esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Configuration Optimization of
## 4 Solution to the Multi-Objective Optimization of Allocating HESS  
From the equations described above, it can be seen that the objective functions with technical and operational constraints are formulated. Appropriate optimization model considering multiple objectives of interest is essential for HESS configuration optimization. Such a model is worthless when it is optimized with an inaccurate optimization method. Traditionally, the multi-objective optimization problems (MOPs) have been solved by linear programming, and usually one of the objectives is optimized and the others are included in the restrictions or using fuzzy method and weighted aggregation approach where the MOP is converted into a mono-objective one. These methods simplify the optimization process of MORPO, but generate disadvantages [14; 15]:  
1. Representation of the objectives using restrictions in linear programming can lead to unfeasible problems.
2. There is not a clear criterion for choosing the suitable objective function, and in many cases the fulfilment of one single objective can be in conflict with others.
3. Fuzzy optimization turns out to be a weighted aggregation approach with a set of stationary weights (preference factors).
4. The weighted aggregation approach cannot accurately reflect the relationship between the various objectives, especially when the involved objectives are conflicted with each other.
5. The only one best solution fails to provide the designer with alternative options.  
Compared with mono-objective optimization (MOO) techniques, the main advantage of MOO is that a set of diverse optimal solutions are identified instead of one optimal solution, which gives more flexibility to the decision-maker. To offer a set of solutions to do tradeoff analysis and provide a deep insight into the multi-objective configuration optimization of HESS, effective MOO algorithms should be applied to this problem.
## 3 Link Efficiency in Zenith Paths
### Examples of Theoretical Application to Time Series \(A(t)\)  
To grasp the time evolution of the symbol rate during a rain attenuation event, Figures 4 and 5 show examples of \(A(t)\) and \(\gamma(t)\), Equation (10), simulated at Spino d'Adda and at Tampa, by adopting the mean efficiency (Table 2). Appendix A reports similar examples for the other sites.  
the large power margin required for a continuous transmission--up to the fantastic value 91.0 dB--in conventional design.  
Interestingly, in Figures 4 and 5, as well as in Appendix A, regardless of the site, the interval of the onset of a peak is smaller than that of its decay, i.e., the two time--constants are significantly diverse. For example, in Figure 4 (Spino d'Adda), \(A(t)\) increases from 13.2 dB to the peak 91.0 dB in 5 min, while it decreases to 14.5 dB in 9 min, i.e., with a rate of change \(+15.6\) dB per minute and \(-8.5\) dB per minute, respectively. Similar observations can be made for the evolution of the ratio \(\gamma(t)\). For Tampa, \(A(t)\) increases from 13.3 dB to the peak 98.9 dB in 5 min, while it decreases to 14.3 dB in 9 min, i.e., with a rate of change of \(+17.2\) dB per minute and \(-9.5\) dB per minute, respectively.  
In other words, the intense rainfall causing attenuation peaks fades away more slowly at its end than at its onset; therefore, the long time-scale (several minutes) positive rates  
Figure 4: Upper Panel: (**a**) Rain attenuation time series \(A(t)\): (**b**) zoom. Lower panel: (**a**) Symbol rate ratio \(\gamma(t)\); (**b**) zoom. Spino d’Adda.  
Figure 5: Upper Panel: (**a**) Rain attenuation time series \(A(t)\): (**b**) zoom. Lower panel: (**a**) Symbol rate ratio \(\gamma(t)\); (**b**) zoom. Tampa.  
of change of attenuation are larger than the negative ones. This behavior at the zenith paths is significantly different than that occurring at slant paths for which the magnitude of positive and negative rates of change is statistically identical, according to the experimental results [29; 30; 31; 32; 33] and modeling [34].  
In the next section, we consider an evolution of the method by assuming that it is applied in links in which a power margin is already implemented. We show that this fixed power margin is very small.
## 2 Related Work  
In [22], recognizing the fact that the maximal power path is not always the first path in channel impulse response (CIR), that is, there are paths with earlier arrival time but with weaker power, which results in ISI if the FFT window is not set ahead of them, it is suggested to bring forward the estimated time index by some amount. In order to eliminate the time synchronization offset, the key is to search for the FAP after capturing the PwMP. Existing methods can generally be classified into four categories [23]: Maximum Likelihood (ML) approaches [24], Matched Filter (MF)-based detectors [25; 26], energy-based detectors [27; 28; 29] and machine learning-based approaches [30; 31; 32].  
In [33], multipath components of measured waveforms are detected using a Maximum Likelihood (ML) detector. The ML estimator is theoretically asymptotically efficient, but its high computational complexity makes it challenging to implement. In [34], a three-step MF detection-based algorithm is proposed. First, the search region for FAP detection is determined; then, a rough detection of FAP is made by a threshold that is dynamically set using the MF output; last, a refined search process is carried out to obtain the precise location of the FAP. However, MF cannot perform satisfactorily in the low signal-noise ratio (SNR) region. In [28], to estimate the time of arrival in impulse radio ultra wideband ranging under harsh environments, the rank test is adopted in the study to detect the FAP in a dense multipath with energy detection receiver. In [35], the machine learning-based approaches are used to identify LoS and NLoS. They generally consist of data collection, feature extraction, model training and regression, which may have extremely high computational demand when the training data size becomes large. However, none of these methods can guarantee the accurate search of the first path; that is, they cannot completely eliminate the synchronization offset. Moreover, these methods add complexity to the system. Since LOS communication does not require searching FAP, computing resources will be wasted to be compatible with LOS communication.  
In addition, to realize the communication in the NLOS scenario, UAV relay technology [36; 37] and reconfigurable intelligent surfaces (RIS) technology [38; 39] are also adopted. These two technologies require additional hardware to implement and are therefore not considered.
# The Use of Augmented Reality for the Management of Equipment Ageing with a Virtual Sensor
## 4 Results
### Interface Development
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Button (or Text Box)**} & \multicolumn{1}{c}{**Functionality Description**} \\ \hline Transfer to App Mobile & Output migration (together with the 3D model of the equipment) from the App Desktop to the mobile device. \\ Help & To provide explanation about how to use the app. \\ Upload fishbone modules & Uploading fishbone modules of the equipment. These modules must be uploaded in chronological order. \\ Upload sampled points (inspection & Uploading thickness measurements carried out on the equipment associated with the relative spatial coordinates. Files must be uploaded in chronological order. \\ Create QR code & Generation of the QR code of the equipment. \\ Compute (on the GUI’s centre) & Calculation of the current condition of the equipment. \\ Compute (on the GUI’s right) & Calculate future condition of the equipment. \\ Reset & To remove data from all fields. \\ Entering ID equipment & To enter the name of the equipment being analysed (this text box must always be filled in). \\ Year for data estimation & To enter the years for the prediction of the equipment condition. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Functionality of the buttons included in the App Desktop. Figure 7: GUI of the (**a**) App Desktop (**b**) and App Mobile. following the physical movement of the inspector, the exploration of the equipment in a fluid and immersive way. The buttons, on the other hand, permit the selection of the year for which the inspector wants to know the ageing metrics and the corrosion map, to make the equipment parts transparent (those not analysed), and finally, to choose the graph to be visualised. After the improvement of the GUI, the virtual sensor was tested again by the same users. The data migration step was automatised and the elements of the App Mobile were reduced in number and dimensions (according to the users' suggestions). The users found the new version very user-friendly. The issue related to the use of a mobile device to give instructions to the virtual sensor is still not solved because this requires the introduction of other technologies or a further improvement of the software. (i.e., the finger on the screen of the mobile device should leave a trace in such a way that the inspector, while looking at the smart glasses, is aware about the position of his/her finger on the screen). Voice command could be another potential way to manage the graphical interface. The use of vocal registrations could be another possible implementation to collect useful information on-field. The consideration above is not an obstacle to the adoption of the technology for the proposed scope, but it represents a potential future development of this research.
## 3 Results and Discussion
### Variation Effects of Mass Flow Rate of Heat Source on Economic Performance of ORC System  
To study the effect of mass flow rate of heat source on the economic performance of ORC system, for R123 system, the evaporation temperature was set at 150 \({}^{\circ}\)C, condensation temperature was set at 27 \({}^{\circ}\)C, and heat source temperature was set at 197 \({}^{\circ}\)C. For R245 fa system, the evaporation temperature was set at 127 \({}^{\circ}\)C, condensation temperature was set at 27 \({}^{\circ}\)C, and heat source temperature was set at 197 \({}^{\circ}\)C. Total cost, net earnings, payback period, return on investment, levelized energy cost, and present value of total profit in system service life of ORC system with different mass flow rates of heat source are depicted in Figures 20-25.  
Figure 21: Net earnings (NE) of ORC system with different mass flow rates of heat source.  
Figure 20: Total cost of ORC system with different mass flow rates of heat source.  
Figure 23: Return on Investment (ROI) of ORC system with different mass flow rates of heat source.  
Figure 24: Levelized Energy Cost (LEC) of ORC system with different mass flow rates of heat source.  
Figure 22: Payback Period (PP) of ORC system with different mass flow rates of heat source.  
From the above figures, it can be seen that, with the increase of mass flow rate of heat source, although the total cost of the system increases, the net earnings, return on investment, and present value of total profit in system service life increase. Meanwhile, the payback period and levelized energy cost decrease. Therefore, the economic performance of system becomes better and better with the increase of mass flow rate of heat source.
## 3 Results and Discussion
### The Effect of Relative Humidity on the Degradation Rate
\]  
Oxygen can be transformed into a superoxide anion (O\({}_{2}\)*\({}^{-}\)) by reacting with the photo-generated electrons on the surface of the catalyst, according to the following reaction [15]:  
\[\text{e}^{-}+\text{O}_{2}\rightarrow\text{O}_{2}^{\text{*}-}\]  
In the presence of water molecules, the superoxide anion (O\({}_{2}\)*\({}^{-}\)) can react with water molecules to generate OH*:  
\[2\text{O}_{2}^{\text{*}-}+2\text{H}_{2}\text{O}\to 2\text{OH}^{+}+ \text{O}_{2}\]  
Thus, increasing the amount of water can lead to an increase in the degradation rate due to the generation of more OH* radicals. However, excess water can negatively impact the conversion of pollutants, as water molecules can compete with organic compounds on TiO\({}_{2}\) adsorption sites. Comparing the degradation rate achieved in dry air (RH 5%) and wet air, it was observed that water has a beneficial effect. However, when the humidity level exceeded 55% RH, the adsorption constant significantly decreased, which has been supported by previous studies [28, 35, 40]. The value of the reaction constant can assume a minimum under dry air conditions and a maximum at about 55% relative humidity, and then decreases with increasing humidity. Under high-humidity conditions, the VOC must first penetrate the layers of  
Figure 4: The influence of humidity on the degradation rates of CB **(a)** and CN **(b)** (I = 20 W\(\cdot\)m\({}^{-2}\), T = 20 \({}^{\circ}\)C, Q = 1 m\({}^{3}\cdot\)h\({}^{-1}\)). water molecules before interacting with the catalyst surface or any radicals generated in the reactor. The lack of H\({}_{2}\)O molecules on the TiO\({}_{2}\) surface limits radical generation in a dry air stream (5%). The water molecule favors photocatalytic reactions under relatively humid conditions (5% to 55%). At a high humidity (80%), the mole fraction of water submerges the TiO\({}_{2}\) adsorption sites. Mass transfer on and/or into the water film on the TiO\({}_{2}\) surface dominates any potential photocatalytic reaction, as demonstrated by [41]. Therefore, the DR of the VOCs decreases regardless of the concentration of the VOCs in the medium. The work of [42] on treating trichloromethane by photocatalysis showed that photocatalytic degradation is generally affected by H\({}_{2}\)O molecules. In most cases, the complete absence of water vapor is fatal to photocatalysis because it directly affects the amount of OH* formed. In addition, a very high relative humidity value can negatively affect the process due to competition between the water and pollutants. [PERSON] and his co-workers [43] made the same remark in their study of the effect of relative humidity on the photocatalytic degradation mechanism of gaseous styrene.
## 2 System Model
It is worth noting that (6) is only valid when the ICI and the ISI occurring are entirely removed. From (5) and (6), it is evident that the post-equalization coefficients are degrading the performance of the system in terms of BER. Therefore, the prime objectives of the jointly designed PPE filter are minimal noise enhancement and BER improvements while restraining the transmitter's complexity. Hence, a few-tap pre-equalizer is recommended, which aims to improve the system performance by shifting a few equalization efforts from the receiver to the transmitter.
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 3 Results  
### Porous Monoliths Characterization  
The obtained porous monoliths were characterized by XRD analysis; the pattern is reported in Figure 3.  
The hydration of the blast furnace slag/high alumina cement mixture after 28 days curing does not form calcium aluminate hydrates, but only stratlingite Ca\({}_{2}\)Al(AlSi)O\({}_{2}\)(OH)\({}_{10}\). 2.25H\({}_{2}\)O [30] is visible, along with the remnants of the amorphous halo of the blast furnace slag.  
In a filtration process, i.e., the application of the synthesized monoliths, porosity is a key property for the success of the material when applied; therefore, porosity of the monoliths was determined by Hg intrusion (Figure 4).  
Figure 3: XRD of the alkali-activated powder (28 days of curing).  
A very complex pores distribution, which can be summarized in the average pore dimension of 0.14 \(\upmu\)m, was found, characterized by a wider pores distribution where very large and very small pores are present (Table 4).  
To better evaluate and confirm the morphology and elemental composition, SEM-EDX analysis was performed on the monoliths; results on the gold-coated samples are reported in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Morphological Characteristics**} \\ \hline Pore Volume = 0.46 cm\({}^{3}\)/g \\ Surface Area = 13 m\({}^{2}\)/g \\ Average Pores Diameters = 0.14 \(\upmu\)m \\ Bulk density = 1.1 g/mL \\ Porosity = 48.7\% \\ \hline \multicolumn{2}{c}{**Prepared Monoliths**} \\ \hline PGM 1 & Volume = 54.7 cm\({}^{3}\), weight = 29 g \\ PGM 2 & Volume = 87.4 cm\({}^{3}\), weight = 46 g \\ \hline \end{tabular}
\end{table}
Table 4: Morphology and size of the monoliths.  
Figure 4: Pore distribution by Hg intrusion of the alkali-activated monolith.  
Figure 5: SEM micrographs ((a–d), at increasing enlargements) and EDX analysis of the monolith (e).  
The complex porous structure, already observed by porosity measurements, was confirmed by SEM analysis. Very large cages were accompanied by smaller pores that are homogeneously distributed in the material, thus assuring similar filtration behavior and pathways in the whole monolith volume. Moreover, the observed morphological situation was a strong indication of the direct correlation between bubbles formation during synthesis and the final material porosity. EDX experiments confirmed that the constituent powder consisted mainly of Ca, Si, Mg, and Al; small amounts of Fe ions are due to the starting slag composition.  
For sake of clarity, monoliths characteristics are summarized in Table 4.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 2 Sensors and Monitoring Technologies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Sensor**} & \multicolumn{2}{c}{**Measurement Range/Accuracy**} & \multirow{2}{*}{**Response Time**} & \multirow{2}{*}{**Power Consumption**} & \multicolumn{2}{c}{**Applications/Technology**} & \multicolumn{2}{c}{**Suitability for both Indoor and Outdoor**} & \multirow{2}{*}{**Cost**} \\  & & & & & & & & **Environments** \\ \hline \multirow{3}{*}{Occupancy} & PIR & 3-10 m distance & 0.5\(\,\)\(\
# A Fast and Reliable Luma Control Scheme for High-Quality HDR/WCG Video
## 1 Introduction
Considering the pros and cons of these previous luma control schemes, this paper focuses on an interesting question: can we design a linear approximation of the nonlinear HDR EOTF which can provide a similar performance to that of the iterative luma control method while being free of the limiting factors in real-time or hardware implementation such as the adaptive selection of control parameters or the uneven amount of required computations? To answer this question, we first analyzed the errors involved in the closed form solution of [9], which derives an upper and a lower bound on the optimal luma value from the convexity property of the EOTF function. Then, we tried various linear approximations employing the derived bounds to design an efficient linear approximation of the nonlinear EOTF function. Based on the trials, we argue that the straight lines passing two points on the EOTF curve are quite useful, where one is the position of the original 4:4:4 signal and the other is the point somewhere between the derived lower and upper bounds. Via the modification of the closed form solution using these straight lines, we show that the proposed scheme can provide nearly the same quality of the iterative solution without any limiting factors in its real-time or hardware implementation. The rest of this paper is organized as follows. Section 2 describes the problem of luma control and the approaches taken by the previous representative algorithms. Then, in Section 3, we investigate the luminance perception error minimized by the fast solution in [9], resulting in two new bounds on the position of the best luma value. This section also explains the proposed linear approximation using the derived two bounds. Simulations for an extensive comparison study and their results are presented in Section 4, and then we conclude this paper in Section 5.
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Baseline Length Constraint DD Positioning Method  
In deformation monitoring, a priori information of baseline can be used and the baseline length constraint equation is [32]:  
\[L=\sqrt{(X_{r}-X_{b})^{2}+(Y_{r}-Y_{b})^{2}+(Z_{r}-Z_{b})^{2}} \tag{15}\]  
where \(L\) is the real baseline length; \((X_{r},Y_{r},Z_{r})\) and \((X_{b},Y_{b},Z_{b})\) are the coordinates of the rover and base receivers, respectively. (15) is linearized at the approximate point \((X_{r}^{0},Y_{r}^{0},Z_{r}^{0})\):  
\[L=L_{0}+[l\ m\ n]\cdot[\delta x\ \delta y\ \delta z] \tag{16}\]  
where \([\delta x\ \delta y\ \delta z]\)= \([X_{r},Y_{r},Z_{r}]-[X_{r}^{0},Y_{r}^{0},Z_{r}^{0}]\) are the increments of the unknown baseline components in X, Y, and Z directions, respectively. It is worth noting that \(x\) in (5) equals \([\delta x\ \delta y\ \delta z]^{T}\) in (16). \([l\ m\ n]\) are cosine values of baseline increments vector. Subsequently, the integrated GPS/Galileo SFSE DD code and phase observation equations with baseline length constraint can read as:  
\[\left[\begin{array}{c}y^{ge}\\ I_{0}\end{array}\right]=\left[\begin{array}{c}B^{ge}x\\ B^{ge}x+A^{ge}N^{ge}\\ B_{\delta x}\end{array}\right]+\left[\begin{array}{c}\xi^{ge}\\ 0\end{array}\right] \tag{17}\]  
where, \(I_{0}=L-L_{0}\) is the difference between the real baseline length and its approximate value and \(B_{\delta}\) is the coefficient vector \([l\ m\ n]\). Afterwards, the stochastic model for GPS/Galileo TCM with baseline length constraint can be expressed as:  
\[D^{GE}=\left[\begin{array}{ccc}\lambda_{P}^{2}Q^{GE}&I_{0}&I_{0}\\ I_{0}&\lambda_{\phi j}^{2}Q^{GE}&I_{0}\\ I_{0}&I_{0}&\lambda_{bic}^{2}\end{array}\right] \tag{18}\]  
where \(\lambda_{bic}^{2}\) denotes the variance of the baseline length.
## 3 Results and Discussion
### Establishment of DBA-LSTM Model  
According to the algorithm flow shown in Figure 6, we located the nodes in the grid model based on the coordinates of monitoring stations, with GD01-GD07 corresponding to id = 534, 598, 4334, 2810, 2742, 5336 and 6258, respectively. Then, to determine the inversion parameters, it was already known from previous analyses that rainfall is the main factor influencing landslide deformation, resulting in precipitation seepage through the surface into the landslide, which increases the water content of the sub-clay layer mud and reduces the slip resistance, leading to accelerated landslide creep deformation. Therefore, we took the shear strength parameter of the sub-clay layer as the inversion target parameter. Meanwhile, the infiltration of rainfall would increase the groundwater pressure, and these elevated pressures would, in turn, trigger slope motion, necessitating a fluid-structure interaction analysis for this landslide. The next step was the generation of the training samples and the training of the network model.