esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters
#### 3.4.4 Particle Recovery Coefficient  
The particle recovery coefficient characterizes the energy dissipation effect due to the normal relative motion that occurs during collisions between particles and between particles and the container walls. It is an important index for studying the energy dissipation characteristics of a particle system. The effects of different particle recovery coefficients (0.15, 0.3, 0.45, 0.6, and 0.75) on the total energy dissipation of the particle system and the total normal energy dissipation at three filling rates were obtained (Figures 11 and 12).  
As shown in Figures 11 and 12, as the particle recovery coefficient increased, both the total energy dissipated and the normal total energy dissipated by the particle system showed a gradual decreasing trend. The smaller the particle recovery coefficient was, the stronger the particle recovery ability was, and the greater the mechanical energy dissipated, the greater the overall dissipated energy of the particle system was. When the particle recovery coefficients were the same but the filling rate was different, a smaller filling rate of the particles resulted in a smaller proportion of space occupied by the particles in the damper cavity. The normal relative velocity of the particles decreased as a result of the increase in motion travel. The number of effective contacts between particles and between particles and the container walls decreased, and the contact collision dissipation of total energy decreased. When the particle filling rate is at a more favorable value, the total energy supplied by the damper to the particle system achieves greater dissipation through the contact body system.  
Figure 11: Total energy dissipated with different particle recovery coefficients.  
Figure 10: Total energy dissipated by the system for different particle densities.
## 6 Open Issues for Cooperative and Precoding Schemes in VLC
### Realistic Backhaul Links  
Both cooperative and precoding schemes rely on an effective backhaul link for either inter-AP or AP-central controller connections. Traditionally, backhaul links have been assumed to be ideal [123], i.e., lossless, noiseless and instantaneous, that considers to provide an unlimited capacity. However, this is an unrealistic scenario, which makes cooperative and precoding schemes inaccurate. Considering a realistic backhaul link is essential for evaluating the overall performance of the system. As shown in Section 2.1.3, several backhaul techniques have been proposed in the literature, considering both wired and wireless solutions. However, there is still a lack of comprehensive studies of backhaul link in terms of cost-efficient, energy-efficient, spectrum-efficient, when large scenarios containing tens and hundreds of APs are deployed. Indeed, this is considered as an open issue in the IEEE 802.11 bb VLC standard [17]. As a consequence, the analysis of cooperative and precoding schemes in a multi-cell scenario when considering realistic backhaul link is also an open issue that should be addressed in the near future.
## 4 RL-Based Decision Making Solution
### RL-Based Solution
#### 4.2.3 Exploration-Exploitation Strategy  
In RL, exploration and exploitation are both very significant concepts. An exploration means a selection of actions other than the ones that have been experienced before while an exploitation indicates the selection of the optimal actions. \(\epsilon\)-greedy strategy was implemented in the proposed solution to have a balance between exploration and exploitation. \(\epsilon\)-greedy method continues to explore, meaning that it gives a chance to execute random actions. It employs a tuning parameter, \(\epsilon\in[0,1]\), to indicate whether the agent should explore with a probability of \(\epsilon\) and exploit with a probability of \(1-\epsilon\).  
The calculation of the \(\epsilon\)-greedy strategy is shown in Equation (8). The RL agent utilises this equation to select the upcoming action at a particular state. In the proposed RL-based solution, the action with the lowest Q-value is selected; this means that, rather than finding a route with the highest reward, the proposed solution obtains a route with the lowest costs by greedily choosing actions having the lowest rewards.  
\[A^{\prime}=\underset{a\in A}{argmin}\ Q^{\prime}(S,A) \tag{8}\]  
where \(\underset{a\in A}{argmin}\) indicates the exploitation of \(Q(S,A)\) in regards to action \(A\). The ongoing exploration in a greedy way improves the instant reward.
## Results and Discussion
### XPS Analysis  
Considering the detection limit of FT-IR measurement, the X-ray photoelectron spectroscopy of samples were employed to evaluate the detailed chemical bonds formed on the surfaces of GO  
Figure 4: X-ray diffraction (XRD) patterns of Nano-TiO\({}_{2}\), GO, nano-PDA@GO-TiO\({}_{2}\) composites.  
Figure 5: Raman spectra of GO and nano-PDA@GO-TiO\({}_{2}\) composites.  
before and after its functionalization with PDA and TiO\({}_{2}\). Figure 6 gives the XPS spectra of GO and nano-PDA@GO-TiO\({}_{2}\) composites. As the spectrum of GO (Figure 6a) and PDA@GO-TiO\({}_{2}\) (Figure 6b) shown that the atomic percentage of C and O in GO was found to be 67.9% (C) and 32.1% (O), whereas it was 70.2% (C) and 23.2% (O) in nano-PDA@GO-TiO\({}_{2}\) composites. The significant decrease in the percent of O confirms the removal of oxygen containing functionalities. Figure 6c,d shows the C 1 s spectra of GO and PDA@GO-TiO\({}_{2}\), respectively. The C 1 s spectrum of GO (Figure 6c) can be curved into four peak components with binding energies at about 284.63, 286.73, 287.80, and 289.03 eV, attributable to the C-C/C=C, C-O, C=O, and O-C=O species, respectively. After reduction and modification by DA, the peaks of the C-O groups disappear, which indicates that most of the epoxide and carboxyl functional groups are removed and the GO is reduced to PDA@GO [46; 47]. More over the new strong peaks at 285.23 eV assigned to the C-N groups increase in the PDA@GO-TiO\({}_{2}\), as shown in Figure 6d, which may be attributed to the catechol/amino groups of the adhered PDA. on the other hand, The deconvolution of the N1s peak at 400 eV (Figure 6e) can be deconvoluted into three peaks at 399.7 (NH\({}_{2}\) and N=N=N), 401.6 (NH\(-\)C=O), and 403.5 eV(N=N=N) consistent with the chemical composition of the dopamine derivative, which indicated that the incorporation of azide-terminated dopamine in the reduced graphene nanosheets [48]. Compared with the GO, the high resolution XPS scan of the Ti2p region demonstrates the presence of the PDA@GO-TiO\({}_{2}\) (Figure 6f). The Ti2p3/2 and 2p1/2 peaks occur at 458.6 and 464.3 eV, respectively [49].
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 1 Introduction  
One of the key components of a nation's economic stability and environmental sustainability is its energy needs. Spain's diverse climate, which ranges from scorching summers to frigid winters, significantly influences patterns of energy use, mostly in the residential and commercial sectors. The demands for heating and cooling increase in the winter and summer, respectively, as temperatures rise from hot to cold, placing a significant strain on energy supplies. The ability to effectively manage energy resources by proactively placingan adequate supply with the least amount of environmental destruction results from an understanding of such dynamics. Furthermore, accurate energy demand forecasting, to which Spain has committed to using renewable energy sources, can improve grid integration going forward and advance sustainability [1; 2; 3; 4].  
Energy demand forecasting for the next three years will be key in the development of strategies that can balance energy supply with environmental sustainability. It will also, by accurately projecting consumption trends, enable energy providers to plan effectively for renewable energy integration, reducing dependence on fossil fuels and minimizing carbon emissions. This foresight enables optimal resource allocation to ensure energy systems meet peak demands without overloading the environment. In addition, effective demand forecasting of energy will provide economic stability, credible supply, and thus a cleaner and more sustainable energy future [5; 6; 7].
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### Parzen Window  
The Parzen window is referred to as the kernel density estimation method to realize the nonparameter test. Suppose that the samples follow an unknown probability density function while lying in D dimension space \(R\). The probability of each sample falling into the \(R\) space is defined as  
\[P=\int p(x)dx \tag{10}\]  
where \(p(x)\) is the probability density function. Then, we employ \(k\) to represent the number of samples in space \(R\). Equation (8) is further written as \(p(x)\approx\frac{k}{nV}\), where \(n\) is the number of samples and \(V\) is the volume in space \(R\). The window function is defined as  
\[H(\mu)=\left\{\begin{array}{ll}1,|\mu|\leq 0.5\\ 0,otherwise\end{array}\right. \tag{11}\]  
\(k\) is then calculated as  
\[k_{n}=\sum_{i=1}^{n}H\bigg{(}\frac{x-x_{i}}{h_{n}}\bigg{)} \tag{12}\]  
where \(h_{n}\) is the length of space \(R\). The probability density is then solved as  
\[P_{n}(x)=\frac{1}{n\hbar}\sum_{i=1}^{n}H\bigg{(}\frac{x-x_{i}}{h_{n}}\bigg{)} \tag{13}\]  
This method is often used to estimate the probability density function of samples. However, the estimated density equation is not smooth enough, and the estimation result is affected by the group distance. In this work, the probability density function and phase distribution of scintillation under different intensities are estimated by the Parzen window method.
# The Influence of Waste Composition on Landfill Gas Generation in a Pilot-Scale Lysimeter
## 1 Introduction
It has been reported that for the first landfill site, H2S is generated at less than 1000 ppm and for the second landfill site in which construction waste, domestic waste, and industrial waste are mixed, the H2S concentration reaches at least 4000 ppm and at most 30,000 ppm [21]. It is necessary to examine substances that result in an increase in the H2S concentration at the second landfill site and its cause. However, former studies have focused on underground water pollution caused by landfill leachate, cover material for odor control, and CH4 production efficiency enhancement [22; 23; 24]. Additionally, for the simulation of H2S production in landfill gas, research on pilot-scaled lysimeter reactors is insufficient. Moreover, research on the production characteristics of the leachate produced from mixed waste containing C&D debris waste and the landfill gas production characteristic is insufficient [19; 25; 26]. In this study, the main causes of H2S concentration increase were investigated in the second landfill site in the Sudokwon landfill site. A lysimeter reactor was designed for landfill modelling of landfilled wastes in the Sudokwon landfill site. The landfill consisted of seven samples of C&D debris waste, C&D debris waste and sewage sludge, C&D debris waste and solidified sludge, mixed waste, domestic waste, brick construction waste, and earth and sand construction waste, respectively. Three components (moisture, ash content, and combustibles) and elements were analyzed to identify the composition of the waste landfilled at the Sudokwon landfill site. The trend in the landfill gas and leachate from the seven samples of landfill was analyzed. Causes of H2S concentration and odor increase at the second landfill site was investigated according to the waste composition.
## 3 Bearing Fault Diagnosis Based on Deep Learning  
In this section, according to the signal processing technology, the original signal is processed by fast Fourier transform (FFT) to extract the characteristics of the bearing vibration signal.  
By building a deep learning network model, we study the bearing fault diagnosis method based on the bearing health state classification data.
## 2 Data and Methods
### Auxiliary Data Sets  
#### 2.5.1 LIDAR Wind Profile  
Wind LIDAR (Light Detection And Ranging) [39] is an optical remote sensing system to measure wind speed and direction as well as aerosol distributions. For that purpose, a pulsed light beam is emitted from a ground station into the atmosphere and is then reflected by aerosols. While the distance to an aerosol is determined by the time the light takes to travel to the target and back to the emitter, the velocity measurement relies on the Doppler shift of the optical wave. Knowing the frequency shift along three different coordinate axes it is possible to calculate the wind speed in each direction and subsequently the total wind speed vector. The main advantage of LIDAR compared to in-situ measure ment techniques is the large vertical range. Measurements are--depending on weather conditions and device - feasible up to altitudes of several kilometers. Since the principle relies on optical measurements, a wind LIDAR does not work properly in clouds or foggy weather. Doppler LIDAR measurements are already well established in the wind energy research and their accuracy has been tested against met mast data up to 100 m above ground (offshore and onshore (at least over not too complex terrain)), (e.g., [PERSON] et al. [40], [PERSON] et al. [41], [PERSON] et al. [42]).  
In this study, a so-called \"WindCube\" of type WLS8 vertical profiling, developed and manufactured by Leosphere (now Vaisala) and owned by the Institute of Flight Guidance at the Technical University (TU) Braunschweig [43], is used. The pulsed laser rotates in a coning pattern so that light is back-scattered by particles in the air and collected by the optical system. The acquisition of wind speeds in all three axial directions is accomplished by inclining the laser at 15\({}^{\circ}\) and measuring at azimuth intervals of 90\({}^{\circ}\). This creates four distinctive measuring volumes. An electronic processing unit converts the signal and post-processes the data. The measurement quality is evaluated according to a threshold based on the carrier-to-noise ratio (CNR), which is dependent upon weather conditions, such as the aerosol backscatter, turbulence, humidity, and precipitation.  
The system was deployed by UL International GmbH on 9 August 2019 (and still on going) on the coast of the Norderney island (Germany, Figure 1) to acquire coastal data as part of the X-Wakes project [44]. The system retrieves data of wind speed and wind direction at 25 altitudes (40 m to 500 m with a vertical resolution of 10 m up to 100 m, 20 m up to 400 m, and 50 m up to 500 m). In the framework of this study, LIDAR data are only used as complementary data to check and understand some unusual wind speed gradient depicted by SAR. LIDAR data allow us to analyse the wind variation at the coast, not only during the SAR passage period (single snapshot), but also before and after the satellite acquisition time.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 3 Results
### Patient Scan Experiments  
To verify the effectiveness of the proposed method on the clinical data with realistic motion, the method was applied to clinical experiments in which motion artifacts had been observed. The scan was performed on a GE Light Speed VCT scanner. The scan parameters were tube voltage 120 kVp and  
Figure 6: Comparison of the reconstructed images for the selected slice of head phantom: (**a**) Image reconstructed by OSEM; (**b**) image reconstructed by OSEM-BD; (**c**) image reconstructed by RL-OSEM; (**d**) image reconstructed by the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Metric** & **RMSE (HU)** & **CC** & **MSSIM** \\ \hline \multirow{2}{*}{OSEM} & Mean & 103.4223 & 0.7632 & 0.6963 \\  & Standard deviation & 27.5123 & 0.0601 & 0.0688 \\  & Mean & 98.6049 & 0.7867 & 0.7069 \\ \multirow{2}{*}{OSEM-BD} & Standard deviation & 25.4556 & 0.0598 & 0.0678 \\  & Mean & 52.7084 & 0.8177 & 0.7559 \\ \multirow{2}{*}{RL-OSEM} & Standard deviation & 11.0234 & 0.0594 & 0.0623 \\  & Mean & 38.2867 & 0.9385 & 0.9068 \\ \multirow{2}{*}{BD-OSEM} & Standard deviation & 8.7886 & 0.0312 & 0.0388 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of motion artifact reduction performance on head phantom.  
Figure 7: Zoomed images of a region of interest (ROI) from Figure 6.  
tube current 200 mA. Image size was 512 \(\times\) 512 pixels and the display window was [-5 HU, 75 HU]. Because the patient bed did not move with the patient during the scan, the patient bed data were removed. Three iterations with 30 subsets were applied for OSEM reconstruction, and four iterations for the BD-iteration. The other experimental parameters were set the same as those of the Shepp-Logan phantom experiments.  
Figure 8 shows the selected slice of reconstructed images and the repeated scan image (which is done because of the observed motion in the first scan). The uncorrected image was reconstructed with the scanner system software, as shown in Figure 8a. Table 4 shows the comparisons of RMSE, CC and MSSIM. Note that all reconstructed images were registered to the repeated scan before the calculation of these metrics. Because the positional differences are irrelevant for image quality [14; 30]. The results of patient scan experiments show that the reconstructed image obtained by the proposed method can effectively eliminate motion artifacts, and the artifact reduction effect of the proposed method is better than OSEM-BD and RL-OSEM methods.
## 2 Materials and Methods
### Total Mercury Analysis  
Analyses of THg in the soil and sediment samples were performed at the laboratory of the Philippine Nuclear Research Institute. Samples were prepared by air-drying and sieving up to US Standard 230 mesh (63 \(\rm{\upmu m}\)), to separate the silt-clay (\(<\)63 \(\rm{\upmu m}\)) size particles from the sand fraction. Only the \(<\)63 \(\rm{\upmu m}\) fractions were analyzed for THg concentrations. Total Hg analyses in the soil and sediment samples were determined using a direct mercury analyzer (Milestone DMA-80 evo, Sorisole, BG, Italy) following the U.S. EPA Method 7473 (mercury in solids and solutions by thermal decomposition, amalgamation, and atomic absorption spectrometry). This Hg analyzer is capable of determining THg concentrations in solid, liquid, and gas samples. Soil samples were weighed (0.10 g) and placed in a nickel boat without any pre-treatment. The boat is then introduced to the direct mercury analyzer where it is initially dried at up to 250 \(\rm{{}^{\circ}C}\), and then thermally decomposed at 900 \(\rm{{}^{\circ}C}\) in a continuous flow of oxygen. The THg content is determined using atomic absorption spectrometry at 254 nm. All reported THg concentrations of soil and sediments analyzed were the average of three replicates. The THg analysis results from DMA-80 were validated using certified reference material for stream sediments (GSD-1) prepared by the Institute of Geophysical and Geochemical Exploration of China, with a mean (S.D.) THg value of 0.018 (0.004) mg kg\({}^{-1}\). The mean (S.D.) value of the analyzed results using DMA-80 was 0.019 (0.002) mg kg\({}^{-1}\), which is in good agreement with the certified value.
## 3 Results
### Clinical Data  
Based on the ROC curve analysis of clinical data, there was a slight increase in the area under the curve of all texture analysis parameters combined with multiparametric MRI; the AUC was 0.82, whereas in the T2 + DWI alone, the AUC was 0.77. There was no significant difference between adding either DCE or texture analysis parameters to T2 + DWI; the AUC values were 0.79 and 0.82, respectively, as shown in Figure 4. For the clinically significant prostate cancer, the prediction model combining mpMRI and radiomics showed an improvement in AUC from 0.81 to 0.85.  
PCA was applied again to generate a set of principal components to assess the difference between healthy and cancerous tissues in the clinical data, using a combination of radiomics and DCE compared with the biopsy. The score plot, looking closely at the first two principal components, exhibited overlap between healthy tissue (normal prostate gland) and prostate cancer in some areas, which described the missed diagnosis and some separated areas indicating the efficiency of the adopted model in detecting the prostate cancer. Healthy tissue (normal prostate gland) was mostly separated from cancer patients, as shown in Figure 5.  
Furthermore, the correlation matrix in Figure S6 showed a similar pattern of highly correlated variables, i.e., highly correlated variables may cause PCA to overemphasise their contribution.  
DAPC was explored to identify clusters using PCA score vectors. The results were exemplified through the density plot of the two clusters, as shown in Figure 6.  
The DAPC showed an almost clear separation between the two clusters. Finally, DCA showed a similar pattern to the previous methods, with a model accuracy of 81%, which was determined using training and testing data, as shown in Figure 7.  
Figure 4: ROC curve for the clinical data. Four models were generated and compared with the gold standard TRUS biopsy. The orange curve is a combination between radiomics and mpMRI, showing a higher AUC of 0.82 compared with mpMRI alone in the red curve; the AUC was 0.77.  
Figure 5: The score plot based on the first and second principal components. The first principal component explained 36% of the variation, and the second component analysis explained 18.3% of the variation. The prediction model was a combination of the radiomic features with mpMRI, and the findings were compared with the TRUS biopsy.  
Figure 6: DAPC and how it clearly differentiates between the two groups healthy and cancerous tissue; there is a high degree of separation in most area. Red represents the cancerous tissues, whereas orange represents the non-cancerous tissues.