esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 4 Electricity Demand Pattern of Dataset  
Our study's scope includes the Thailand metropolitan area, which comprises Pathum Thani, Nonthaburi, and Nakhon, as well as Bangkok and the neighboring provinces. These provinces are linked to an enormous number of enterprises, offices, universities, and industrial parks, all of which increase the overall energy demand. For the period of 1 January 2012 to 31 December 2018, we used half-hourly electrical energy demand data from EGAT (same as [56]) in this paper. Based on meteorological information for the Bangkok metropolitan region, the period of cooling typically spans from March through September, with the highest temperatures and humidity occurring between April and August. Throughout this timeframe, there is a notable rise in electricity usage, primarily for cooling needs such as air conditioning. There is still a noticeable demand for space heating  
Figure 4: The architecture of the GRU memory block [53].  
during the cooler period from December to February. Additionally, there is a consistent need for hot water heating throughout the year in both residential and commercial settings to provide warm water [57]. According to electricity usage data, buildings consume roughly 70% of Bangkok's total demand, which includes residential, business, and institutional areas. The rest (30%) of the city's electrical consumption comes from the industrial sector, which includes manufacturing and production [58]. From Figure 5, we can see that for the moving average, a rolling window of 365 samples was used to track the data's consistent fluctuation over time. This plot shows that seasonality influences total demand growth, which follows a linear trend.
## 2 Related Work  
### Traditional Methods  
In early research, financial time series prediction mainly relied on econometric or statistical methods. The autoregressive integrated moving average model (ARIMA) and its enhancements are effective approaches for addressing this issue in econometrics and statistics [23]. Subsequently, ARIMA has been applied to time series forecasting in finance, resulting in several enhanced algorithms, including autoregressive (AR) [24], vector autoregressive (VAR) [25], autoregressive distributed lag (ARDL) [26], autoregressive conditional heteroskedasticity (ARCH) [27], generalized ARCH (GARCH) [28], and mixed data sampling (MIDAS) [29]. Although these techniques can be successfully used for short-term prediction, they are not suitable for nonlinear problems and have poor long-term prediction performance [30, 31].  
To address these limitations machine learning is introduced to analyze time series and successfully applied to stock price forecasting [32, 33]. Research indicates that machine learning algorithms, through adaptive noise suppression mechanisms such as regularization constraints or feature importance weighting, can effectively separate high-frequency noise from underlying signals in financial time series, thereby capturing the inherent nonlinear correlation patterns of data more accurately and significantly improving the prediction accuracy [34]. Machine learning methods include support vector machine (SVM), decision tree, naive Bayes, and random forest. [PERSON] et al. used a hybrid model of decision tree and SVM to predict future price trends [35]. [PERSON] et al. established a feature-weighted SVM and K-nearest neighbor algorithm to predict the stock market index [36]. [PERSON] et al. used convolutional neural networks for stock price prediction and found it outperformed multilayer perceptrons and support vector machines in predictive capability [37, 38]. By leveraging large-scale financial datasets, AI algorithms can identify complex nonlinear patterns and subtle market signals that are often overlooked by traditional methods. This capability enables more accurate stock price forecasting, early detection of market trends, and data-driven investment strategies that enhance portfolio performance [39, 40, 41, 42].
## 3 Antenna Characteristics
### Effect of the Size of Groove  
A circular groove is attached to the top-middle of the ground plane to remove the notched band at 8.82 GHz, the dimension of this groove has a significant effect on the bandwidth and reflection coefficient. Simulations were carried out on the groove to obtain a proper radius with good impedance matching and a wide bandwidth. The effect of the radius of the groove is shown in Figure 5. The desired result is obtained with a radius of 2.46 mm. This groove dimension brings down the notch with a good reflection coefficient at the second resonant frequency leading to enhanced overall performance of the antenna.  
Figure 4: Effect of the length of ground plane.  
Figure 5: Effect of the size of the center groove.  
The dimensions of this groove play a crucial role in determining the antenna's bandwidth and reflection coefficient. A radius that is too small has a poor reflection coefficient and a radius that is too big also has an extremely poor reflection coefficient. Therefore, the radius of 2.46 mm was determined as the optimal dimension for achieving the desired outcome in terms of reducing the notch and improving the impedance matching.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 5 Experimental Results
### Comparing Time Performance with Original 2D Detection Algorithms  
In this subsection, we test the time performance difference between our algorithm and the _FastDetect_ algorithm using exact comparison in non-prefix field conflict detection. Table 12 shows the average conflict detection time required for each filter in different-sized databases. Experimental data shows that our algorithm was no less than 10% faster than the general exact comparison methods in non-prefix field conflict detection for databases of different types and sizes. This also proves that filtering out the time in which exact comparison is not required will improve the time performance. Because the average time of performing exact comparison for every filter in databases ACL1 and ACL5 was minimal, the effect of the filtering algorithm was not obvious. The required time was almost the same for the filter conflict detection of these two databases. The performance of the ACL2 database was better than the other ACL databases except for ACL1 and ACL5 due to ACL2 containing the lowest ratio of requiring exact comparison sets. Similarly, FW1 performed better than most FW databases. For IPC databases, the huge difference in the number of exact comparisons required between databases IPC1 and IPC2 also showed that our algorithm performs better.  
Our method requires only small memory space to extend the original 2D conflict detection to 5D conflict detection for specific performance improvement. Since an additional data structure is not required, it prevents the possibility that the existing data structure will change with frequent addition of dynamic filters and application updates, and reduces the impact to the overall performance.
## 2 Rock Mass Characteristics  
### Geological Conditions in the Area of Shaft II  
The geological data used in this study were obtained from several sources. The G.281(05) borehole, drilled from a mining level at 950 m depth, provided core samples over a length of approximately 308 m. These samples were collected at regular intervals to determine lithology and coal seam thickness. Additional data were obtained from the geological profile of Shaft II and the mine plan of the 950 m level, which included geological information from previous excavation works. Field observations from the on-site inspection also played a crucial role in confirming the lithology and structural features encountered during excavation. The reliability of the data were ensured through cross-validation between these sources. Discrepancies were addressed by correlating borehole data with field observations and the geological profile, improving the accuracy of the interpretation.  
Figure 1: Arrangement of auxiliary working for the deepening of Shaft II (1—shaft bottom at depth of 980 m, 2—hoisting machine chamber, 3—technological shaft inset).  
The rock mass in the vicinity of Shaft II, within a depth range of 950-1150 m, consists of Carboniferous formations represented by the Lower Ruda (coal seams 417/1-418/1-2) and Saddle (coal seams 502/1-503-1) beds. These formations are composed of alternating layers of sandstones and claystones, with occasional interlayers of coal seams and mudstones. In the geological shaft profile, sandstones (63%) predominate over claystones (25%). The thickness of the coal seams varies from 0.80 m to 5.40 m.  
Groundwater conditions were not identified as a major concern in the area of Shaft II due to the relatively low permeability of the host rock and stable hydrogeological conditions. Field observations confirmed the absence of significant groundwater ingress or pressure buildup, which justified the decision to exclude groundwater as a design parameter in further analyses.
# Fretting Fatigue Behavior under Tension-Bending Mixed-Mode Loading
## 2 Problem Formulation  
### Model Description  
The numerical model of cylindrical pad-on-flat contact (see Figure 1) is established to evaluate the fretting fatigue behavior of the material under tension-bending mixed-mode loading. Regarding the load setting, two constant normal loads, \(P\), are applied at the top of each cylindrical pad, and the cyclic oblique load, \(F\), is applied to the right end of the flat specimen to simulate fretting cycles. The stress ratio of the cyclic load \(F\) is 0.1, and it is applied in a direction that has an angle (defined as the mode angle \(\theta\)) with the \(x\)-axis. Here, \(\theta\) = 0\({}^{\circ}\) represents tension-mode fretting fatigue, \(\theta\) = 90\({}^{\circ}\) indicates bending-mode fretting fatigue, and 0\({}^{\circ}\)\(<\)\(\theta\) < 90\({}^{\circ}\) signifies a tension-bending mixed-mode fretting fatigue condition. The left end of the specimen is fixed, and the horizontal displacement of the pad is zero. Both the specimen and the pad have the same thickness (the \(z\)-directional size), \(t\), and the coefficient of friction between them is \(\mu\). The magnitude of \(F\) and \(\theta\) should ensure that the tangential stress, \(q(x)\), distributed along the contact surface is less than the frictional force provided by the normal stress, \(p(x)\), i.e., \(q(x)<\)\(\mu p(x)\), and thus the contact remains in a partial slip regime. Under the action of a small relative oscillatory motion, the fretting fatigue phenomenon occurs at the edge of the contact surface.
## 4 Discussion  
### APD Model  
As stated in the introduction section, 5G channel models should describe the APDs. There is a number of functions that the different models use to shape the APDs. These include the Laplacian, Gaussian, von Misses, and logistics functions [33], as the shape of the SLs has been modeled using different functions. In reference [22], we found that in an urban microcell at 60 GHz, there were several SLs present in most of the measured APDs, so we proposed a Gaussian mixture model for them. However, for almost all the locations of the four macrocellular environments that we studied in this paper, there was a single SL in the APD. A (modified) Gaussian model should be adequate to describe the azimuthal APDs, while a Laplacian model may shape the elevation APD. The rms lobe angular spread (LAS), that is the standard deviation of the APD function, was calculated for all locations in all  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Environment**} & \multicolumn{2}{c}{**Open**} & \multicolumn{2}{c}{**Wooded**} & \multicolumn{2}{c}{**Suburban**} & \multicolumn{2}{c}{**Urban**} \\ \hline \multirow{2}{*}{Number of SLs} & \(\mu\) & 1 & & 1.71 & & 1 & 1.4 \\  & \(\sigma\) & 0 & & 1.50 & & 0.63 & & 1.14 \\ AoA & & Az. & El. & Az. & El. & Az. & El. & Az. & El. \\ \multirow{2}{*}{LAS (degrees)} & \(\mu\) & 49.3 & 23.6 & 58.8 & 49.3 & 52.5 & 33.7 & 47.1 & 37.5 \\  & \(\sigma\) & 7.3 & 8.0 & 29.0 & 14.3 & 8.2 & 7.5 & 23.6 & 26.0 \\ \multirow{2}{*}{rms LAS (degrees)} & \(\mu\) & 11.5 & 3.2 & 14.6 & 12.9 & 11.8 & 8.7 & 11.6 & 9.7 \\  & \(\sigma\) & 0.9 & 3.0 & 4.6 & 3.8 & 1.5 & 1.7 & 3.8 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Angular spread parameters.  
Figure 9: APDs for urban environment (**a**) azimuth and (**b**) elevation.  
the environments and its mean, \(\mu\), and standard deviation, \(\sigma\), are given in Table 3. This will help to incorporate these four macrocellular models to current 5G channel models. We also give in this table the lobe angular spread between nulls, defined in reference [40] as the angular span of an SL above a given threshold.  
The azimuth rms LAS was slightly over \(11^{\circ}\) for all the environments, except for the wooded one, where it increased to \(14.6^{\circ}\). The elevation rms LAS values were lower for the open environment and higher for the wooded one. The rms LAS value found in the urban environment, corresponding to \(9.7^{\circ}\), was between the \(7.8^{\circ}\) and \(10^{\circ}\) reported by other studies [32; 40], from measurements at 28 GHz in Manhattan. It is not far from the values proposed by the Winner II model [28], which are between \(11^{\circ}\) and \(15^{\circ}\) for channels at 60 GHz. The values found for 73 GHz, i.e., \(6^{\circ}\) in reference [32], are smaller.
## 3 Results
### Scapular Angles during Different Angles of Shoulder Flexion and Abduction  
The data regarding scapular angles during arm flexion and abduction at 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), and 120\({}^{\circ}\) for the paretic and non-paretic sides and compared with healthy controls [29] are presented in Table 3. Significant differences between paretic and non-paretic values were found for scapular tilts of 60\({}^{\circ}\) and 90\({}^{\circ}\) during arm abduction due to greater motion on the non-paretic side (\(p\) = 0.015 and \(p\) = 0.0017, respectively).  
Figure 2 shows the scapulothoracic UD changes corresponding to arm movements. Major differences were found between the paretic and non-paretic sides during arm abduction, while during flexion, the scapulothoracic rotation was not particularly affected on the paretic side. The SHR was calculated as the ratio between the angle of shoulder abduction or flexion and the respective mean scapular upward/downward rotation angle for the paretic and non-paretic sides (Table 4). For shoulder abduction on the paretic side, the SHR ranged between 3.3 and 4.7, while it was slightly greater on the non-paretic side, ranging from 4.9 to 5.4. For shoulder flexion, there was an inverted trend on the paretic side, in which the ratio decreased from 6 to 3.6, and on the non-paretic side, from 7.2 to 4.8.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Paretic Side** & **Non-Paretic Side** & **[PERSON] et al., 2020 [29]** \\ \hline
30\({}^{\circ}\) flexion & & & \\ PR & 2.3 (2.6) & 0.5 (1.6) & 1.4 (1.7) \\ UD & 5.0 (2) & 4.2 (2.2) & 2.6 (1.7) \\ TI & 2.4 (2.1) & 2.8 (3.2) & 2.5 (2.7) \\ \hline
60\({}^{\circ}\) flexion & & & \\ PR & 1.5 (5.3) & 0.9 (3.7) & 2.3 (2.8) \\ UD & 14.8 (4.8) & 12.8 (4.8) & 9.6 (3.1) \\ TI & 5.3 (6.2) & 6.1 (3.4) & 4.5 (4.2) \\ \hline
90\({}^{\circ}\) flexion & & & \\ PR & \(-\)3.3 (11.1) & \(-\)1.9 (5.3) & 1 (3.9) \\ UD & 23.7 (6.5) & 21.6 (6.5) & 18.6 (4.3) \\ TI & 10.0 (10.1) & 11.6 (3.5) & 8 (5.7) \\ \hline
120\({}^{\circ}\) flexion & & & \\ PR & \(-\)8.5 (8.5) & \(-\)7.3 (8.4) & \(-\)4.6 (5.2) \\ UD & 32.1 (3.1) & 25.8 (7.6) & 25.2 (5.4) \\ TI & 11.1 (13.7) & 18.9 (3.4) & 12.9 (6.6) \\ \hline
30\({}^{\circ}\) abduction & & & \\ PR & 1.7 (3.9) & \(-\)2.0 (5.1) & \(-\)1.7 (2.7) \\ UD & 9.1 (8.7) & 6.1 (5.8) & 3.7 (2.3) \\ TI & 2.3 (5.5) & 6.7 (3.6) & 1.9 (2.3) \\ \hline
60\({}^{\circ}\) abduction & & & \\ PR & 2.7 (6.8) & \(-\)2.9 (8.2) & \(-\)3.1 (3.9) \\ UD & 16.8 (10.4) & 12.9 (8.6) & 10.7 (3.4) \\ TI \({}^{\ast}\) & 3.5 (7.4) & 10.7 (4.9) & 4.1 (4) \\ \hline
90\({}^{\circ}\) abduction & & & \\ PR & 1.8 (10.4) & \(-\)3.3 (10.7) & \(-\)4 (4.8) \\ UD & 23.7 (8.8) & 17.1 (9.8) & 18.1 (4.1) \\ TI \({}^{\ast\ast}\) & 5.5 (10.5) & 14.0 (5.8) & 7.3 (5.3) \\ \hline
120\({}^{\circ}\) abduction & & & \\ PR & \(-\)6.3 (12.2) & \(-\)3.5 (11.5) & \(-\)3.5 (5.7) \\ UD & 32.5 (6.5) & 20.7 (10.8) & 24 (5.3) \\ TI & 7.6 (12.2) & 16.4 (6.4) & 10.3 (6.0) \\ \hline \end{tabular}
\end{table}
Table 3: Scapular angles during shoulder flexion and abduction.
## 3 Numerical Simulation of Wave Propagation in Glasses  
We implemented the elastodynamic finite integration technique (EFIT) in an effort to numerically simulate the ultrasound propagation in glasses taking into account dispersion, scattering, reflection, and mode conversion phenomena that may overwhelm the desired information concerning the structural properties of the material. The numerical simulation is performed in the time-domain and handles effectively the above \"defect\" acoustic phenomena [32; 33; 34] and the papers cited therein. The first step is to design the two-dimensional geometry of the glass sample or \"cell\", which is subjected to the acoustic field; then, we integrate the corresponding equations over this cell. We utilized an equivalent formulation of the Kelvin-Voigt constitutive equations by considering the glass sample as a visco-elastic isotropic medium [35; 36]. In the context of the Kelvin-Voigt model for an isotropic medium, the 2D linear elasto-dynamic equations of momentum conservation in terms of velocity and stress are:  
\[\rho\frac{\partial u_{x}}{\partial t}=\frac{\partial\tau_{xx}}{\partial x}+ \frac{\partial\tau_{xy}}{\partial y}+f_{x} \tag{4a}\] \[\rho\frac{\partial u_{y}}{\partial t}=\frac{\partial\tau_{xy}}{\partial x}+ \frac{\partial\tau_{yy}}{\partial y}+f_{y}\] (4b) and \[\frac{\partial\tau_{xx}}{\partial t}=(\lambda+2\mu)\frac{\partial u_{x}}{ \partial x}+\lambda\frac{\partial u_{y}}{\partial y}+g_{xx}\] (5a) \[\frac{\partial\tau_{yy}}{\partial t}=(\lambda+2\mu)\frac{\partial u_{y}}{ \partial y}+\lambda\frac{\partial u_{x}}{\partial x}+g_{yy}\] (5b) \[\frac{\partial\tau_{xy}}{\partial t}=\mu\left(\frac{\partial u_{x}}{\partial y }+\frac{\partial u_{y}}{\partial x}\right)+g_{xy} \tag{5c}\]  
where \(\lambda\), \(\mu\) denote the Lame constants; \(\rho\) is the mass density; and \(f_{ij}\), \(g_{ij}\) with \(i\),\(j\) = [\(x\),\(y\)] are the components of force and stress sources. The first step in the elastodynamic finite integration technique is to perform space discretization and integration of the above equations over the integration cell (glass sample). In Figure 2, a schematic representation of the velocity and stress components in a 2D grid is presented, where the central point of the integration cell is denoted by \(i\). Considering  
Figure 1: The two ultrasonic waves \(r_{1}(t)\) and \(r_{2}(t)\) as a function of time. Both signals have similar shapes, equal intensity (amplitude-normalized) and are only shifted in time. The time series are aligned by a time-lag determined through the cross-correlation function, which exhibits the strongest local maximum at time t = \(\tau_{s}\).
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 5 Titanium-Based Nanomaterials
### TiO\({}_{2}\) Porous, Supported, and Interconnected Structures  
Due to the tendency of TiO\({}_{2}\) nanoparticles to aggregate into larger clusters, they are often supported on various materials or incorporated into porous substrates and ordered interconnected three-dimensional structures [121]. Coupling TiO\({}_{2}\) with adsorbents that have a high specific surface area and strong affinity for pollutants can further enhance its photocatalytic activity. Porous materials like zeolites and ordered mesoporous silica have been commonly used as supports for TiO\({}_{2}\) nanoparticles [122].  
Zeolites, in particular, are mesoporous materials with high adsorption efficiency and uniform pore sizes, making them valuable support materials. Recent studies have shown that using waste zeolites can significantly improve photocatalytic efficiency for indoor pollutant removal [95]. Specifically, waste zeolites have been found to increase the adsorption of formaldehyde and reduce the recombination rate of charge carriers. Additionally, waste zeolites enhance the contact between pollutants and the photocatalyst through surface diffusion [95].  
Carbon-based support materials have also been utilized to boost photocatalytic activity. For example, carbon nanotubes (CNTs) have been employed as a support for mesoporous TiO\({}_{2}\) in the photocatalytic degradation of CO\({}_{2}\)[123] and acetone [124]. CNTs influence the morphology of TiO\({}_{2}\) aggregates and the size of its crystallites. It has been observed that increasing the CNT content leads to the formation of smaller anatase crystallites and a higher anatase fraction in the composite, thereby improving photocatalytic performance.  
Several studies have also suggested that three-dimensional hierarchically porous materials, composed of one-dimensional TiO\({}_{2}\) nanotubes, offer high efficiency in the photocatalytic degradation of pollutants [125]. This structure allows for greater pollutant adsorption due to its large number of pores, enhancing photocatalytic activity. Moreover, the hierarchically porous structure improves light diffusion into the internal regions of the photocatalyst, further increasing its efficiency.