esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 3 Architecture's Proposed Approach
### System-Supporting Architecture
#### 3.2.3 Extended Reality Manager (XRM)  
Finally, the XRM is the entity that manages the interaction and building of XR services. The XRM modules are described next:  
* Service Controller: The controller is responsible for the XR service deployment (including the extended data integration and service building). This module can be approached with several techniques. Network Function Virtualization (NFV) is a suitable technology for deploying the different XR services; then, technologies such as OpenMANO, OpenNFV, OpenStack or even proprietary solutions can be adopted. This results in it being necessary to additionally design this module with AT techniques in order to determine, according to the user's request, if the requested service is one of a reading or writing nature. A reading service just consults the network model information; for instance, visualization services correspond to those of a reading nature. On the other hand, services that modify the network model, such as resource re-allocation, smart routing tables, etc., correspond to writing services. The objective of classifying the services aims to forward the appropriate request to the DTM Modeler along with the \"extended Network Model\". This block needs to interact directly with the DTM Modeler in two iterations: in the first iteration, the XRM Service Controller requests from the DTM Modeler the network model information (stored in the DTM Model Storage), and once the network model information is retrieved, the XRM Service Controller has the responsibility of processing the network data and integrating them with extended data; on the other hand, for the second iteration, the XRM Service Controller forwards the \"Extended Network Model\" along with the request nature (reading or writing) to the DTM Modeler. Moreover, this module has to manage the XR service with the objective of guaranteeing an efficient service interaction. In order to manage the XR service, different criteria can be followed; for instance, service nature (reading or writing), user role priority (super users or network administrators have more priority than clients), latency and response time (XR services that require low latency and response time should have a higher priority), resource availability (XR services must be deployed according to the current resources utilization), etc.
* Service Interface: This has the function of serving as an input/output user interface. The Service Interface can be realized as a set of interfaces including: Human-Machine Interfaces (HMIs), glazes, touch sensors and voice commands, among others. Subsequently, this block has to process the input/output information. The service interface receives the network information for the corresponding user visualization from the DTM Network Builder. Moreover, this interface has to be designed with a standardized platform to receive XR service creation requests, and must be flexible in handling any type of information exchanged, as an information-agnostic interface.
# Powder Bed Selective Laser Processing of Alumina: Scanning Strategies Investigation
## 2 Methodology
### Experimental Procedures
#### 2.1.2 Sample Preparation and Characterization  
The Laser Beam-Powder Bed Fusion (LB-PBF) 125 printer manufactured by RENISHAW(r) and equipped with fiber laser (with a wavelength of 1070 nm, maximum power of 200 W, and a variable spot size) was used to print the alumina samples. The laser spot size of 70 um and layer thickness of 100 um were selected for all samples. Six scanning strategies were investigated in this study, as illustrated in Figure 3. An alumina-based material was used as a base plate. Cubic alumina samples (10 \(\times\) 10 \(\times\) 10 mm\({}^{3}\)) were printed to investigate the effect of scanning strategies.
## 3 Results and Discussion
### Basic Safety Properties
When the temperature drops for the He-CAP, a correlating drop in leakage current is observed in Figure 7c as described previously for the voltage variation (Figure 7a). For the Ar-CAP, however, a constant leakage current is measured over the whole investigated range of 20 mm with a filament reaching the surface continuously. A constant leakage value over the whole range is not reported in literature yet and could neutralize distance uncertainties in applications caused by user errors or complex geometries. The correlation of He-CAP and Ar-CAP operation at different applied voltage amplitudes and ROS and RNS density production is presented in Figure 8 with ozone and nitrogen oxides as semi stable products from the chemistry between plasma and neutral gas [74]. For Ar-CAP, the density is exponentially increasing with the increased voltage and hence scales with the dissipated power. He-CAP, on the other hand, is not affected by the increased applied voltage or the correlating increase in dissipated power. The measurements for He-CAP were within range of the environmental offset and hence the species density could not be detected. A further investigation considered the ROS and RNS species density over the distance to the device. For He-CAP, the densities remained below the detection limit and the results are hence not shown. For Ar-CAP, a strong decrease over distance with a local maximum at about 20 cm is measured and in accordance with previous measurements before the values drop below the thresholds [46; 50]. For both cases, He-CAP and Ar-CAP, the ROS and RNS content will be further evaluated qualitatively with OES in the next chapter.
## 3 Thermal Aspects for Improving Energy Performance
### Maintenance of the Greenhouse Environment
#### 3.1.1 Temperature Maintenance  
Temperature regulation in greenhouses is a fundamental element in agronomy and plant science, playing a crucial role in controlling physiological processes essential for plant growth and development. Adequate temperature is vital to optimize photosynthesis, a fundamental biological process where plants convert Carbon Dioxide (CO2), Water (\(H_{2}O\)) and Light into Oxygen (O2) and Glucose (C6H12O6), as represented by the Equation (1). This process occurs mainly in leaves, where chlorophyll and other photosynthetic pigments capture light energy [112].  
\[6 CO_{2}+12H_{2}O+\text{LUZ}\longrightarrow C_{6}H_{12}O_{6}+6O_{2}+6H_{2}O. \tag{1}\]  
Specific temperature ranges promote maximum photosynthetic efficiency, while temperatures outside these ranges can limit the ability of plants to carry out this process effectively, thus affecting the production of their own energy and food [112; 113; 114].  
In addition to photosynthesis, temperature directly influences the rate of transpiration, essential for thermal regulation, nutrient absorption and maintenance of cell turgor. Optimal temperatures ensure a balanced transpiration rate, crucial to the plant's overall health. However, temperature extremes can lead to imbalances, resulting in water stress or reduced photosynthetic activity, and, consequently, negatively impacting plant growth and productivity [115].  
In this way, the integration of strategies for effective temperature regulation in greenhouses emerges as an essential agronomic practice, aimed at maximizing the efficiency of photosynthesis and ensuring an optimal transpiration rate, contributing to sustainability and increased agricultural production.  
Temperature also influences other aspects of plant development, such as seed germination, root growth, flowering and fruit ripening. Each phase of the plant's life cycle may have specific temperature requirements to optimize development [116].  
To maintain temperatures within ideal ranges, greenhouses employ heating and cooling systems, such as gas, electric, or oil heaters, and water misting or natural ventilation systems. The implementation of automated control technology allows continuous temperature adjustments, ensuring optimal conditions for production [117].  
Effective temperature management in greenhouses is, therefore, a critical scientific practice that integrates knowledge in plant physiology, engineering and monitoring technology. This multidisciplinary approach is essential to maximize the efficiency of agricultural production, contributing to global food security in the context of climate change and increasing demand for water resources.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 3 Methodology
### Method 2: Remote Online Measurement
The biggest limitation is that, because GPS signals cannot be received indoors, this method can only be used for outdoor measurements.
## 2 General Properties of Method
### Multilateration Method
 \tag{13}\]  
The general solution can be obtained by writing Equation (13) as a matrix equation:  
\[\mathbf{A}\mathbf{x}=\mathbf{b} \tag{14}\]  
with \(\mathbf{A}\in\mathbb{R}^{(N-1)\times 4}\), \(\mathbf{b}\in\mathbb{R}^{(N-1)}\) and \(\mathbf{x}\in\mathbb{R}^{4}\). The \((i-1)\)-th rows of the matrices \(\mathbf{A}\) and \(\mathbf{b}\), for \(i=2,3,\ldots,N\):  
\[\mathbf{a_{i-1}}=\left[2(\mathbf{p_{i}}-\mathbf{p_{1}})^{T}\mathbf{2}v_{s}\tau_{i1}\right], \mathbf{b_{i-1}}=\left[\mathbf{p_{i}^{T}}\mathbf{p_{i}}-\mathbf{p_{1}^{T}}\mathbf{p_{1}}-\mathbf{v_{s}^ {2}}\tau_{i1}^{2}\right]\]  
When a sufficient number of microphone measurements are taken (i.e., \(A^{T}\mathbf{A}\) is a full-rank matrix), the unknown \(\mathbf{x}\) solution can be obtained using the LSE method [30]:  
\[ \begin{bmatrix}\mathbf{p_{k}}\\ d_{1}\end{bmatrix} =\left(A^{T}\mathbf{A}\right)^{-1}\mathbf{A}^{T}\mathbf{b} \tag{15}\]When Equation (13) is examined, it is evident that the matrix \(A\) and the vector \(b\) are dependent on the TDoA values. Therefore, the accuracy of the method in producing results close to the true location depends on the correct determination of the TDoA values.
## 4 Quantitative descriptions of scale threshold and scale domain
### Wavelet transform method (WTM)  
The wavelet transform method, which is a relatively new mathematical technique, has already been widely used in various disciplines. It uses a localized function in time or space. The wavelet's size can be adjusted and shifted to analyze a data set. Thus, we can investigate features of interest in the data set at an appropriate scale, for example, broad features at a large scale and fine features at a small scale. With the help of the wavelet transform method, we can find where changes in a data set take place and simultaneously measure how large these changes are. [PERSON] [60] suggested that the wavelet variance calculated by the wavelet transform method is a natural tool to investigate the spatial scales of variability in remote sensing data. It can be used as an indicator to quantify the length scale of land surface. Through the analysis of simulating images, the hypothesis is confirmed that the dominant length scales in the landscape may correspond to the scale with the highest wavelet variance [16]. The best wavelet to identify length scales is the Haar wavelet. The results indicate that the wavelet transform method is also a suitable tool to analyze the heterogeneity of land surface and infer the optimum scale under which the main variability presented in the image may be lost. Apparently, GVM and WTM are different methods. One starts from the space domain, while the other starts from the frequency domain. However, these two methods will produce the same conclusion through several data simulations. It may be due to the fact that the cumulative variance and covariance are equal to the variance and covariance within the specific scale in the case of the Haar wavelet. Wavelet analysis would be a good tool to study multi-scale relationships of spatial pattern and heterogeneity [10]. However, the manner of WTM is also dependent on the mother wavelet. WTM's potential for using other wavelets, such as Daubechies, Coiflet and so no, needs to be further explored.
## 4 Numerical Simulation  
In order to verify the correctness and applicability of the proposed reliability assessment method considering fuzzy detection information and member failure credibility, a typical expressway bridge was selected as the research object for numerical simulation analysis. The bridge is a single span-reinforced concrete hollow slab beam, which is composed of five hollow slabs. The total length of the bridge is 10.0 m and the total width of the bridge deck is 7.5 m. The main beam is constructed with concrete of grade C50, reinforced with HRB335 and R235 steel bars. The material properties of the bridge, as well as the statistical parameters and probability distribution types of random variables of main slab resistance, dead load effect, and live load effect, are the same as those of reference [47]. The profile diagram of the bridge and the location of hinges are shown in Figure 2.
## 7 Conclusions  
A robust distributed LFC design using both CDM and LQG has been proposed for an interconnected power system and has been presented in this paper. A three-area power system is used to check the effectiveness of all of the proposed control strategies, the conventional integrator and CDM alone, under load change and parameter change cases.  
From the results, it was shown that the power system with both CDM + LQG and CDM controllers is robust in face of load change and parameter perturbation, and the system has a more desirable performance as compared to the classical integral controller design. Also, it is indicated that the proposed CDM + LQG can give a desirable and smooth response.  
Figure 8: Estimated and actual \(\Delta P_{m1}\), \(\Delta P_{g1}\).  
Figure 7: System response to the second case: (**a**) frequency deviations; (**b**) tie-line powers. CDM + LQG (solid line), CDM (dashed line), and conventional integrator (solid and thin line).
# Europium, Yttrium, and Indium Recovery from Electronic Wastes
## 3 Results and Discussion
### Europium and Yttrium Recovery from Cathode Ray Tubes
When increasing the concentration of HNO\({}_{3}\), the removal of europium increases. At a concentration of 120 g/L, a removal of europium of 95% is reached with a loss of yttrium in the organic phase of 17%. Then, the yttrium that was contained in the organic phase, which was previously purified, was re-extracted into a new aqueous phase. So, solutions of different concentrations of hydrochloric acid were used in order to analyze the influence of the concentration of acid in the percentage of yttrium re-extraction. In Figure 4b, it can be observed how the re-extraction of yttrium varies with increasing HCl concentrations from 50 g/L to 300 g/L. Yttrium recovery of 95% is obtained with an HCl concentration of 300 g/L. Lower concentrations of HCl do not allow acceptable yttrium recoveries toward a new aqueous phase. Finally, the yttrium was precipitated from the aqueous phase with the addition of oxalic acid at a pH of 1.5. The pH was regulated with the addition of sodium hydroxide, and the amount of oxalic acid was four times higher than the stoichiometric value. With these conditions, a 99% precipitation of the yttrium contained in the purified aqueous phase was achieved. Subsequently, the product precipitated in the form of yttrium oxalate was calcined for 2 h at 600 \({}^{\circ}\)C to transform it to yttrium oxide with a purity of 68% [29]. The recovery of yttrium oxalate that was acomplished during the precipitation stage is very similar to those that have been carried out in other works [27]. Nevertheless, in contrast with other works, solvent extraction and srcubbing stages were implemented in order to separate yttrium from other impurities. In addition, a calcination step is essential for transforming yttrium oxalate to yttrium oxide, which is a commercial salt. It should be noted that this product can be sold commercially (see Table S1, Figures S2-S4). In Figure 5, a process flow diagram for yttrium recovery is proposed. The best conditions that were found in this research were employed in the design of the process flow diagram. As other publications, this process incorporates a complete study of all of the parameters related to leaching with acids. Pulp density attracted special attention, since the more CRT powder that is treated, the more profitable the extraction of yttrium. Nevertheless, in the present proposed process, unlike other research studies [27; 28], a precipitation step is suggested for yttrium recovery as an oxide, since this salt has a high commercial value. Figure 4: (a) Removal of europium with HNO\({}_{3}\); (b) Re-extraction of yttrium with HCl.
## 5 Patents  
The refined Merox process of Equations (5) and (6) for the preparation of DMDS is our patented technology (CN 105924372 B), and the synthetic method of the supported AC-CoPcS catalyst has already applied for a Chinese patent (CN 106984361 A). No person or organization is allowed to use these technologies for commercial purposes unless admitted by Chongqing Unis Chemical Company Ltd. (CUC).  
Figure 12: The effect of reuse times of commercial AC-CoPcS catalyst on \(\mathit{CPP_{\mathrm{SMM}}}\), \(\mathit{Yield_{\mathrm{DMDS}}}\), and \(\mathit{Purity_{\mathrm{DMDS}}}\). Experimental conditions: SMM (18.0 wt%) 450 g, \(RR\) = 600 r/min, \(C_{\mathrm{ca}}\) = 1333.3 ppm, \(t\) = 60 min, \(T_{\mathrm{re}}\) = 65 \({}^{\circ}\)C, \(P\)(O\({}_{2}\)) = 0.9 MPa.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{\begin{tabular}{c} **Synthetic** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{\(\mathit{CPP_{\mathrm{SMM}}}\)} & \multirow{2}{*}{\(\mathit{Yield_{\mathrm{DMDS}}}\)} & \multirow{2}{*}{\(\mathit{Purity_{\mathrm{DMDS}}}\)} & \multicolumn{2}{c}{**Optimum Operation Parameters**} & \multirow{2}{*}{\begin{tabular}{c} **Proper** \\ **Reuse Times** \\ \end{tabular} } \\ \cline{5-10}  & & & & \(t\) & \(C_{\mathrm{ca}}\) & & \(T_{\mathrm{re}}\) & \(P\)(O\({}_{2}\)) & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **AC-CoPcS** \\ **(new)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **chemical** \\ **grafting** \\ \end{tabular} } & \multirow{2}{*}{**98.70\%**} & \multirow{2}{*}{**86.8\%**} & \multirow{2}{*}{**99.8\%**} & \multirow{2}{*}{**60 min**} & \multirow{2}{*}{**888.9 ppm**} & \multirow{2}{*}{**65 \({}^{\circ}\)C**} & \multirow{2}{*}{**0.9 MPa**} & \multirow{2}{*}{**4**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **AC-CoPcS** \\ **(commercial)** \\ \end{tabular} } & \multirow{2}{*}{**physical**} & \multirow{2}{*}{**97.4\%**} & \multirow{2}{*}{**85.6\%**} & \multirow{2}{*}{**99.7\%**} & \multirow{2}{*}{**60 min**} & \multirow{2}{*}{**1333.3 ppm**} & \multirow{2}{*}{**65 \({}^{\circ}\)C**} & \multirow{2}{*}{**0.9 MPa**} & \multirow{2}{*}{**2**} \\ \cline{5-10}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance comparison between commercial and newly-prepared AC-CoPcS catalysts.  
**Author Contributions:** [PERSON] and [PERSON] designed the work and revised the initial draft of the paper; [PERSON] and [PERSON] conducted the experiment; [PERSON] wrote the initial draft of the paper; and [PERSON], [PERSON], and [PERSON] discussed data analysis and gave many constructive suggestions.  
**Funding:** This research was funded by the Postdoctoral Research Project of Chongqing (Xm2016063), the Research Project of Chongqing Unis Chemical Company Ltd. (2016Q28), the Foundation and Frontier Research Project of Chongqing (cstc2015 cyjA20005), and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1600927).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Theoretical Background  
The theoretical background of our investigation is collected here for the sake of completeness, even if several concepts are well-known among the celestial mechanics community and the treatment may be long. In this way, the interested researcher has all the necessary information to follow our approach and results, and to reproduce them, if needed. In the following, the Keplerian orbital elements will be referred to as: the semi-major axis \(a\), the eccentricity \(e\), the inclination \(i\), the argument of the perigee \(\omega\), the longitude of the ascending node \(\Omega\) and the mean anomaly \(M\). Moreover, the subscripts \(\oplus\), \(\mathcal{Q}\) and \(\odot\) will denote the parameters of the Earth, the Moon and the Sun, respectively. The satellite's elements will be denoted by no subscript.
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.6 Comparative Effectiveness  
Accuracy: Generally, models using LSTM and XGBoost significantly improve accuracy over traditional methods like SARIMA. For example, LSTM models reduce forecast errors by 15-20%, while XGBoost models improve accuracy by 8-10% [8; 14].  
Computation Time: XGBoost has proven to be faster in both training and prediction, making it highly useful for real-time forecasting applications [14]. Additionally, XGBoost'ssupport for parallel computing significantly shortens calculation times, enhancing its utility in dynamic environments.  
Complexity: On the other hand, support vector machines (SVMs) and random forest classifiers are comparatively easier to implement and interpret than LSTM networks, which are more computationally expensive. This was affirmed by [PERSON] [35], who reported that [PERSON] et al. [14] noted the complexity of the architecture and training of LSTM models, which require specialized knowledge and significant computational power.  
Robustness: Random forest models resist overfitting and perform exceptionally well when there are a large number of predictors involved, whereas LSTM networks excel when the dataset exhibits temporal dependencies. The ensemble nature of random forest models makes them highly resilient to changes in data.
###### Abstract
The results showed that the displacement control effect of the device is equivalent to that of LQR semi-active control, and the acceleration response control effect is much better than that of LQR semi-active control [9]. Most of the existing negative-stiffness devices provide negative stiffness at the preset displacement, which is difficult to control the displacement of the isolation layer under different ground motions and reduce the response of the superstructure. Based on past devices [6], a new type of adaptive variable-stiffness isolation device is illustrated in this paper. Different from other devices, it integrates the flexible track concept and the concept of negative-stiffness damping system into the variable-stiffness device, and a variable-stiffness isolation system is proposed in combination with lead rubber bearings so that the stiffness of the isolation layer presents different stiffness characteristics under different displacement states. Thus, it can provide positive stiffness to limit the displacement of the isolation layer under interference load, provide negative stiffness under different seismic forces, control the displacement of the isolation layer, and achieve effective isolation, so as to meet the design requirements of different levels of seismic performance. It does not only realize the effective isolation of long-period structures but also improves the disadvantages of the previous negative-stiffness devices, such as their complex structure and residual deformation. In addition, the track surface function or other parameters can be flexibly changed according to the design requirements to meet the isolation requirements under different conditions.