esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet Video Delivery Improved by Super-Resolution with GAN
## 2 Background and Related Work
### DNN Super-Resolution for Internet Video Delivery  
In [9], the authors addressed the challenges of Internet video delivery by running DNN-based super-resolution models on the client side. On the server side, videos were clustered based on their content and encoded at low resolution. In each cluster, a DNN-based super-resolution model was proposed and served along with each video. On the client side, the built DNN model super-resolves the delivered content and creates a streaming session at the expense of transmitting low-resolution content and running a DNN model. The principal assumption was that client devices have the resources to run a DNN model in a feasible time window.  
In [6], the authors considered a bitrate adaptive streaming session as the input of the DNN-based super-resolution model proposed in [9]. The rationale was to super-resolve low-resolution segments that arrive during the session and decrease the computing burden of super-resolving all video segments. The authors chose a state-of-the-art adaptive bitrate algorithm to control the quality of the streaming session. Experimental studies showed perceptual improvements using super-resolution models compared to streaming sessions performed without these models.  
These two papers super-resolved videos on the client side, in contrast to the approach taken herein. We use super-resolution models in the surrogate servers of a cloud-based video service, intending to overcome the computing deficit on the client side while, at the same time, reducing the traffic between video injection points and surrogate servers.
## 3 Results and Discussion
### FTIR Analysis  
The evolution of the functional groups during the combustion of the raw biomass fuels and the ashes produced at 600 \({}^{\circ}\)C was examined by employing a Fourier Transformation Infrared (FTIR) analysis. The characteristic peaks and functional groups are presented in Figure 3 and Table 3, respectively. According to the FTIR analysis, the major volatile components change in the spectra relates to the vibration generated by the O-H bonds (3000-3600 cm\({}^{-1}\)), C\(-\)H groups (2900-3000 cm\({}^{-1}\)), stretching vibration of C=C and C\(-\)O. These bands generally disappear during combustion as the significant share of organic compounds (i.e., cellulose, hemicellulose, and lignin) is decomposed as observed in in Figure 3 (bottom). The bands at approximately 1065, 972, and 1415 cm\({}^{-1}\) are corresponding to the stretching vibration of CO\({}_{3}\)\({}^{2-}\), which corresponds to the content of calcite (CaCO\({}_{3}\)) in the ashes. The positions of the bands agree with the reference calcite in Figure 3 (bottom). [PERSON] et al. [50] also detected the formation of CaCO\({}_{3}\) in the silica-rich ashes obtained from rice husk and rice straw using a diffractometry technique (i.e., quantitative Rietveld refinement of XRD data). They reported that the formation of this phase could result from a reaction between CaO in the ash and the ambient CO and CO\({}_{2}\) gaseous species after the combustion. It is probably because some of the ashes were regularly exposed to the ambient air for different analyses after production considering their production time. The infrared spectra at the ranges 400-600, 778-799, and 1035-1125 cm\({}^{-1}\) could be attributed to bending, stretching, and asymmetrical stretching vibration of Si-O-Si, which are in line with the literature reports and the detected bands (i.e., 797 and 1065 cm\({}^{-1}\)) in the reference silica sample (CWK Kostropur\({}^{\circledR}\) 021012). Typically, the asymmetrical stretchingvibration of Si-O-Si in the wavenumber region 1035-1125 cm\({}^{-1}\) obscures with several different bands such as the SO\({}_{4}\)\({}^{2-}\) bands in the region 1138-1155 cm\({}^{-1}\), R-OH groups in 1051 cm\({}^{-1}\), stretching band of aliphatic ether C-O and alcohol C-O in cellulose and hemicellulose in 1050 cm\({}^{-1}\), \(\beta\)-(1-4) glycosidic bonds in cellulose such as C-O-C stretching and pyranose ring skeleton vibration in the region 1159-1108 and 1027-1051 cm\({}^{-1}\)[72, 73, 74, 75, 76].  
Figure 3: The evolution of the main functional groups in raw biomass (**upper**) and ashes of 1 \(w/\
u\)% biomass fuels (**bottom**). Ashes were generated at 600 \({}^{\circ}\)C and compared with the FTIR of commercial calcite and silica powders.
## 2 Materials and Methods
### Data Collection
#### 2.5.3 Focus-Group Discussions  
Focus-group discussions were organized with 25 farmers who were grouped based on their geographical proximity or similar farming practices. Each group comprised 5 farmers. These discussions provided a platform for participants to share their experiences, exchange ideas, and discuss challenges and opportunities related to drought management in their cattle farming systems. The group dynamics encouraged rich discussions and the exploration of diverse perspectives. The duration of the focus group discussions also varied depending on the group size and the extent of the discussions. However, each group discussion did not exceed 45 min.  
The integration of quantitative and qualitative methods allowed for a comprehensive understanding of farmers' perceptions and willingness to tackle drought-related issues. The quantitative data from the questionnaire surveys provided statistical insights and allowed for generalizability to the larger population, while the qualitative data from the in-depth interviews and focus-group discussions offered detailed insights into farmers' experiences, beliefs, and contextual factors influencing their decision-making processes. The combined analysis of the quantitative and qualitative data provided a robust and triangulated understanding of the research topic, enhancing the validity and reliability of the findings. The integration of different data collection methods also facilitated a more comprehensive exploration of farmers' perspectives, allowing for a nuanced understanding of the complexities and nuances of their attitudes and motivations towards drought management in small-holder cattle production systems in the Eastern Cape Province.
## 4 Monitoring System Framework
### Overall Framework of the Monitoring System  
As the working face is advanced, FBG stress sensors, displacement sensors, roof separation sensors, hydraulic sensors and lateral pressure sensors are installed in the roadway every 50 m. The monitoring data are transmitted through the communication cable to a switch on the communication vehicle at the other end of the working face by an Ant Fiber Bragg Grating Demodulator, which transmits the data through an industrial ring network to the main machine on the well. Users in the mining area can access the data through the industrial ring network. In addition, open secure ports are available through an industrial ring server for internal and external network port mapping. Finally, the big data generated by the coal-free self-forming roadway construction method is deployed and released on the internet through the use of a cloud server. The schematic diagram of the system is shown in Figure 12.  
Figure 11: FBG lateral pressure sensor.
## 2 Materials and Methods
### Regulation Algorithm  
Aste SBS is dependent on the availability of sunshine, the system is further modified to enable it to serve its purpose even in periods when the sunlight is not sufficient. The modification will increase the reliability of the system as it will be able to preserve energy and operate normally during times when there is no sunshine. The improvement makes use of numerical mathematical models that are established to control the working of the model. The effectiveness of developing a mathematical model wasstested, and it was shown that an algorithm could be used to improve the reliability of the model.  
The model is designed to change the features of the water and air in the different chambers. By controlling the temperature of the water and air that enter and leave the towers, it is possible to recycle hot water and keep the temperature of the air and the water below that in the system, which preserves the overall energy usage. The result is that energy will be conserved. During times when there is no sunshine, the system can still work because there is some conserved energy. However, the system should have an alternative way because conserved energy cannot work for a prolonged time [19].  
The energy evolution and preservation process in the system is shown by the following equation:  
\[\frac{\partial T_{w(n+1)}}{\partial t}=\frac{1}{\delta}\Bigg{(}-(m_{w}\xi) \sum_{j=1}^{n+1}l_{(n+1)j}T_{wj}-m_{w}\xi l_{(n+1)0}T_{ww}-T_{w(n+1)}+\frac{ \tau\alpha I(t)}{U_{w}}\ \ +T_{amb}(t)\Bigg{)} \tag{17}\]  
where \(i=n+1\) at the water solar collector outlet.  
Figure 8: The input/output block diagram of the condensation tower.  
We set \(y=T_{w(n+1)}\). The adopted formula serves the purpose of forcing \(y\) to follow the set-point \(y_{e}\) for satisfying the desired dynamic. We consider the dynamics of a closed loop given by the following linear equation:  
\[\frac{dy}{\partial t}=\chi(y_{e}-y);\chi\geq 0 \tag{18}\]  
Using Equations (17) and (18), we deduce the following command equation:  
\[m_{w}=\frac{\frac{\tau\alpha t(t)}{\Omega_{w}}\ +T_{amb}(t)-y-\chi\delta(y_{e}-y)}{ \xi\Big{(}\sum_{j=1}^{n}l_{(n+1)j}T_{wj}+l_{(n+1)(n+1)}y+l_{(n+1)0}T_{ww}\Big{)}} \tag{19}\]
# Intercalating Sn/Fe Nanoparticles in Compact Carbon Monolith for Enhanced Lithium Ion Storage
## 1 Introduction
Unlike the priorly chronicled protocols which need both sophisticated nano-techniques and exquisite apparatus to assemble the SnO\({}_{2}\)/carbon nano-compounds, our currently demonstrated study merely deals with the maturely industrial procedures. By virtues of multicomponent synergy and compact secondary architecture, the as-synthesized samples feature several unique merits as follows: (1) the amorphous carbon works as a protective matrix to not only ensure the swift electron/ion mobility through the overall electrode but also accommodate the mechanic strain to hurdle the diffusion/aggregation of the active materials and disintegration of electrode. (2) Small-sized metal nanoparticles guarantee the boosted electrochemical reaction kinetics based on both the maximized interface boundary with the closely contacted carbon and short pathways for movement of Li\({}^{+}\) and electrons. (3) The non-porous and micro-sized compact structure gives the high tap density and avoids the morphological issues to enhance the structural stability and volumetric battery performance. (4) A combination of Fe\({}_{2}\)O\({}_{3}\) with SnO\({}_{2}\) can not only produce the closely spaced nanostructures with an internal potential for offering extra charge transfer driving force, quickening the reaction dynamics and the electron/ion migration across their boundary areas, but also promote the input/output of external charges outside SnO\({}_{2}\) due to the great electronic conductivity of Fe\({}_{2}\)O\({}_{3}\). (4) Ultra-small metal nanoparticles are firmly inlayed between carbon layers instead of simple physical stacking, which largely boosts overall structural durability of the electrode materials. As a novel configuration of the compact materials, the microstructure without the highly hierarchical porosity possesses the conductive protecting carbon sheath and no free void spaces, thus owing the greater flexibility in the tight calendaring processing of electrode, full usage of electroactive materials, relief of volume expansion, and mass fabrication. With such encouraging superiorities, this dense composite monolith can withstand a stress-induced volume variation of the SnO\({}_{2}\)-based nanoparticles to maintain the structural framework and thus endows the outstanding anodic performances for LIBs in terms of the stable cycle stability, good rate performance, and big specific capacity. This study develops a facile way to prepare the promising anode material for LIBs, and, at the same time, it will motivate the chemists to further explore other advanced SnO\({}_{2}\)-based LIB anodes.
## 3 Stress Estimation Based on the Monitored Strain
### Monitoring Information  
The strain of the PC beam in the case study bridge during the construction phase was monitored. The strain sensors were embedded in each span of the concrete beam before the concrete was poured. The concrete bridge was constructed by transforming a simply supported system into a continuous system. The data collection schedule progressed according to the construction stage, as shown in Table 2. The performance parameters of the sensors are shown in Table 3.  
The three spans were monitored within the range of the construction stages. The mid-span, 1/4 span, and end cross-sections were selected as the monitored cross-sections. The sensors were symmetrically arranged at the top flange, centroid, and bottom flange. A total of 48 monitoring points were arranged with six points for each cross-section. The selected cross-section and monitoring points are illustrated in Figure 2. For the sake of discussion, \(m-S_{jk}^{i}\) was used to number the sensors, where \(m\) represents the beam; the superscript \(i\) represents the cross-section and \(i=M\) (mid-span), \(Q\) (1/4 span), and \(E\) (girder end); the subscript \(j\) represents the position of the sensors along the height and \(j=t\) (top), \(c\) (center), and \(b\) (bottom); and the subscript \(k\) represents the position of the sensors along the span and \(k=l\) (left) or \(r\) (right).
## 3 Results and Discussion
### Exploitation of Electricity from a Hypothetical PV System  
The economic viability of an investment in a hypothetical PV system considers only the electricity produced by the PV system. Based on the methodology described in Section 2.3, we first estimate the amount of aluminium produced over the years, limiting the consumption of electricity produced to the expected demand of the large aluminium smelter. Figure 10a shows the projected aluminium production under the typical electricity consumption of the smelter for the case of the hypothetical 60 MWp PV system, while Figure 10b shows the aluminium production for the 90 MWp PV system. Notably, there is a marginal decline in production, which is attributed to the degradation of the solar modules over time.  
The economic aspect of our analysis, which depends on the aluminium price and aluminium production costs, is presented next. We analysed the impact of varying aluminium prices and aluminium production costs based on the values set out in Table 3 in the following five scenarios:  
1. Low price of aluminium + average alum. production costs;
2. Average price of aluminium + average alum. production costs;
3. High price of aluminium + average alum. production costs;
4. High price of aluminium + low alum. production costs
5. Low price of aluminium + high alum. production costs.  
For both PV system sizes, we computed the NPV across these scenarios, using the methodology detailed in Section 2.3, over a decade. The results of the economic viability calculation for the hypothetical 60 MWp PV system are shown in Figure 11a, while Figure 11b shows the results for the 90 MWp PV system. The IRR and PP for all five scenarios with 60 MWp and 90 MWp photovoltaic system capacities are shown in Table 4. The investment in both PV systems pays back within approximately 6 years in the best scenario. In scenarios with low aluminium prices, the investment does not pay back within 10 years. The IRR values reveal that only Scenarios 3 and 4 are financially viable, with IRRs exceeding 8%, particularly when aluminium prices are high and production costs are moderate to low. In contrast, Scenarios 1 and 5 show negative IRRs, indicating that the investment would be unprofitable under conditions of low aluminium prices. The trend of the NPV is higher for the 90 MWp PV system, primarily due to the increased electricity production, which allows the smelter to operate longer at 60 MW consumption using the surplus compared to the 60 MWp PV system.  
Figure 10: Annual aluminium production from the production of the hypothetical 60 MWp (**a**) and 90 MWp (**b**) PV systems.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 3 Method
### Meta-Classifier  
After a good embedding model is obtained, the classifier will be trained and tested using the extracted features. The classifiers used for classification tasks include nearest neighbor algorithm, Bayesian algorithm, logistic regression, and support vector machine. At present, the multinomial logistic regression model is used primarily for meta-classification. Logistic regression is a regression model that narrows the prediction range and limits the predicted value to the range [0, 1]. Multinomial logistic regression improves binary logistic regression that can be generalized to train and predict multiple classification problems. The algorithm trains a multinomial logistic regression model for the multi-classification problem, including M-1 binary regression models. Given a data point, M-1 models are run simultaneously, and the category with the highest probability is chosen as the prediction category.
## 4 Test Results and Analysis
### Significance Analysis  
SPSS software was used to test the significance of multivariate linear regression [35; 36]. Design statistical verification value is 0.05 \(<\) f \(<\) 0.10, and the analysis results are shown in Table 9.  
According to Table 9, the ratios of Portland cement/gypsum and filling material/bonding material were two important factors controlling sample density and porosity, and both were positively and negatively correlated. For compressive strength, the standardized coefficients of the four factors were similar, and their effects were positively correlated. The ratio of mica powder to standard sand had little effect on the elastic modulus, and the other three factors had similar effects on the elastic modulus. Through SPSS significance analysis, it was found that the calculation results were the same as those of factor sensitivity analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Mica Powder/Standard** & **Filling Material/Bonding** & **Portland** & \\  & **Sand** & **Material** & **Cement/Gypsum** & **Silicone Oil Ratio** \\ \hline
1 & 15.05 & 12.84 & 16.48 & 14.20 \\
2 & 15.10 & 13.44 & 16.36 & 15.51 \\
3 & 15.01 & 15.61 & 15.71 & 16.51 \\
4 & 13.73 & 16.31 & 14.13 & 14.42 \\
5 & 16.48 & 17.18 & 12.70 & 14.65 \\ \hline Range & 2.75 & 4.34 & 3.77 & 2.32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Range analysis of density %.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Mica powder/Standard** & **Filling Material/Bonding** & **Portland** & \\  & **Sand** & **Material** & **Cement/Gypsum** & **Silicone Oil Ratio** \\ \hline
1 & 0.78 & 1.39 & 0.55 & 0.79 \\
2 & 0.77 & 1.07 & 0.65 & 0.97 \\
3 & 0.91 & 0.98 & 0.89 & 0.90 \\
4 & 1.23 & 0.56 & 1.22 & 1.18 \\
5 & 1.16 & 0.85 & 1.53 & 1.02 \\ \hline Range & 0.45 & 0.83 & 0.98 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Range analysis of compressive strength MPa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Mica Powder/Standard** & **Filling Material/Bonding** & **Portland** & \\  & **Sand** & **Material** & **Cement/Gypsum** & **Silicone Oil Ratio** \\ \hline
1 & 0.3912 & 0.3961 & 0.3729 & 0.3823 \\
2 & 0.3824 & 0.4192 & 0.3713 & 0.3176 \\
3 & 0.4018 & 0.4038 & 0.4184 & 0.4830 \\
4 & 0.4011 & 0.3763 & 0.4012 & 0.4002 \\
5 & 0.4000 & 0.3807 & 0.4131 & 0.3877 \\ \hline Range & 0.0204 & 0.0431 & 0.0468 & 0.1650 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Range analysis of elastic modulus GPa.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 6 GNSS Meta-Signals  
The concept of _GNSS meta-signals_ was introduced for the first time in [31]. A GNSS meta-signal is the combination of two different GNSS signals transmitted at two different carrier frequencies which can be expressed as a single Alternate Binary Offset Carrier (AltBOC) modulated signal [32]. Based on that initial work, [PERSON] et al. [33] further discussed the fundamental concept of GNSS meta-signals, proving through analytical and practical implementations that, unlike other conventional methods, where GNSS receivers process multiple signals, the GNSS meta-signal method could improve the single-point ranging accuracy over that of the better of the two generating signals. Two different GNSS meta-signals have been proposed [31; 33] considering Galileo signals  
* Galileo E5a + E6-BC: AltBOC(50,10,5) [31; 33],
* Galileo E5b + E6-BC: AltBOC(35,10,5) [31; 33],  
In both works [31; 33], the GNSS meta-signal built from the combination of the Galileo E5b and Galileo E6-BC signals is shown to be especially interesting. This is because this specific GNSS meta-signal is centered at 1242.925 MHz which is exactly half frequency of the carrier frequency of a potential GNSS signal in the S-band [31].
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.3 Cost-Effectiveness and ROI  
AutoML offers significant cost-effectiveness and improved return on investment (ROI) by reducing the expenses associated with traditional machine learning processes and delivering faster, more accurate results. Developing machine learning models manually often requires substantial investment in specialized talent, computational resources, and time [56]. The iterative nature of model development, which involves experimenting with different algorithms, tuning hyperparameters, and validating results, can be costly and resource-intensive. AutoML mitigates these costs by automating much of the process, reducing the need for highly specialized personnel and extensive computational resources [57]. This automation enables businesses to deploy models more quickly, shortening the time-to-value and allowing for more rapid iteration and improvement.  
As a result, companies can achieve better outcomes with less financial investment, directly contributing to higher ROI [58]. In addition, AutoML's ability to optimize models for specific business objectives ensures that the models are accurate and aligned with the organization's goals, further enhancing their value. Streamlining the machine learning process and reducing the associated costs makes advanced analytics accessible to a broader range of businesses, including small and medium-sized enterprises that may not have the resources to invest in traditional data science teams [57; 59]. This democratization of machine learning contributes to a more level playing field, where businesses of all sizes can leverage data-driven insights to improve their operations and drive growth.