esa-sceva/satcom-chunk-collection huggingface dataset:
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results  
The aim of the first experiment was to compare and evaluate the accuracy of wind power predictions produced by the respective model architectures described in Section 4. The study evaluated the resulting forecast errors and training time to determine which models are not only accurate but also efficient. Table 5 shows the resulting values of MAE and RMSE metrics (both reported and normalized) describing the accuracy of the models, as well as the time taken to train them.  
LSTM model, as shown in the first row of Table 5, presented a weak correlation between wind speed, wind gusts and generated power, providing not only the poorest (nMAE = 0.2119, nRMSE = 0.2437) wind power prediction results, but also requiring an extremely long time (16,250.4 s) to train. This duration was at least 27 times longer than that of any other model evaluated in the experiment. This indicates that this model is less suitable for wind power forecasting when wind behavior is particularly erratic or when the data is noisy. These findings are supported by results obtained by other researchers, which show that the model performs reasonably well for a short period of 1-h forecasts, but the accuracy of the model's prediction decreases significantly for longer periods (e.g., when the forecast horizon is 24 h) [14].  
Statistical methods, including Exponential Smoothing, ARIMAX, and Random Walk with Drift (see gray shaded rows in Table 5), demonstrate significantly better performance and efficiency than the LSTM model. All three methods yielded similar results in (n)MAE and (n)RMSE metrics, which indicates that time series patterns are captured well by all of them. However, the Random Walk with Drift model took the shortest time (35.39 s) to train compared to the other two models; thus, from the accuracy and training time perspective, it could be considered the most efficient in the group.  
Moving back to machine learning models, the LTC model outperformed LSTM, XGBoost, and statistical methods by at least \(\sim\)2% in terms of MAE error metrics, which proves its potential in wind power prediction. At least 5% better RMSE values show that LTC, as well as XGBoost, handle outliers better. Despite the computational complexity of neural networks, which was more time-consuming than statistical methods, the potential of both methods for wind power prediction is clearly evident.  
The Hybrid (XGBoost + LTC) model achieved the best overall performance, balancing accuracy and efficiency. Compared to the best-performing models, its MAE and RMSE values improved by at least 16% and 13% respectively, keeping the model's training time relatively low. This suggests that hybrid models, leveraging the strengths of machine learning methods, can offer the best trade-off between accuracy and computational cost.  
Given the best results obtained with LTC, XGBoost, and Hybrid (XGBoost + LTC) models, further in-depth studies were limited to this group.
## 4 Experiments and Results
### Datasets and Basic Experimental Setup  
All ukiyo-e images were obtained from the Ritsumeikan ARC ukiyo-e Dataset which was released from the Art Research Center, Ritsumeikan University [8]. We only selected 1000 images for training in the demonstration system due to the computational cost of utilizing a low specification cloud server equipped with a 4 core Intel Xeon Platinum 8255C CPU and 16 GB RAM, which is a provisional demo environment. From 1000 images we can extract image training datasets with total permutation (1000, 3) pairs, and 12,000 pairs of cross-modal training data. We augment negative samples for cross-modal training by randomly selecting color descriptions that the image does not contain. Two color name datasets in English are used for fine-tuning and evaluation experiments of the model: (1) the HCN dataset for training and testing, where 20% of the training data was selected as the testing data during the training process, and (2) the CN [13] dataset for evaluation. The HCN dataset [12] is a handpicked list of 29,205 unique color names from various sources and thousands of curated user submissions. CN is a color name dataset including over 1200 color names and corresponding RGB values. We extracted the 'color name-RGB value' pair data corresponding to the RGB values recorded in the two datasets. As shown in Table 3, the vocabulary differences between the two datasets are obvious, and the different color names account for almost 48%. Color name examples from HCN and CN are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Totally Different** & **Overlap or Differ in Alphabetic Case** & **The Same** \\ \hline Proportion & 721 (48\%) & 570 (37\%) & 223 (15\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vocabulary differences between CN and HCN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Label: Anchor** & **Label: Positive** & **Label: Negative** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fine-tuning for learning image representation focused on the similarity of the sketch structure.  
To evaluate the effectiveness of the fine-tuning process, we used two datasets to evaluate the proposed method:  
1. the CN dataset and corresponding RGB color cards, where the color cards were extracted according to their HEX code and used as input for image feature extraction, and
2. the 100 most recent color description data obtained from colornames.org [34], which provides a color naming interface. We also collected the color cards corresponding to these color descriptions but only used them for visualizing text embeddings. The collected data samples for evaluation are shown in Table 5.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background  
This section will explore the relationship of suggested technologies with healthcare to better understand the proposed solution and the motivations behind it. Further, it will review rural communities' potential healthcare challenges and the existing solutions to find the current gaps and propose a solution based on the identified gaps.
## 4 NR Navigation System  
This section presents the system block diagram of the NR transmitter and navigation receiver. We give a detailed introduction to the receiver tracking loop. A novel correlation integration scheme is proposed to avoid the interference caused by the CP and inter-carrier. Based on the tracking loop, an extended Kalman filter (EKF) is proposed to realize LEO NR positioning.
## 2 Materials and Methods
### T-Splines  
T-Splines have been introduced as a strong candidate to replace NURBS in isogeometric analysis (IGA) with applications on curved structures [25]. T-Splines illustrate the ability to represent complex geometries seamlessly; unlike NURBS, which enforces a rigid tensor product structure that can lead to fragmentation and discontinuities between patches, T-Splines allow for local refinement at T-junctions. This means it does not require many cross-nodal repeat patches to curve around the geometry neatly and properly without flowing over the edges of inserted shapes. Furthermore, T-Splines are used to reduce the required control points for a more efficient model and correspond to reduced degrees of freedom in analysis [22]. These advantages allow T-Splines to be particularly good choices for the design of curved surfaces. They still preserve basic geometry while optimizing computational resources, comparing favorably with traditional NURBS-based approaches. In our previous work [25], we clearly showed the advantage of T spline; notably, T-Splines offer several advantages over the traditional NURBS in isogeometric analysis. T-Splines also handle trimmed and untrimmed surfaces more effectively, making them particularly suitable for complex geometries. Additionally, their compatibility with Bezier extraction facilitates their integration with finite element analysis (FEA) software. Compared to NURBS-based IGA and the FEM, T-Splines improve computational efficiency while maintaining high accuracy in structural analysis, especially for mechanical structures with intricate surface details. The mathematical expression for T-Splines with the parametric coordinates \(\xi\), \(\eta\) can be expressed as [23]  
\[\text{T}(\xi,\eta)=\sum_{i=1}^{n}R_{i}(\xi,\eta)P_{i},\ \ \ \ \ R_{i}(\xi,\eta)=\frac{N_{i}(\xi,\eta) \omega_{i}}{\sum_{i=1}^{n}N_{j}(\xi,\eta)\omega_{j}} \tag{1}\]  
where \(P_{i}\) is the T-Splines control point; \(R_{i}\) and \(\omega_{i}\) denote the rational blending function and weight of the \(i\)-th control point; \(N_{i}\) is the B-Splines basis function defined on a topologically rectangular domain; and \(n\) is the total number of control points.  
B-Splines basis functions are constructed using the Cox-de Boor recursion formula:  
Basis function for 0 degrees:  
\[N_{i}^{0}(\xi)=\left\{\begin{array}{ll}1,&if\ \xi_{i}\leq\xi\leq\xi_{i+1}\\ 0,&otherwise\end{array}\right. \tag{2}\]  
Basis function for higher degrees:  
\[N_{i}^{p>0}(\xi)=\frac{\xi-\xi_{i}}{\xi_{i+p}-\xi_{i}}N_{i}^{p-1}(\xi)+\frac {\xi_{i+p+1}-\xi}{\xi_{i+p+1}-\xi_{i+1}}N_{i+1}^{p-1}(\xi) \tag{3}\]where  
\[N_{i}(\xi,\eta)=N_{i}^{p}(\xi).N_{i}^{q}(\eta) \tag{4}\]
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
### HTTP Flood Attack  
Users access CCE services through a web browser by using the GET or POST request method, which is handled by an HTTP server [1]. HTTP flood attacks are DDoS attacks that target the services, applications, or protocols within the OSI model's application layer (layer  
Figure 1: Data exchange through HTTP on the web.  
Figure 2: Most common protocols targeted by application-layer attacks.  
7), such as their web services, web servers, and HTTP. Since the HTTP is the foundation of web-based requests and data exchange, these attacks can quickly deplete CCE resources and services. These attacks are particularly difficult to detect because the malicious requests are indistinguishable from normal ones.  
HTTP flood attacks are typically carried out via botnets or malware-infected devices to send out multiple attack requests, resulting in a denial of service. There are two primary types of HTTP flood attacks: HTTP-GET and HTTP-POST.  
* _HTTP-GET Attack:_ An HTTP-GET attack involves multiple computing devices sending HTTP-GET requests for resources, such as documents or pictures, to a targeted web server (WS) until it is overwhelmed and stops responding to all requests, including legitimate ones.
* _HTTP-POST Attack:_ An HTTP-POST attack targets web applications that use the HTTP-POST method to handle user-submitted data. The attacker floods the server with fake HTTP-POST requests, overwhelming its resources and causing it to crash or become unresponsive. This can disrupt legitimate traffic, making the website inaccessible and causing potential damage. These attacks are often automated and difficult to detect, making them a significant threat to online businesses and organizations.  
The HTTP-GET flood DDoS attacks pose a significant security threat in the CCE. They have been identified as the most common form of application-layer attack [12]. It is essential to protect against these attacks to maintain the availability and reliability of web applications and services.  
Figure 3 shows that HTTP-GET DDoS attacks are the most frequent application DDoS-layer attacks [12].
## 3 Practical Implementation
### Pixel Dispersion Properties
Due to this fact, when a TM mode propagates in the unit cell along the \(x\)-axis, \(\beta_{TM}\) increases considerably. Increasing \(c_{b}\) also affects the dispersion properties of the TE mode (Figure 4b); when the mode propagates in the cell towards \(\phi=90^{\circ}\), \(\beta_{TE}\) increases in the \(y\)-axis. On the other hand, the effect of broadening the slot corresponds to an electrically larger patch. In Figure 4c,d, the slot width towards the \(x\)-axis (\(w_{a}\) parameter) is enlarged, and due to this effect, \(\beta_{TM}\) and \(\beta_{TE}\) increase when TM and TE modes propagate in the cell at angle of \(\phi=0^{\circ}\) and \(\phi=90^{\circ}\), respectively. It is necessary to remark that this patch geometry is especially attractive due to the multiple possibilities for its geometrical parameterization, which make it possible to independently control the dispersion properties for both TM and TE modes within a certain frequency band. For instance, the patch geometry shown in Figure 4c,d (pixel B) allows the synchronized propagation of TM and TE SWs when impinging on the patch along its larger axis (in this case, \(\phi=90^{\circ}\)) at 13.5 GHz. As can be seen, the isofrequency curves of both modes coincide at this frequency, and the phase constant of both modes is the same: \(\beta_{TM}=\beta_{TE}=597\) rad\(/\)m. However, the modes' synchronization can be also obtained with other geometrical parameterization. This is the case, for instance, for pixel C (shown in Figure 4e,f), in which the cross slot is increased along the shorter axis of the ellipse (\(c_{a}\) parameter has been increased). This geometry also ensures \(\beta_{TM}=\beta_{TE}=597\) rad\(/\)m at 13.5 GHz, when TM and TE modes impinge on the cell along the \(\phi=0^{\circ}\) angle. This feature is also shown in Figure 5.  
It has to be mentioned that the phase synchronization bandwidth is mainly limited by the TE mode (see Figure 5), as the frequency dispersion behavior of this mode changes faster with frequency than the TM SW dispersion.
# A Primer on CFD-DEM for Polymer-Filled Suspensions
## 2 Single-Phase Fluid Flows
### Single-Phase Fluid Model Assessment  
In this section, we start by validating the methodology for the calculation of the fluid velocity, stress tensor components, and torque expressions derived in Section 2.2. In Figure 3, we show the fluid azimuthal velocity component \(v_{\theta}\) and the shear stress component \(\tau_{\theta z}\) at several radius locations. As expected from Equation (6), the azimuthal fluid velocity component profile is linear and the shear stress component is constant along the \(z\)-direction for each radial location.  
Figure 4 shows the contour of the magnitude of the fluid velocity for a Newtonian fluid moving inside a parallel-plate rheometer. As expected, the magnitude of the fluid velocity \(\underline{v}\) varies from zero at the bottom plate to a maximum value at the top plate. In addition, for a fixed height in the parallel-plate rheometer, the velocity profile is linear from the center to the edge of the plate.  
The numerically calculated torque for a Newtonian fluid is shown in Figure 5 and is compared with the exact value given by Equation (8). The relative error, in percentage, between the numerical torque results and the values of the analytical solution is also shown in Figure 5. For all shear rates tested (\(0\leq\dot{\gamma}\leq 100\)), the relative errors are less than 0.15%. Note that the radius is fixed; thus, the dependence of the torque profile on the radius is not observed.  
Figure 2: Schematic of the mesh employed for the single-phase fluid flow calculations in a parallel-plate rheometer.  
The torque results for a power law fluid were also numerically calculated and compared with the analytical solution from Equation (9) for \(n=0.3,0.6\), and \(0.9\) (see Figure 6). Again, the numerical results and the values of the analytical solution agree remarkably well. We no longer have a linear profile. Introducing a nonlinearity into the constitutive equation leads to a nonlinear torque profile that depends on the power of \(n\). For higher values of \(n\) (\(n\to 1\)) the Newtonian profile is restored and we observe an almost linear profile. For low values of \(n\), the non-Newtonian flow behavior is more pronounced and the fluid has more difficulty in increasing the torque for higher shear rates. This means that the stresses on the wall are lower due to the strong shear-thinning effect leading to lower viscosity.  
We can, therefore, conclude that the numerical implementation for the single-phase fluid reproduces well the physics of both Newtonian and non-Newtonian flows.  
Figure 3: (**a**) Azimuth velocity \(v_{\theta}\) and (**b**) shear stress \(\tau_{\theta z}\) fluid components for the single-phase fluid flow calculations in a parallel-plate rheometer.  
Figure 4: Velocity magnitude contour for the single-phase flow of a Newtonian fluid inside a parallel-plate rheometer.  
Figure 5: Analytical and numerical results for Newtonian torque calculations on parallel-plate rheometer with a gap of 0.5 mm.
## 2 Materials and Methods
### Study Area and Landslide Impact Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Data** & **Resolution** & **Source** & **Purpose** \\ \hline
4 & Pre-landslide inventory & - &
\begin{tabular}{c} Sichuan Ecological and \\ Environmental Restoration Institute \\ National Cryosphere Desert \\ Data Center \\ \end{tabular} & Landslide inventory \\
5 & Earthquake-induced landslides & & \
\begin{tabular}{c} Data Center \\ European Space Agency (ESA) \\ \end{tabular} &
\begin{tabular}{c} Landsatide inventory \\ InSAR and deformation \\ \end{tabular} \\
6 & SAR images & 10 m &
\begin{tabular}{c} European Space Agency (ESA) \\ \end{tabular} & InSAR and deformation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Jizhaigou area and SAR images. Figure 3: _Cont_. Figure 2: Landslides and faults in Jiuzhaigou. In (**a**), the purple triangles are pre-earthquake landslides, and red dots are post-earthquake landslides. In (**b**), F180 (bottom left), F181 (middle), and F182 (top) are the Minjiang Fault, the Tazang Fault, and the Guanggaishan-Dieshan Fault, respectively. Figure 3: _Cont._  
Table 2 presents the data presentation and sources for landslide factors. The slope affects the climate, vegetation distribution and potential energy of rocks in the area. The landslide probability is related to the slope angle, and different aspects influence solar radiation and water flow distribution. Profile curvature and plan curvature influence flow velocity and direction. The active faults with significant sliding displacement of rock on both sides are areas of high landslide incidence. Lithology and LULC are also important landslide factors. In the study region, mudstone and limestone were the main lithological types. The TWI is an indicator of the effects of runoff flow and volume. As an important external factor, rainfall infiltration increased the water content of the soil, raised interstitial pore water pressure and seepage pressure, reduced the angle of internal friction and increased the probability of landslides. Finally, NDVI reflects the influence of plant canopy and vegetation distribution and is often used as a landslides factor. In addition, for training and validation, we randomly created the same number of non-landslide points as the landslide points, including 410 pre-earthquake landslide points, 97 EIL points, and 507 non-landslide points. These pre-earthquake landslides in Jiuzhaigou were mainly distributed on both sides of the Tazang Fault, and the earthquake-induced landslides were located between the Minjiang Fault and the Tazang Fault. These non-landslide points were randomly distributed, and we ensured that the distance to the landslide points was greater than 500 m. Then, the study set a 100 m buffer zone of landslides and non-landslides as their sphere of influence. The buffer distance was adjusted at 100 m based on the influential range of landslides and non-landslides. The landslide inventory was randomly divided into two portions to fit and test LSM models: 80% of landslides were used for training, and 20% of landslides were used for validation [50].
###### Abstract
Therefore, the beams exciting these plasmons must have equal diameters, and all wave energy must be concentrated in one annular beam. Theoretically, so-called perfect beams possess such properties [14; 15]. Such beams are generated due to focusing by a lens of Bessel beams of different orders with the same transverse wave numbers [16]. In practice, however, \"perfect beams\" are far from perfectness. The reason is that, in reality, there are no ideal Bessel beams with an infinite cross-section and infinite energy. The properties of real Bessel beams depend on the methods of their generation, which have even a greater effect on the characteristics of perfect beams, as will be shown below. In the visible range, Bessel beams are usually generated via formation of phase transparencies using commercially available spatial light modulators. No radiation modulators are available so far in the terahertz range, and phase diffractive elements [17] made of plastics [18] or silicon [19] are mostly used. This work is devoted to analytical and numerical study of the characteristics of ideal beams obtained using diffractive phase axicons. In Section 2, we describe the characteristics of three types of axicons that can be used to generate Bessel beams. In Section 3, we explain the experimental setup in which ideal beams will be used, present an analytical calculation of the electric field of an ideal beam for the case of a binary phase axicon, and perform a comparison with numerical calculations. In Section 4, ideal beams created by different types of axicons are compared. Section 5 is devoted to a discussion of the results and their use in experiments on excitation of vortex plasmons.
## 3 Results and Discussion  
### Fourier-Transform Infrared (FTIR) Spectroscopy  
The three selected PCMs are compared by FTIR analysis before and after EG impregnation, as shown in Figure 2, in order to identify the functional groups of the PCMs, which can be correlated with the leakage behavior. As expected, the presence of EG reduces the transmittance of the composite materials. The position of the peaks remains at the same position before and after impregnation.  
The peaks at 2915 and 2850 cm\({}^{-1}\) correspond to the stretching of the aliphatic C-H, while those at about 1470 and 715 cm\({}^{-1}\) indicate C-H bending and wagging; thus, RT35 and RT44 are organic aliphatic compounds. No other peaks are present in the RT35 and RT44 spectra, indicating that these two PCMs are paraffins.  
MPA, a mixture of myristic and palmitic acids, presents a stretching peak at about 2915 and 2850 cm\({}^{-1}\), as in the already described olefins. In addition, it presents an intense peak at about 1700 cm\({}^{-1}\), attributed to C=O stretching and a large band at 2600-2550 cm\({}^{-1}\) typical of the carboxylic group. Moreover, the fingerprint zone confirms that MPA is an aliphatic fatty acid [35].  
Figure 2: FTIR spectra of neat PCMs and composite materials: (**a**) RT35 and RT35/EG, (**b**) RT44 and RT44/EG, (**c**) MPA and MPA/EG.