esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition
#### 2.1.3 Demand and Generation Profiles  
The total annual energy demands are calculated in the different scenarios. There, industrial load profiles with hourly resolution are considered to model the power requirements of individual energy carriers. The profiles, their origin, and their usage are briefly outlined in this section.  
The electricity demand was calculated based on measured load profiles [23], representing various industries of the industrial sector. The profile of the electricity demand is used as the basis for the estimation of the required power generation profiles in subsequent tasks.  
Industrial gas demand profiles were made available by AGGM Austrian Gas Grid Management AG in a standardized and anonymous format [24].  
The generation profile of electrical energy is based on the data of the APG generation statistics [25]. The data set covers generation from the following energy carriers: wind power, PV, biogenic, geothermal, waste and hydropower. Hydropower is subdivided into the categories \"run-of-river\" and \"(pumped) storage power plants\". The latter also includes negative values if, for example, the power plants are in pumping operation  
Figure 3: Final energy consumption [TWh] per energy carrier and industrial sector and their renewable shares [%] in Austria in 2019 [5].  
Figure 2: Exemplary energy flow chart of the iron and steel sector in Austria in 2019 based on the used energy carriers (left) and the six useful energy categories (right) where fossil fuels are in reddish colors and renewables in greenish colors (data based on [5]).  
when low or negative electricity prices occur. Thus, partially negative values for the total generation of electrical energy can occur if pumping operation outweighs the capacities of other producers. This implicitly depicts the actions in the energy markets caused by (renewable) electricity generation. Geothermal and waste incineration are assumed to be constant in power generation at 0.88 TWh according to 2017 generation levels. All other energy carriers are scaled up using the potentials shown in Figure 4 with the corresponding generation profiles.
## 2 Materials and Methods
### 3D Interframe Displacement Estimation
#### 2.4.2 Single-Probe Compounding  
For the single-probe compounding method, the axial displacement (\(d_{0}{}^{ax}\)), together with the lateral inter-frame displacement estimated by displacement compounding, served as basis for strain estimation. In displacement compounding, both angular axial displacement components (\(d_{\alpha}{}^{ax}\), \(d_{-a}{}^{ax}\)) obtained using the angled transmissions for each probe (\(A_{-\alpha}\), \(A_{\alpha}\) for probe A and \(B_{-\alpha}\), \(B_{\alpha}\) for probe B) were used to derive the lateral displacement \(d_{0}{}^{comp\_lat}\):  
\[d_{0}^{Comp\_lat}\ =\ \frac{d_{\alpha}^{ax}\ -\ d_{-\alpha}^{ax}}{2\sin\alpha} \tag{1}\]  
Combining the two axial displacements into a single lateral displacement has shown to be beneficial, since a lateral displacement estimate based on the phase information of two angled acquisitions yields a more accurate estimate than the lateral displacement estimation originating from zero-degree acquisitions [18]. To determine the elevational displacement component for the single-probe compounding approach, the displacements \(d_{0}{}^{de}\), \(d_{a}{}^{de}\), and \(d_{-a}{}^{de}\) were combined to a single estimate by means of the median displacement. Please note that these displacements are perpendicular to the elevational direction.
## 2 Materials and Methods  
### Study Area  
Our research area encompasses thirteen provinces in the southern China (73-135\({}^{\circ}\)E, 18-30\({}^{\circ}\)N), including Hainan Island, Guangxi, Guizhou, Yunnan, Sichuan, Chongqing, Guangdong, Hunan, Hubei, Tibet, Jiangxi, Fujian, and Zhejiang. The region's terrain is complex, including the Hengduan Mountains, Yungui Plateau, Sichuan Basin, Guangxi Basin, and coastal plains, which is higher in the west and lower in the east. The climate is notably diverse and variable. The Tibetan Plateau, due to its high altitude, exhibits a cold and arid plateau climate. The Hengduan Mountain region, with its high mountains and deep valleys, displays diverse climates, ranging from subtropical to cold temperate. The Sichuan Basin, surrounded by mountains, often experiences basin effects, resulting in hot summers and mild winters. The middle and lower reaches of the Yangtze River plain belong to a subtropical monsoon climate, characterized by distinct seasons and abundant precipitation.  
The vegetation types in southern China are diverse, ranging from subtropical evergreen broad-leaved forests to cold temperate coniferous forests, exhibiting significant regional geographical differences in vegetation distribution (Figure 1). Vegetation in the Tibetan Plateau is dominated by alpine meadows and grasslands, while the Hengduan Mountain region showcases a variety of vegetation, from subtropical evergreen broad-leaved forests to cold temperate coniferous forests. The vegetation in the Sichuan Basin is mainly composed of crops and subtropical forests, while the middle and lower reaches of the Yangtze River plain are primarily characterized by crops and wetland vegetation, especially extensive areas of rice cultivation (Figure 1).  
Figure 1: Map of study area (**a**), (red polygon) and the corresponding vegetation types (**b**).
## 4 Results and Discussion
### Performance Analysis of Image Communication System
#### 4.2.3 Evaluation of Communication Systems over Rayleigh Fading Channel
Overall, the 16-QAM system with MMSE channel estimation maintained the highest MS-SSIM score, while the 1024-QAM system with MMSE estimation had the lowest throughout the experiment. Figure 7: Performance analysis of various OFDM systems in terms of (a) BER, (b) PSNR, and (c) MS-SSIM with respect to various E\({}_{\rm b}\)/N\({}_{0}\) under the Rayleigh Fading channel. Figure 8 shows recovered images from communication system over Rayleigh Fading channel. Compared to the visual inspection of images from AWGN channel discussed in Section 4.2.2, the images from the Rayleigh Fading channel are heavily distorted and most of the ROI information at the lower \(\rm E_{b}/N_{0}\) are not clearly visible. The amount of noise on the images decreases slightly with higher \(\rm E_{b}/N_{0}\), and that is sufficient to make the information on the ROI visible due to the robust design of the traffic signs with different color contrast [76; 77]. Therefore, for higher \(\rm E_{b}/N_{0}\), the images may be heavily distorted but the information in the ROI (speed limit) is still slightly visible, and the DL models can exploit that and extract the features from the ROI to predict the signs correctly, which will be further discussed in Section 4.4. Another trend that is observed is that the images from the M-QAM systems with odd length of symbol (128-QAM) have reduced the brightness compared to the even length of the symbol, which retained brightness the same as the original image. In terms of channel estimation, the images at \(\rm E_{b}/N_{0}\) 0 dB from system utilizing MMSE channel estimation (bottom row) is clearer compared to LS channel estimation (top row) across the M-QAM, where the information in the ROI is not visible at all for the LS channel estimation. Therefore, it indicates that MMSE retains better quality images for lower \(\rm E_{b}/N_{0}\) compared to LS in terms of human visual perspective. Figure 8: Sample recovered images from OFDM system over Rayleigh Fading channel. The top row of each \(\rm E_{b}/N_{0}\) represents received images from system using LS channel estimation, whereas the bottom row images are from systems using MMSE channel estimation.
## 3 Results and Discussion
### Sulfur Analysis
#### 3.1.4 Root Mean Square Error (RSME) Average  
The calculated RMSEC and RMSECV averages for both the original data and the SG smoothing-processed data are indicated in Table 3. These values are used for comparisons between properties of unequal size. The values in this study can be compared with the results of previous studies [4; 5]. The RMSEC average for the original data was calculated at 7.6019%. For the RMSECV average value, a slightly higher value was produced, at 9.1453%. In the case of SG smoothing-processed data, improved values were calculated. The RMSEC average in this case was 7.5097% and the RMSECV average was 9.0301%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Measured Value** & **Predicted Value** & **Absolute Error** & **Relative Error** \\
**Number** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline UO1 & 0.880 & 0.8878 & 0.0078 & 0.8864 \\ UO2 & 0.880 & 0.9798 & 0.0998 & 11.3409 \\ UB1 & 0.985 & 0.8851 & 0.0999 & 10.1421 \\ UB2 & 1.090 & 1.2115 & 0.1215 & 11.1468 \\ UB3 & 1.195 & 1.3009 & 0.1059 & 8.8619 \\ UB4 & 1.300 & 1.4492 & 0.1492 & 11.4769 \\ UB5 & 1.405 & 1.2706 & 0.1344 & 9.5658 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction results of sulfur analysis for the unknown samples.  
Figure 8: Prediction plot of sulfur analysis for the unknown samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Data Process** & **RMSEC Average (\%)** & **RMSECV Average (\%)** \\ \hline \multirow{2}{*}{Sulfur concentration} & Original & 7.6019 & 9.1453 \\  & SG smoothing & 7.5097 & 9.0301 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The RMSE average values of the sulfur analysis for the original and SG smoothing processed data.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation  
In this section, we discuss the experiments we conducted using the millimeter-wave (mmWave) model in the network simulator ns-3 [79; 80] to evaluate the performance of the proposed DT in predicting the optimal parametric settings of sensors' transport protocols over 5G IoT. In the evaluation of the improved SCTP IMP-SCTP versus ORG-SCTP, we specifically focused on two parts, namely, the setup for machine learning and the simulation results. We also compared the findings with those of state-of-the-art DTs and predictions of optimal sensors' SCTP congestion control mechanisms over 5G IoT. We conducted a paired-sample \(t\)-test to show the significant variance between standard mechanism results and those of improved congestion control approaches.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 2 The Improved Model  
For a better understanding of the improved model for clock prediction, existing methods are introduced first. Then the improved model is presented as a combination of the SA model and the GRNN in detail.
## 4 Theoretical Background and Simulations
### Monte Carlo Simulation
As a quick remedy, the theoretically calculated diffuse reflectance has been exponentiated by a value \(s\) such that the absorption coefficient yields comparable results to the Monte Carlo simulation, i.e., instead of comparing \(R_{\infty}^{\text{sim}}\) with \(R_{\infty}^{\text{exp}}\), the final step matches \((R_{\infty}^{\text{sim}})^{s}\) to experimental data. This can be understood by the total diffuse reflectance being composed of directly backscattered radiation and the forward scattered fraction that is then backscattered by additional particles (naturally, their scattering wave can be re-scattered by further particles, etc., yielding a plethora of higher-order terms) [164]. It should be highlighted that \(s\) has no physical relevance except for empirically correcting ab initio values to those from literature and Monte Carlo simulation to account for multiple scattering. The best agreement between both simulations has been achieved for a wavelength-independent correction exponent \(s=2.5\), thus establishing a coarse prediction of \(R_{\infty}\) while including dependent scattering. The latter is naturally accounted for in the Monte Carlo simulation which paints a more accurate picture of \(\alpha\) at the cost of severely increased processing times and demands. In this regard, each method offers distinct advantages: Whereas ab initio methods can be used for attaining a quick overview of the optical properties of the investigated material, a more thorough and quantitative analysis can be performed via Monte Carlo simulation. While additional rays increase the accuracy of the Monte Carlo simulation, the overall result is not significantly affected. Diffuse reflectances generated from 25 million and 1 billion rays are largely identical and differ by less than 0.1%. Hence, a large number of data points may be generated with a relatively low number of rays while maintaining an accurate estimate, thereby greatly speeding up the process of predicting absorptions from a given diffuse reflectance spectrum or, vice versa, calculate the diffuse reflectance properties from a material with known bulk parameters.
## 2 Experimental Section  
### Image Acquisition  
Two healthy human subjects with no known cerebral vascular disease were recruited and underwent MR imaging studies on a General Electric 3T MR750 scanner using a 32 channel phased array coil (Nova Medical, Inc., Wilmington, MA, USA). MR imaging studies were acquired under Institutional Review Board approval. For MR angiography (MRA), a 3D time-of-flight pulse sequence was used with the following parameters: TR = 26 ms, TE = 3.4 ms, NEX = 1, Flip Angle = 18\({}^{\circ}\), acceleration factor = 2, number of slab = 4, magnetization transfer = on, matrix size = 512 \(\times\) 512 \(\times\) 408, voxel size = 0.39 \(\times\) 0.3 mm\({}^{3}\). MR venography (MRV) was performed using a 2D INHANCE pulse sequence with the imaging protocol: TR = 18.5 ms, TE = 5.65 ms, NEX = 1, Flip Angle = 8\({}^{\circ}\), matrix size = 512 \(\times\) 248 \(\times\) 512, voxel size = 0.47 \(\times\) 1.6 \(\times\) 0.47 mm\({}^{3}\). T1 was conducted with a 3D Axial SPGR sequence with the following parameters: TR = 13.4 ms, TE = 4.23 ms, NEX = 1, Echo Train Length = 1; Flip Angle = 25\({}^{\circ}\), matrix size = 512 \(\times\) 512 \(\times\) 120, voxel size = 0.43 \(\times\) 0.43 \(\times\) 1.5 mm\({}^{3}\). T2 was conducted with an Axial T2 Propeller sequence with the following parameters: TR = 1176 ms, TE = 100.04 ms, NEX = 2, Echo Train Length = 26; Flip Angle = 142\({}^{\circ}\), matrix size = 512 \(\times\) 512 \(\times\) 100, voxel size = 0.43 \(\times\) 0.43 \(\times\) 1.5 mm\({}^{3}\).  
T1 provides contrast for the grey and white matter, skull, and scalp. T2 provided the cerebrospinal fluid space. The MRA and MRV captured major branches of the cerebral arterial and venous systems. This allows us to reconstruct the arterial and venous networks, grey and white matter surface, skull and scalp, and cerebrospinal fluid space for each subject. Automatic rigid coregistration of the MRA, MRV, T1, and T2 using one plus one evolutionary optimizer and Mattes mutual information metrics was performed to compensate for the different resolution settings and motion artifact. The image processing pipeline is illustrated in Figure 1 and discussed in details below.
# A New Multi-Axial Flux Pm Motor-Generator System for Flywheel Energy Storage
## 5 Conclusions  
The flywheel designed in the study provides a significant advantage by providing energy in under 1 sec compared to traditional flywheels with their winding structure. Designed for CML, the traditional flywheel has a single-coil structure. This winding design is used both as a motor winding and as a generator winding. In designs where the motor and generator windings are used together, the discharge lasts for two minutes. Consequently, the discharge times of less than 2 s required for the coil launcher cannot be achieved. Therefore, it is not possible for the flywheel to be ready in under two seconds and provide high energy to the CML in less than 1 s. To eliminate this disadvantage, a motor and a motor-generator set were designed on the same shaft for the high energy capacity required by the CML in in under two seconds. While the multipole motor-generator set works to overcome the moment of inertia at low speed in motor mode, the 2-pole motor ensures that the flywheel reaches high speed in under two seconds. In addition, a third winding designed additionally on the multi-pole motor winding is used in order to meet the required energy need in under two seconds.  
When the experimental results obtained are examined, regenerative power can be obtained by connecting the A-B windings in series and parallel. In the double-winding structure of the motor-generator set used in the traditional flywheel design, a maximum power output of 474.829 watts was achieved at 460 rpm. The emf generated at the 1188 rpm reference speed of the flywheel is five times higher than that of the C winding, owing to the number of turns in the A and B windings. Since the emf determines the discharge current flowing through the winding, it must be appropriate for the cross-section of the winding conductor. However, since the A-B windings are designed in accordance with the motor operating mode, the winding conductor cross-sections exceed their energy-carrying capacity in regenerative mode at high speeds. Therefore, it is not possible to obtain high current values in under two seconds with the regenerative mode, considering the conductor's current carrying capacity. However, the insulation on the windings is damaged.  
In the study, high discharge currents at high speeds (38.4 A at 1188 rpm) were obtained with the C winding designed with a winding cross-section of \(8\times 0.5\,\mathrm{mm}\). Thus, a new winding design and motor-generator set that reaches the reference speed in under two seconds and provides high regenerative energy has been verified with experimental data.  
However, the new winding design offers a more compact structure. Based on the results of this study, mechanical improvements and new methods should be developed to enhance the reference speed in future research, thereby increasing the discharge of currents. Additionally, improving the efficiency of the motor-generator set used in the flywheel by examining solid and iron losses is an important area of study.
## 6 Results, Analysis and Discussion
### Analysis Spatial Distribution of Performance Metrics
#### 6.2.2 CSMA Retries with 2D Representation  
Figure 8 shows the average CSMA retries when there is no jamming and reactive jamming cases when the jammer node is near, in the middle and far with regard to the coordinator. Note that the no jamming column for AODV has a maximum of 3.5 average CSMA retries for the closest nodes to the coordinator, while DSR has an intermediate value between three and 3.5, and MPH a value of three. We remark that as nodes move away from the coordinator, the CSMA retries are fewer, because these nodes forward less traffic, and therefore, the channel is less congested for communication. MPH reaches a value of 1.5 CSMA retries to the farthest nodes from the coordinator, while DSR has a value of two and AODV a value between two and 2.5. In the near column, we clearly observe that AODV has the highest difference (about one) for the nearest nodes to the coordinator. DSR continues with a difference value between 0.5 and one for these nodes and, finally, MPH with a value of 0.5. We note that MPH has the smallest change for the farthest nodes from the coordinator, showing that collisions caused by the jammer node do not affect a more extensive area in a large scale, as in the AODV and DSR cases. With respect to the middle column, the biggest change is observed for the AODV protocol, with values between one and 1.5 for about half of the nodes (the farthest part from the coordinator). DSR has a slight change between 0.5 and one for about the same half of nodes in the topology. While MPH has no abrupt changes in network nodes, it has only a few nodes with values between 0.5 and one around the central part of the topology (just around the jammer node position). The far column presents a similar behavior for the three protocols; however, MPH is the one with the least change (values between 1.5 and two) in the closest nodes with respect to the jammer node, and it is important to remark that although AODV presents the same values, MPH covers fewer nodes than AODV with these values.  
Figure 7: 2D representation of the average retransmissions in the no jamming case and the difference between the values in the reactive and no jamming cases for each of the positions of the jammer node.
## 3 Results
### Data Extraction  
In the data extraction process, all 39 articles were gathered under the same techniques used. Table 1 shows the nine techniques that can be used to measure the residual lower limb volume fluctuation. These techniques include water displacement, anthropometric measurement, contact probes, optical scanning, spiral X-ray computed tomography (SXCT), magnetic resonance imaging (MRI), ultrasound, laser scanning, and bioimpedance. Of the nine techniques, the highest number of publications is focused on the bioimpedance technique (n = 15) followed by laser scanning (n = 9), optical scanning (n = 9), water displacement (n = 8), anthropometric measurement (n = 5), contact probes (n = 3), ultrasound and SXCT (n = 2), and the lowest number of publications is focused on MRI (n = 1).