esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation  
### Modeling Conditional Forecast Error for Multiple Wind Farms  
The forecast uncertainty of wind power is characterized by probabilistic modeling of the forecast error with continuous probability density function (PDF). In most studies, the forecast error is simply represented by Gaussian distribution with zero mean and wind power standard deviation \(\sigma_{W}\). However, the Gaussian assumption has known limitations as fixed kurtosis (\(\kappa=3\)) which is inappropriate for fitting the skewed and fat-tailed error distribution (\(3<\kappa<10\)) [8]. Moreover, the forecast error distribution is a conditional PDF with respect to the day-ahead point forecast, showing significant diversity in different forecast levels [27]. Therefore, we adopt the following procedure [8] to generate the PDF of wind power output for a given forecast using the actual wind data. First, the standardized forecast range ([0, 1] p. u.) is evenly divided into several levels, namely the _forecast bins_. Then, the data pairs [actual forecast] of wind power at each time step are sorted by the point forecast and assigned to the corresponding forecast bins. Finally, the specific distribution is selected to fit the actual wind power PDF at each forecast bin. The beta distribution is employed here because it has variable skewness and kurtosis, and its value is limited to the interval [0, 1] which matches the range of normalized wind power.  
The way of modeling the dependence of adjacent wind farms is through the correlation coefficient obtained from numerous historical wind power measurement data, which is expressed as  
\[\rho_{ij}=\frac{Cov\big{(}W_{i},W_{j}\big{)}}{\sigma_{i}\sigma_{j}}=\frac{ \sum_{t\in\mathcal{T}}\big{(}W_{i}^{t}-\overline{W}_{i}\big{)}(W_{j}^{t}- \overline{W}_{j})}{\sqrt{\sum_{t\in\mathcal{T}}\big{(}W_{i}^{t}-\overline{W}_ {i}\big{)}^{2}}\sqrt{\sum_{t\in\mathcal{T}}\big{(}W_{j}^{t}-\overline{W}_{j} \big{)}^{2}}} \tag{1}\]  
where \(Cov(\cdot)\) denotes the covariance operator; \(W_{i}^{t}\) and \(W_{j}^{t}\) are actual wind power at bus \(i,j\) and time \(t\); \(\overline{W}_{i}\) and \(\overline{W}_{j}\) are the means of wind power at bus \(i,j\) throughout the whole time periods, and \(\sigma_{i}\), \(\sigma_{j}\) are their standard deviations. The correlation coefficient is 1 indicating a perfect linear relationship, and is 0 in case of uncorrelation. Then the coefficient \(\rho\) makes up of the correlation coefficient matrix \(\mathbf{R}_{W}\) for \(n\) wind farms, defined as  
\[\mathbf{R}_{W}=\left(\begin{array}{ccc}1&\ldots&\rho_{1n}\\ \vdots&\ddots&\vdots\\ \rho_{n1}&\ldots&1\end{array}\right)_{n\times n},\rho_{ij}=\rho_{ji} \tag{2}\]  
It is noted that \(\mathbf{R}_{W}\) only measures the degree of linear dependence among wind farms, and should be modified when undergoing non-linear transformations (e.g., Nataf transformation [28]).
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Specification** \\ **of WMR** \\ \end{tabular} } & \multicolumn{3}{c}{**WMR with CBF-QP**} & \multicolumn{3}{c}{\begin{tabular}{c} **WMR with Adaptive Model** \\ **Productive Control (AMPC)** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **\% Change in CBF-QP over Adaptive** \\ **Model Productive Control (AMPC)** \\ \end{tabular} } \\ \cline{2-10}  & \begin{tabular}{c} **Settling** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Rise** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Error** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Settling** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Error** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Settling** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Rise Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Error** \\ **Error** \\ \end{tabular} \\ \hline Position (m) & 0.98 & 0.67 & 0.005 & 1.7 & 1.6 & 0.4 & 42\% & 58\% & 98\% \\ \hline Velocity (m/s) & 0.88 & 0.66 & 0.003 & 1.9 & 1.7 & 0.3 & 54\% & 61\% & 99\% \\ \hline Control gain (J) & 10 & 9 & 0.05 & 100 & 110 & 0.9 & 90\% & 92\% & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of CBF and adaptive model productive control (AMPC).
## 3 Results
### Technical Performance Results
#### 3.1.3 Phase Evolution  
Figure 5 shows the XRD patterns of the different pastes over time. It was observed that in all pastes, there was a quick decline in the clinker phases (C\({}_{3}\)S, C\({}_{3}\)A and C\({}_{4}\)AF), while hydrates (portlandite, ettringite, hemicarboaluminates and monocarboaluminates) were formed. Note that similar hydration products were formed in all pastes. The Rietveld analysis results are plotted in the form of area charts in Figure 6a-e. Comparing the rate of consumption of the clinker phases between P_PC and other pastes, it can be concluded that the clinker reactions progress faster in the blended mixtures. The calcite also appeared to be consumed over time in all mixtures, and hemicarboaluminate and monocarboaluminate phases formed at day 1 and continued to slightly grow over time. The P_TS2 paste showed some traces of fayalite, which comes from the slag. However, the fayalite did not appear to be reacting.  
Figure 4: The specific heat flow and cumulative heat patterns of the pastes per gram of cement (**a**) in 24 h and (**b**) in 7 days.  
Figure 5: The evolution of X-ray patterns of different pastes over time: (**a**) P_PC, (**b**) P_QP, (**c**) P_TS1, (**d**) P_TS2 and (**e**) P_TS3. Ett: ettrinigite; Hc: hemicarboxoluminate hydrate; Mc: monocarboxoluminate hydrate; Pt: portlandite; Ft: fayalite.  
Figure 7 shows the clinker DoR for different pastes over time. Note that the clinker degree of reaction of the blended pastes were in all cases and ages higher than that of pure cement paste (P_PC). One reason for the higher DoR in blended pastes is the replacement of part of the cement with an inert (quartz) or a moderately reactive material (slag), which resulted in an increase in the effective water-to-cement ratio. As such, the accessibility of water for cement was enhanced, leading to more reactions at any given time. Comparing the three slags with each other, it could be argued that the mixture containing TS2 experienced a slightly higher clinker reaction compared to TS1 and TS3. Nevertheless, all three slags were almost equally reactive, judging by the calorimetry and clinker DoR results.  
Figure 6: Area charts displaying the evolution of different phases over time; values in g/100 g of binder. (**a**) P_PC, (**b**) P_QP, (**c**) P_TS1, (**d**) P_TS2 and (**e**) P_TS3.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 1 Introduction  
Indoor environments, such as homes, schools, and offices, account for approximately 90% of the time spent by individuals [1], making indoor air quality (IAQ) [2] a critical factorfor human health. Prolonged exposure to various gaseous compounds in these settings has necessitated a deeper understanding of IAQ to ensure health safety. Indoor spaces are characterized by a wide range of pollutant sources that continuously release contaminants, including volatile organic compounds (VOCs) [3], nitrogen oxides (NO\({}_{\rm x}\)) [4], non-biological particulate matter (PM) [5], and biological agents like bacteria and fungi [6,7]. These pollutants, even at low concentrations, can accumulate over time and contribute to a decline in IAQ, posing significant risks to human health. Long-term exposure to these hazardous compounds can lead to mild symptoms such as headaches, nausea, and skin irritation, while higher exposure levels have been linked to severe respiratory and cardiovascular diseases, organ damage, cancer, and even premature mortality [8, 9, 10]. As such, maintaining IAQ in indoor environments is essential to safeguud human well-being [11].  
A variety of strategies have been developed to mitigate the risks associated with poor IAQ, with photocatalytic oxidation emerging as a promising approach due to its ability to degrade pollutants using light irradiation and photocatalytic materials [12]. This process has gained considerable attention for its potential to effectively eliminate diverse indoor air contaminants. Research has predominantly focused on the development of nanomaterials that serve as efficient photocatalysts, with titanium dioxide (TiO\({}_{2}\)) standing out among semiconductors for its remarkable properties in the oxidative degradation of VOCs and NO\({}_{\rm x}\)[13]. Studies have extensively investigated the photocatalytic activity of TiO\({}_{2}\), examining its various morphologies--such as nanoparticles, nanotubes, and nanosheets--and the impact of modifications like doping and heterojunction formation on enhancing its pollutant degradation capabilities [14].  
Beyond TiO\({}_{2}\), other nanomaterials have been explored for their roles in photocatalytic oxidation, either as pollutant adsorbents or as active photocatalysts. Carbon-based nanomaterials [15], including carbon nanotubes and graphene, have garnered significant interest due to their exceptional adsorption properties and versatile structural characteristics. Furthermore, less commonly used nanomaterials, such as zinc oxide (ZnO) [16] and tungsten oxide (WO\({}_{3}\)) [17], have also been investigated to expand the range of photocatalytic oxidation applications.  
The integration of these nanomaterials into real-world applications, such as building materials, paints, and air purification systems, has demonstrated their efficacy in reducing indoor pollutants and providing antimicrobial benefits. By making use of these innovative materials, substantial improvements in IAQ can be achieved, promoting healthier indoor environments.
## 6 Future Trends and Issues
### Demand Control Ventilation (DCV)  
Commercial building ventilation is required to remove the airborne contaminants generated by occupants' and other living organisms' biological functions, occupant tasks and operations, equipment, supplies and furnishings, building materials, and the products of chemical reactions between contaminants from both indoor and outdoor sources [207]. [PERSON] [208] estimated that improving interior environmental conditions in commercial buildings might save USD tens of billions per year in the US. One of the few important elements required for these improvements was adequate ventilation. In nearly all U.S. climates, building HVAC systems require a large amount of energy to condition the external air utilized for ventilation. Natural ventilation, mechanical ventilation, or a combination of the two, referred to as hybrid ventilation, are examples of different types of ventilation systems [209]. In addition, there are also two types of ventilation airflow that can be supplied to the room by using constant air volume (CAV) or variable air volume (VAV). In industrial, commercial, educational, and office buildings, the VAV system is already widely used [210]. Several researchers have examined the effects of outdoor air supply rates (i.e., ventilation rates) on human health, comfort, and performance in commercial buildings. Lower rates of sick leave and the incidence of common respiratory disorders are linked to higher ventilation rates. Lower ventilation rates have been associated with building-related sickness (BRI), sometimes referred to as sick building syndrome (SBS). By adapting the ventilation air conditioning load to the actual occupancy, DCV can save energy. During this time, a variety of occupancy measurements were considered, including carbon dioxide (CO\({}_{2}\)), volatile organic compound (VOC), and humidity concentrations [211].
## 3 Spectrum Management for CR-VANET Using MCDM
### Channel Decision Based on Channel Ranking
#### 3.2.1 DAHP Data Services Implementation Procedure  
The channel decision factor deals with some quality metrics to analyze the weight of the channel for decision-making in terms of signal-to-noise ratio, bit error interference ratio, and bandwidth. Optimizing the channel strength needs to give priority to major channel parameters. Initially, AHP generated a rating from 0 to 1 to improve the better channel selection. Scores need to be compared to produce a better decision-making strategy. The AHP rating parameters are as follows.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 4 Design, Sizing, and Modeling of the Wind Energy and Storage System
### Sub-Model 1: Wind Speed Model  
This sub-model utilizes the \"2020 Wind Data, 15 Minutes\" dataset to simulate variations in wind speed under different conditions. For simulation purposes, three distinct wind scenarios were defined:  
* Unfavorable: Wind speed decreases between \(-10\%\) and \(-5\%\);
* Random: Wind speed fluctuates within a range of \(-5\%\) to \(+5\%\);
* Favorable: Wind speed increases between \(+5\%\) and \(+10\%\).  
These conditions enhance the model's flexibility, reflecting the natural variability of wind speeds, which fluctuate continuously. The simulation of wind speed variations is controlled by the following formula (Equation (1) and Equation (A4) in Appendix A):  
\[r=a+(b-a)*rand(N,1) \tag{1}\]  
where  
* \(a\) and \(b\) are the lower and upper bounds of the wind speed variation interval;
* \(N\) represents the random numbers within the interval (\(a\),\(b\));
* \(rand(N\),\(1)\) is the MATLAB random function that generates values within the specified range.  
A 15 min time step is used in the model, ensuring high-resolution variability in wind speed. Equation (1) serves as the MATLAB function for wind speed randomization, allowing for dynamic simulation of the 25 kV grid response, particularly regarding  
* Wind turbine performance fluctuations;
* Energy storage system behavior under variable wind conditions;
* Grid stability under different wind penetration scenarios.  
Additionally, this sub-model enables further analysis of critical wind conditions, such as  
* The number of hours per year with no wind;
* Consecutive hours with zero wind generation;
* Consecutive hours without peak wind speeds.  
By incorporating these stochastic wind variations, the model ensures a realistic assessment of wind energy intermittency and its impact on grid stability and energy storage requirements at the Ragland mining site.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 5 Conclusions
It follows that an image made from the output of only one of the linearly-polarized channels of an HCP radar can be misleading. The fact that results from HCP vs. QP classification comparisons are so favorable raises the obvious question: _why_? To formulate a credible response, the usual methods of QP classification deserve to be revisited. It could be true, for example, that the dominant polarimetric characteristics conveyed by the backscattered field observed by a QP radar are the same as those observed by an HCP radar. If true, then the sensitivity of a scene's signature to its illumination polarization has yet to be fully exploited through classification procedures that are adopted by operational agencies. As a review of the literature shows, rather few QP-based classifications work explicitly with the polarization signature, which as [PERSON] observed more than 30 years ago is one key to revealing a target's signature dependencies on the transmitted polarization. Without a polarimetric signature of an observed scene, it should be no surprise that results from the two architectures should be so similar. The main point of this review is that QP as exploited in practice performs only marginally better than hybrid CP, with most examples showing near equivalence, and a few isolated examples showing HCP superiority. The reason is that QP decompositions do not take into full account the degree of freedom embedded in the QP \(3\times 3\) matrix data to search over all possible transmit polarizations to find one that best exposes a scene's characteristics of interest. Since wide area coverage by a polarimetric SAR is a significant user's requirement, and classifications deduced from HCP data are to first order comparable to standard decompositions from QP data, hybrid compact polarimetry qualifies as an operational mode.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 5 Conclusions  
In this paper, we demonstrate 5G LDPC (7680, 3840) code in FTN signaling to explore spectrum efficiency and reliability. When limited to low latency and low storage, i.e., few BCJR states and few turbo iterations, we found an undesirable error floor. To combat the error floor, we proposed one possible solution, namely, using the LDPC-RS concatenation and shortening scheme together with perturbation introduced into the BP decoder, where the LDPC has a rate of 0.5 as well as a 0.94 high-rate hard decision RS code to lower the rate loss. We proposed a 4-parallel encoder architecture for RS component code and a concise algorithm to calculate its constant multiplier coefficients, leveraging a traditional serial encoder. This algorithm can also be used for calculating other parallelisms, code rates, and lengths. The simulation results show that the proposed concatenation and shortening scheme can lower the error floor of the original single LDPC code, and with perturbation in the BP decoder, the error floor can be eliminated below \(10^{-7}\) for different LDPC maximum decoding iterations. The advantage of the proposed scheme is that the LDPC has an error correction ability for BCJR detection with few states and few iterations of turbo equalization FTN signaling, while RS concatenation, shortening, and perturbation techniques together lower the error floor. In future research, other LDPC codes will be constructed, factor graph-based FTN detection with LDPC joint decoding will be explored, and LDPC-RS soft iteration decoding will also be studied. A better equalizer will also be studied to strengthen and broaden the LDPC-RS concatenation scheme's implementation for fading channels and other scenarios.  
[PERSON] conceptualized the idea of this research, conducted experiments, collected data, and prepared the original version. [PERSON] improved the methodology and updated the idea and structure of this research. [PERSON] supervised and reviewed. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant No. 2021 YFB2900500, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2023B1515120093, the Key Natural Science Foundation of Shenzhen under Grant No. JCYJ20220818102209020, and the Science, Technology and Innovation Commission of Shenzhen Municipality under Grant No. JCYJ20210324120002007.  
The data used for the experiments reported in this paper are available upon request to the authors via email.  
The authors declare no conflicts of interest.
## 4 UAV Communication Network and Technology
### Communication Solution Models
* UAV Communication over Ad Hoc Network  
This model works on the principle of centralized management, where UAVs communicate with each other temporarily. The network structure can be organized as a flat or  
Figure 16: UAV communication via satellite. in a specific area. In complex environments such as urban or mountainous terrain, wireless LoS communication solutions are preferred [62]. * UAV communication to ground control station  
Ground control stations are often permanently installed to perform UAV management [63], through which different types of UAVs can be connected and communicate with each other. Some UAVs are specially designed to interact with ground control to reduce traffic congestion and increase connectivity.
## 3 Results and Discussion  
### Effect of Carbonization Pressure on CO\({}_{2}\) Sequestration and Rheological Properties of CGBS  
#### 3.1.1 Effect of Carbonization Pressure on CO\({}_{2}\) Sequestration Performance of CGBS  
The effect of carbonization pressure on the CO\({}_{2}\) sequestration performance of CGBS is shown in Figure 5. As the carbonization pressure increases, the CO\({}_{2}\) sequestration capacity of CGBS increases first and then stabilizes. When the carbonization pressure is 0.7 MPa, the CO\({}_{2}\) sequestration capacity is 0.38%, which is 1.92 times higher than that at 0.1 MPa. When the carbonization pressure is further increased, the CO\({}_{2}\) sequestration capacity changes slightly. There may be two main reasons: 1 When the carbonization pressure exceeds 0.7 MPa, the carbonation reaction intensifies. A dense calcium carbonate shell is generated on the surface of the particles, which hinders the penetration of CO\({}_{2}\) into the interior of the particles, slowing down the carbonation reaction [19]. 2 The increase in pressure also intensifies the generation of hydration products. The rapid increase in hydration  
Figure 4: Rheological testing equipment and procedure.  
products will make the slurry structure dense [20], resulting in basically unchanged CO2 sequestration capacity.
## 2 Effect of Floating DC Bus on System Stability
### Numerical Simulation  
By removing the conventional load CL from the equivalent circuit in Figure 2, it is possible to define the system's states as in Equation (1).  
\[\left\{\begin{array}{l}\frac{\bullet}{i}=\frac{di}{dt}=\frac{1}{t}(e-ri-v)\\ \frac{\bullet}{v}=\frac{dv}{dt}=\frac{1}{c}(i-i_{L})=\frac{1}{c}\big{(}i-\frac {p}{v}\big{)}\end{array}\right., \tag{1}\]  
where \(v\) is the CPL voltage, and \(i\) is the current flowing in the inductor \(l\). The system parameters were those used in Reference [33], while the CPL power was set at its rated value (i.e., \(p=1\) p.u.).  
Using Matlab Simulink, Equation (1) was implemented to perform simulations to assess the \(v\)-\(i\) transients following a perturbation. The results were used to provide a dynamic validation of the stability limits previously calculated. The studied DC microgrid in steady-state condition (working point \(v_{0}=0.8\) and \(i_{0}=1.25\) p.u.) was perturbed at \(t=t_{0}=0.2\) s by a voltage impulse  
Figure 3: Basins of attraction (BAs) corresponding to shifted equilibrium points.  
capable of instantaneously moving the voltage \(v\) applied to the CPL to the new \(v(t_{0})\) voltage. After the perturbation, the state variables were free to evolve. The study developed in Reference [37] previously demonstrated that this perturbation can be employed for effectively testing the large-signal stability. Therefore, in the following sections, a voltage impulse was considered proper for evaluating the capability offered by the two approaches. The variation in the perturbed initial state \(v(t_{0})\) allows comparing the consequent \(v\)-\(i\) transients shown in Figures 4 and 5. In particular, it is possible to notice an unstable behavior when \(v(t_{0})=0.56\) p.u. (black curves), whilst red/green transients (\(v(t_{0})=0.59\) p.u.) are stable and converge toward the pre-disturbance working point. Thus, the performed simulations verified the lower voltage stability limit for the system calculated using BA analyses (\(v\approx 0.57\) p.u.).