value. In this paper, the mesh size of the model is 10 mm, and the mesh subdivision size of the impact contact area is 2.0 mm. The finite element model of the specimen has a total of 646,136 units. The element shape selection criteria was employed to assess the finite element mesh. The criteria used for evaluating the elements included a geometric deviation factor greater than 0.2, edge shorter than 0.01, edge longer than 1, and stable time increment less than \(1\times 10^{-5}\). Additionally, the analysis errors were found to be 0 (0%), with no warnings reported. Dynamic face-surface contact is adopted between the hammer head and pipe, normal hard contact, tangential friction coefficient is defined as 0.3 by penalty function, which can properly simulate the sliding contact problem and output contact force at the same time. Other contacts are defined as normal hard contact and tangential friction contact by general contact. In the model, the drop hammer and constraint bars adopt a rigid body element, and the pipe and base adopt a C3D8 solid element. General elastic-perfectly plastic constitutive model was adopted by the pipeline and steel. The constitutive curve of ABR pipe is shown in the Figure 8. The elastic modulus and yield strength of steel fixture and steel bar are 206,000 MPa and 235 MPa respectively. As the research content in this paper is the process of low-speed movement, the strain rate effect is not considered in the material constitutive model. [PERSON] et al. [36] proposed the strain rate effect remains negligible if the initial impact velocity is less than 10 m/s. All specimens in this study have an initial velocity that remains below 7 m/s. The comparison between the numerical analysis and the experimental is reported in the subsequent section.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(v\)} & \multirow{2}{*}{\(S_{p}\)} & \multirow{2}{*}{\(m_{2}\)} & \multicolumn{2}{c}{\(F_{\text{max}}\)} & \multirow{2}{*}{\(F_{fit}\)} & \multicolumn{2}{c}{\(F_{fit}\)/\(F_{\text{max}}\)} \\  & & & & **1** & **2** & **3** & & **1** & **
## 3 Measurement Results and Discussion
### Mode Control in Optically-Pumped Molecular Laser  
Since the bolometers exhibit very high sensitivity, it would be of a particular interest to explore their properties far above the coupling antennas resonances. If a mode control could be possible under these unusual conditions, it could extend the device application areas into THz systems with a wider frequency band of operating and hence making the microbolometers more universal. In these experiments, we have chosen THz OPML as a source of radiation delivering discrete spectrum of 0.76 THz, 1.63 THz and 2.52 THz at various power levels. Special attention was given to polarization-related control; it deserves to be mentioned that the spatial mode profile was intentionally disturbed by varying power of the optical pumping and adjusting grating of the pumping laser [28].  
The experimental results of bolometers investigation are presented in Figure 6 together with reference data recorded by the commercially available pyroelectric detector. Emission powers at 0.76 THz, 1.63 THz and 2.52 THz frequencies were 1.6 mW, 1.6 mW and 7 mW, respectively, i.e., they were kept in a low level in comparison to conventional powers used in this type of lasers. As it can be seen, reference data presented in Figure 6a can be understood as the averaged signal since the detector used is insensitive to the polarization. The polarization-resolved mode structures--both for parallel and perpendicular polarizations--are displayed in panels (b, c) and allow to analyse in details spatial profiles of the unfocused modes. As the diameter of the laser beam profile amounts to 11 mm, its  
Figure 5: Spectrum of THz pulsed emitter (antenna switch) plotted in semi log scale **(a)**. The measurement geometry is shown in the inset, where fs denotes to optical femtosecond pulse exciting THz emission from LTG GaAs optoelectronic switch, D labels Ti-mB detector. Note sharp lines caused by water vapour absorption in the air decorating the THz emission spectrum. Beam profiles of THz pulsed emitter obtained with antenna-coupled Ti-mB optimized for resonant frequencies of 0.3 THz **(b)** and 0.7 THz **(c)**. White lines indicate intensity cross sections along the relevant axes.  
spatial deviations can be also nicely distinguished. The estimated polarization extinction ratio was found to be of 2.2 dB, 11.3 dB and 8.9 dB at 0.76 THz, 1.63 THz, 2.52 THz, respectively, suggesting additional application option of the Ti-mB for polarization-sensitive mode profile far above the antenna resonances. Relatively low PER value obtained at 0.76 THz can be explained by two constituents of the mode distinctly observed in Figure 6c.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Environmental Monitoring
#### 3.2.1 Area Coverage and Exploration  
Effective environmental monitoring requires thorough and systematic coverage of the target area. SI algorithms can optimize the exploration and coverage tasks:  
* ACO can be used where robots lay virtual pheromone trails to mark areas that have been explored. Other robots follow these trails to ensure thorough coverage without redundant overlaps.  
Figure 1: Smart agriculture monitoring system using IoT devices and sensors [50].  
PSO can model the movement of robots as particles in a swarm, allowing them to cover large areas by adjusting their trajectories based on their own experiences and those of their neighbors.
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 3 Method
### Model 1: Urban Mobility Structure Embedding with Multi-Scale Features
Specifically, the goal is to minimize the contrast loss, ensuring the embedding distances of the same nodes in the two enhanced views is closer, while increasing the distances between different nodes, as follows:  
\[I_{NCE}\Big{(}\mathbf{Z}^{1};\mathbf{Z}^{2}\Big{)}\ =\ -log\sum_{i}\frac{\delta\big{(}\mathbf{Z} ^{1}[i,:],\mathbf{Z}^{2}[i,:]\big{)}}{\sum_{j}\delta\big{(}\mathbf{Z}^{1}[i,:],\mathbf{Z} ^{2}[j,:]\big{)}} \tag{6}\]  
\[\mathcal{L}_{node}\ =\ \frac{1}{2}\Big{[}I_{NCE}\Big{(}\mathbf{Z}^{1};\mathbf{Z}^{2} \Big{)}+I_{NCE}\Big{(}\mathbf{Z}^{2};\mathbf{Z}^{1}\Big{)}\Big{]} \tag{7}\]  
where \(\delta(\mathbf{x}_{1},\mathbf{x}_{2})\ =\ \exp\Big{\{}-\|\mathbf{x}_{1}-\mathbf{x}_{2}\|^{2}/ \tau^{2}\Big{\}}\) is the Gaussian RBF distance, and \(\mathbf{Z}^{1}\) and \(\mathbf{Z}^{2}\) are the node embedding vectors of two enhanced views, respectively. **2. Global contrastive learning**  
To learn the global structure features, we determined the community ownership of each node by calculating the distance between each node and the community centroid, as follows:  
\[k_{i}\ =\ \arg\min_{\mathbf{k}\in\{1,\dots,K\}}\delta(\mathbf{Z}[i,:],\mathbf{\Phi}[k,:]) \tag{8}\]  
where \(k_{i}\) indicates that node \(i\) belongs to the community \(k\), and \(\mathbf{\Phi}\) is a trainable community centroid matrix. Then, we adopted the cross-comparison objective to compare the node representation of one view with the community centroid of the other view, to maximize the community consistency between the two views, as follows:  
Figure 1:0  
Figure 1:1  
where \(\textbf{Z}[i,:]\) represents the embedded representation of the \(i\)-th node, \(\textbf{\Phi}[k_{i},:]\) represents the centroid of the community \(k\) to which the \(i\)-th node belongs, and \(\omega(i,k)\ =\ \exp\Big{\{}-\gamma\|\textbf{Z}[i,:],\textbf{\Phi}[k,:]\|^{2} \Big{\}}\) is the RBF weight function. To train the node contrast loss and community contrast loss in a single loss function, we proposed the epoch decay coefficient \(\alpha(t)\) such that the loss weight of \(\mathcal{L}_{com}\) decreases as the number of training epochs increases, and the overall training loss is as follows:  
Figure 1:2  
where \(\alpha(t)\ =\ \exp(-t/\eta)\) decreases as the epoch \(t\) increases.
## 6 Conclusions  
In this paper, we compare, discuss, and analyze DCO-, ACO-, and U-OFDM in underwater optical communication systems. Since the peak transmitted power is limited, distortions caused by the peak power are taken into account. We propose to optimize the modulation index and compromise the clipping effects and SNR. By optimizing the SNR, the achievable transmission distance is obtained by the given transmission bit rate and BER performance. Considering the bandwidth limitation, we find the relationship between the optimal modulation index and the noise spectral density, which can guide us to achieve the best BER performance in different noisy environments. For the channels with different bandwidths, we compare the performances of DCO-, ACO-, and U-OFDM in underwater OWC systems. When the required bit rate is high (compared to the bandwidth), DCO-OFDM can provide a longer propagation distance than ACO- and U-OFDM. Otherwise, U-OFDM outperforms ACO- and DCO-OFDM.  
Figure 14: The maximum propagation distance for different bit rates. Peak transmitted power is fixed, and a bifloading algorithm is used.  
Figure 15: The maximum bit rate for DCO-, ACO-, and U-OFDM with an increasing \(\sigma_{h}^{2}\). The propagation distance is 20 m, and the 3-dB bandwidth of the LED is 10 MHz.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 4 Simulation Results and EXIT Analysis
This is due to the employment of advanced video coding techniques, such as variable length coding and predictive coding, resulting in the propagation of erroneous information into the neighboring pixels of the frame and even to the neighboring frames. Therefore, these errors result in a huge loss in the performance of the final decoded stream, with reference to the error free transmission. Figure 4: BER versus \(E_{b}/N_{0}\) performance curves of the SBC schemes presented in Table 2.  
For the subjective video quality performance, the video sequences containing both the luminance and chrominance components were decoded via the H.265 decoder and the obtained quality is shown in Figure 6. This perceptual video quality indicator is clearly inclining towards the LSSTC-SP-URC-SBC\({}_{[2,6]}\) scheme over the LSSTC-SP-URC-SBC\({}_{[2,6]}^{*}\) benchmarker. More precisely, the optimized LSSTC-SP-URC-SBC\({}_{[2,6]}\) scheme attains much better video quality at lower \(E_{b}/N_{0}\) values in comparison to the benchmarker. Distortions, even at the considerably higher \(E_{b}/N_{0}\) value of 30 dB in the LSSTC-SP-URC-SBC\({}_{[2,6]}^{*}\) scheme, result in a substandard and defeated performance compared to the optimized LSSTC-SP-URC-SBC\({}_{[2,6]}\) scheme.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 2 Control Design
### ESO-Based PIDSMC
#### 2.2.3 Perturbation Effects on Output Error  
It is important to understand the effects of perturbation (\(x_{3}\)) on the output error (\(e\)). The relation between these two variables is derived and explained in this section (Figure 4). By taking the Laplace transform of Equation (23), the relation between the output error (\(e\)) and sliding variable (\(s\)) is derived as follows:  
\[s=pe+c_{1}e+c_{2}\frac{e}{p}, \tag{54}\]  
\[s=e\bigg{(}p+c_{1}+\frac{c_{2}}{p}\bigg{)}, \tag{55}\]  
\[s=e\bigg{(}\frac{p^{2}+c_{1}p+c_{2}}{p}\bigg{)}, \tag{56}\]  
\[\frac{e}{s}=\frac{p}{p^{2}+c_{1}p+c_{2}}, \tag{57}\]  
by using Equations (57) and (53), the relation between error (\(e\)) and perturbation (\(x_{3}\)) is calculated as (\(\frac{e}{x_{3}}\)):  
\[\frac{e}{x_{3}}=\frac{s}{x_{3}}.\frac{e}{s}=\frac{e}{x_{3}}, \tag{58}\]  
\[\frac{e}{x_{3}}=\frac{\epsilon_{0}p^{3}+l_{1}p^{2}+l_{2}p}{\epsilon_{0}p^{4}+ A_{1}p^{3}+A_{2}p^{2}+A_{3}p+A_{4}}.\frac{p}{p^{2}+c_{1}p+c_{2}}, \tag{59}\]  
\[\frac{e}{x_{3}}=\frac{\epsilon_{0}p^{4}+l_{1}p^{3}+l_{2}p^{2}}{\epsilon_{0}p^ {6}+B_{1}p^{5}+B_{2}p^{4}+B_{3}p^{3}+B_{4}p^{2}+B_{5}p+B_{6}}, \tag{60}\]  
Figure 3: Effect of perturbation on actual sliding variable.  
where \(B_{1}=A_{1}+\delta c_{1}\), \(B_{2}=A_{2}+A_{1}c_{1}+\delta c_{2}\), \(B_{3}=A_{3}+A_{2}c_{1}+A_{1}c_{2}\), \(B_{4}=A_{4}+A_{3}c_{1}+A_{2}c_{2}\), \(B_{5}=A_{5}c_{1}+A_{3}c_{2}\), and \(B_{6}=A_{4}c_{2}\).  
For further explanation, the relation between the error and perturbation for the ESO based SMC is also presented and plotted in this section. This is helpful for explaining the superiority of the designed algorithm (ESO based PIDSMC). The relation of ESO-based SMC is derived as:  
\[\frac{e}{x_{3}}=\frac{e_{0}p^{3}+l_{1}p^{2}+l_{2}p}{e_{0}p^{5}+D_{1}p^{4}+D_{2} p^{3}+D_{3}p^{2}+D_{4}p+D_{5}}, \tag{61}\]  
where \(D_{1}=A_{1}+\delta c_{1}\), \(D_{2}=A_{2}+A_{1}c_{1}\), \(D_{3}=A_{3}+A_{2}c_{1}\), \(D_{4}=A_{4}+A_{3}c_{1}\), and \(D_{5}=A_{4}c_{1}\).  
The Bode plot of relations (61) and (60) can be observed in Figure 5. In the low-frequency (LF) region, the proposed algorithm (blue dotted line) can effectively work against perturbation, whereas in the mid-frequency (MF) and high-frequency (HF) region, the performances of both algorithms are same. The designer can change the frequency areas, depending upon the values of the controller parameters.