esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, hybrid integration of a heliostat solar receiver (HSR) and gas turbine cycle, combined cycle (gas-steam cycles), and a heat recovery steam generator (HRSG) were examined for power generation in Tehran (Iran). The application of the heliostat solar receiver was to utilize solar energy to heat the pressurized exhaust air before entering the combustion chamber to produce more steam in the gas cycle (GC) and combined cycle (CC). This action led to less consumption of fossil fuels and less of an environmental effect of these cycles. Additionally, the usage of adding a heat recovery steam generator (HRSG) is to exploit excess heat in the exhaust gas of these cycles for more electricity  
Figure 16: The annual average exergy destruction rate of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR.  
Figure 17: The levelized cost of electricity (LCOE) of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR.  
generation and reducing heat loss. Thermodynamic performance assessment of this study revealed since the energy efficiency for standalone GT was 28.4%, it could be significantly increased by up 76.5% for the combination of GT + HRSG + HSR. Moreover, the exergy analysis of this configuration states since the average exergy efficiency for the GT type was 29.2%, it could be improved up to 49.8 and 78.5% for the GT + HRSG and combination of GT + HRSG + HSR. The economic analysis of this hybrid system shows that the NPV and PP for the GT standalone were 1.92 \(\times 10^{8}\) US$ and 2.96 years, respectively. These two parameters can be changed to 2.83 \(\times 10^{8}\) US$, and 4.8 years for GT + HRSG, 1.44 \(\times 10^{8}\) US$, and 6.16 years for GT + HSR, and 2.37 \(\times 10^{8}\) US$ and 6.73 years for GT+HRSG + HSR. Furthermore, the IRR factor for the GT cycle was about 0.36, and 0.22 for the GT + HRSG and 0.17 for the GT + HSR and 0.16 for GT + HSR + HRSG.  
The thermoeconomic assessment of the combination of heliostat solar receiver (HSR) and Brayton cycle with air and supercritical \(Co_{2}\) as a working fluid instead of gas cycle alongside with HRSG and the ORC Rankine cycle can be examined as further work. Additionally, replacement of the absorption chiller with ORC or the application, Kalina cycle, and Goswami cycle instead of the heat recovery steam generator (HRSG) in this combination can be evaluated for future works too.  
S.M.A.; Methodology and Writing--original draft, A.G.; Visualization and Formal analysis, M.A.E.; Writing--original draft, Resources, Formal analysis, Investigation, Methodology, A.A.; Conceptualization, Software, Methodology, D.H.J.; Writing--original draft, Visualization, N.N.; Validation, Visualization, A.D.; Writing--review & editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
There is no special funding for this paper.  
The authors declare no conflict of interest.
## 3 Markov Chain Monte Carlo Algorithms
### Delayed Rejection Adaptive Metropolis (DRAM)
#### 3.1.2 Adaptive Metropolis (AM)  
While DR addresses the high rejection rate, the tedious tuning for the user-defined proposal variance remains. AM is proposed to adaptively modify the proposal variance based on the history of generated samples, automating the sampling process. The main principle of AM is to create a Gaussian proposal distribution with a dynamically varied covariance matrix, calibrated based on the trace path of a series of samples in the chain [18].  
1. _Non-Adaptation period_  
During an initial period \(\lambda_{0}\), AM is not activated, and the covariance matrix remains unchanged.  
2. _Modification Rule for Covariance_  
After a non-adaptation period, the covariance matrix is modified according to the previously sampled state:  
\[C_{\lambda}=\begin{cases}C_{0},&\lambda<\lambda_{0}\\ s_{d}Cov(x_{0},x_{1},\cdots,x_{\lambda-1})+s_{d}\epsilon L_{d},\lambda>\lambda _{0},\end{cases} \tag{15}\]  
where \(C_{0}\) is an arbitrarily pre-defined positive definite covariance during the non-adaptation period \(\lambda_{0}\), which is usually decided through some prior knowledge that may be poor. \(s_{d}\) is a parameter associated with problem dimension \(d\). Based on studies in [18], \(s_{d}\) can be typically taken as \(2.4^{2}/d\). \(\epsilon\) is a positive constant that may be selected as a very small value and only avoids any singular values for \(C_{\lambda}\). \(l_{d}\) is a \(d\)-dimensional identity matrix. For \(\lambda>\lambda_{0}\), covariance \(C_{\lambda+1}\) can be recursively calculated as  
\[C_{\lambda+1}=\frac{\lambda-1}{\lambda}C_{\lambda}+\frac{s_{d}}{\lambda} \Big{(}\lambda\overline{x}_{\lambda-1}\overline{x}_{\lambda-1}^{T}-(\lambda+ 1)\overline{x}_{\lambda}\overline{x}_{\lambda}^{T}+\overline{x}_{\lambda} \overline{x}_{\lambda}^{T}+\epsilon I_{d}\Big{)}, \tag{16}\]  
where \(\overline{x}_{\lambda}=\frac{1}{\lambda+1}\sum_{i=0}^{\lambda}x_{i}\). Equation (16) also allows the calculation of covariance without extra computational burden since \(\overline{x}_{\lambda}\) is also recursively calculated.
## 5 Conclusions  
In order to realize the rapid and low-cost update of the SAR ATR system, this paper proposes an incremental SAR ATR method based on a class-specific dictionary. To this end, we make use of intra-class divergence to constrain the process of dictionary learning. Then, the class-specific dictionary of each class target is learned separately. These class-specific dictionaries are organized into a dictionary set for classification. During testing, the sparse representation coefficients of these samples under all dictionaries are calculated. On the basis of the coefficients, two indicators of reconstruction error and intra-class divergence are calculated, and finally, the targets are classified. When encountering new class data, it is enough to learn the corresponding class-specific dictionary, and then it is used to update the dictionary set. Experimental results on the MSTAR dataset have demonstrated the effectiveness of our method. In summary, the learning process of class-specific dictionaries does not require the participation of other class samples, so it is very suitable for class incremental learning.  
For future research, our method will be enhanced in two key aspects.  
(1) Incremental feature extraction [50, 51]: the current approach of directly learning dictionaries from vectorized SAR images without feature extraction incurs high computational costs. We will explore incremental feature extraction techniques to mitigate this issue. Additionally, ensuring feature extraction captures target-specific features rather than SAR image background information is also a key focus for future work.  
(2) Open-world recognition [52, 53]: Class incremental learning leverages novel class targets to rapidly update SAR ATR systems, while open set recognition [54, 55] facilitates the quick discovery of unknown class targets. These two approaches are inseparable in their applications, and their combined use has been termed open world recognition by scholars [52, 53]. In future work, we will investigate SAR target recognition under open world scenarios to enable the SAR systems to adapt efficiently to dynamic environments.  
Conceptualization, X.M. and X.B.; methodology, X.M.; software, X.M.; validation, D.Z. and Z.W.; formal analysis, X.M.; investigation, X.B.; resources, X.B.; data curation, X.M.; writing--original draft preparation, Z.W. and J.L.; writing--review and editing, X.M. and Z.W.; visualization, Z.W. and D.Z.; supervision, J.L.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Satellite Network Digital Technology Co., Ltd.
## 6 Discussion  
### Research Questions  
To address the objective of this work, we have studied the following research questions (Table 2).  
Specifically, in this paper, we investigate the MEC-enabled UAV-assisted VANET architecture to provide vehicles with low latency and reliable computing services through UAVs. On this basis, the network optimization problem is formulated as a joint problem of the task offloading, the resource allocation, and the security assurance, and the objective is to minimize the task processing delay. For tackling this joint optimization problem, we decouple the optimization problem as two subproblems and propose an efficient iterative algorithm according the relax-and-rounding method and the Lagrangian dual decomposition.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**No.** & **Research Questions.** \\ \hline
1 & As the obstructions of vehicles can affect the service quality of vehicle-RSU links, how can we improve the connectivity of VANETs? \\
2 & As the obstructions of vehicles can affect the service quality of vehicle-RSU links, how can we improve the connectivity of VANETs? \\  & How do we minimize the task processing delay for MEC-enabled UAV-assisted VANETs by jointly considering the task offloading, the resource allocation, and the security assurance? \\ \hline \hline \end{tabular}
\end{table}
Table 2: Research questions.  
Figure 13: Comparison of the successful task processing ratio with respect to the number of vehicles under different task maximum allowed latencies.  
The simulation results show that the proposed LBTO algorithm achieves better performance than the other algorithms in terms of the successful task processing ratio and the task processing delay. In our designed LBTO algorithm, each vehicle can select one appropriate MEC server on UAV and offload parts of the computation task, which can achieve load balancing and service reliability improvement by the task scheduling among edge MEC servers on UAVs. Furthermore, these results fully show that our proposed LBTO algorithm is better than the benchmarking algorithms, and the schemes with optimized network parameters are superior to those without optimization.
## 2 Different Methods of Hydrogen Generation
### Commercial Technologies of Hydrogen Generation
#### 2.2.1 _Steam Reforming_  
The endothermic conversion of natural gas and water vapor into hydrogen and carbon monoxide occurs during steam reforming. The heat is frequently provided by the burning of part of the natural feed gas. The process is generally carried out at temperatures that range from 700 to 1000 \({}^{\circ}\)C [29].  
Equation (1): Steam reforming of hydrocarbon [30]:  
\[\mathrm{C_{n}H_{m}+nH_{2}O\rightleftharpoons nCO+((m/2)+n)H_{2}} \tag{1}\]  
Equation (2): Water-gas shift reaction [31]:  
\[\mathrm{CO+H_{2}O\to CO_{2}+H_{2}} \tag{2}\]  
\(\mathrm{C_{n}H_{m}}\) represents a hydrocarbon with \"n\" carbon atoms and \"m\" hydrogen atoms. nH\({}_{2}\)O represents \"n\" water molecules. nCO represents \"n\" molecules of carbon monoxide. Finally, (m/2 + n) H\({}_{2}\) represents the total number of hydrogen gas molecules produced, taking into account \"m\" hydrogen atoms from the hydrocarbon and \"n\" from water. The second equation represents the reaction between carbon monoxide (CO) and water (H\({}_{2}\)O) to produce carbon dioxide (CO\({}_{2}\)) and hydrogen gas (H\({}_{2}\)). This reaction is known as the water-gas shift reaction (1).
## 3 Path Loss Models
### UMa Path Loss Models
#### 3.2.3 METIS Model  
In this model, LOS and NLOS PL is measured using the large-scale ABG model [18].
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 2 Overview of Test Stimulus Compression Techniques
### Scan Chain Structure Optimization-Based  
#### 2.2.1 Linear Decompression Architecture  
Stimulus compression methods based on a linear decompression architecture typically utilize a linear decompression mechanism composed of XOR gates and flip-flops to expand and populate the test data output from the ATE into the scan chains within the CUT [18].  
The linear decompression architecture effectively leverages the large number of don't-care bits in the test vector set to achieve test compression [25]. However, this method is ineffective for the fixed bits within the test vectors. Consequently, the compression efficiency of such stimulus compression methods is limited by the number of fixed bits in the test vectors, and the compressed test data volume must be at least equal to the number of fixed bits in the original test set [26]. To achieve higher test efficiency, researchers have combined the linear decompression architecture with nonlinear encoding compression to deeply compress both the fixed and don't-care bits within the test vectors [27].
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Effect of Adsorption Time  
Approximately 100 mg of Alg-EDTA was weighed and added to 50 mL of 1.5 mM Cd\({}^{2+}\) (Pb\({}^{2+}\), Cu\({}^{2+}\), Cr\({}^{3+}\) or Co\({}^{2+}\)) solution at 25 \({}^{\circ}\)C, stirred for 2, 4, 6, 8, 10, 12, 15, 20, 30, 40, 50, 60\(\cdot\cdot\cdot\cdot\cdot\)480 or 540 min, and then filtered. The concentration of residual metal ions in the filtrate was determined by ICP-OES. The experimental results are shown in Figure 6. The adsorption capacity of Alg-EDTA for Cd\({}^{2+}\) and Pb\({}^{2+}\) increases first, and then remains constant with the increase in the adsorption time. When the adsorption time is 60 min, Alg-EDTA can adsorb more than 40% of heavy metal ions in the solution. While the adsorption time greater than or equal to 360 min, Alg-EDTA can adsorb more than 85% of heavy metal ions in the solution. Therefore, an adsorption time of 360 min was selected in this study. In addition, the pseudo-first-order and pseudo-second-order models were employed to fit the kinetic data in order to study the adsorption mode of metal ions by Alg-EDTA. The fitting results show that the pseudo-first-order and the pseudo-second-order models are fit well with the kinetic data (Table 1), thereby indicating that physisorption and chemisorption are both included in the adsorption mechanism [12; 13].  
Figure 5: Effect of solution pH on metal ion adsorption of the Alg-EDTA.  
Figure 6: \(Cont.\)
# Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
## 2 Mathematical Setting of Thermodynamic Formalism
### Correlations
#### 2.3.3 Time Averages and Central Limit Theorem  
We have seen that if the measure \(\mu\) is ergodic the time averages \(A_{n}(f)\) converge \(\mu\)-almost surely to the expected value \(\int f\mathrm{d}\mu\). Now, we can ask about fluctuations around the expected value. The observable \(f\) satisfies the central limit theorem (CLT), with respect to \((\sigma,\mu)\) if:  
\[\frac{A_{n}(f)-n\int f\mathrm{d}\mu}{\sqrt{n}}\xrightarrow{\mathrm{law}} \mathcal{N}_{0,\sigma_{f}^{2}} \tag{10}\]  
where \(\mathcal{N}_{0,\sigma_{f}^{2}}\) is the Gaussian distribution with zero mean and covariance \(\sigma_{f}^{2}\), which is given by the following expression involving temporal correlations:  
\[\sigma_{f,g}^{2}=C_{f,g}(n)(0)+\sum_{n=1}^{\infty}C_{f,g}(n)+\sum_{n=1}^{\infty }C_{g,f}(n).\]  
that is a particular case of (7). We illustrate an application of this theorem in the context of spike train statistics later in Section 2.6.  
Strong properties of convergence and exponential decay of correlations are ensured for Holder continuous potentials in finite dimension. These properties are associated with the spectral gap property and they do not (necessarily) hold for less regular potentials or in non-compact spaces [78; 80].
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 4 Polyphenol Analysis: From Quantification to Antioxidant Capacity
### Spectrophotometric Methods
#### 4.2.4 Ferric Reducing Antioxidant Power (FRAP)  
FRAP assay is a colorimetric method that measures the antioxidant capacity of a sample based on a redox reaction. The reaction involves a SET mechanism and consists of the reduction of ferric tripyridyl triazine complex to ferrous form at low pH (3.6), which has an intense blue color and can be monitored by measuring the change in absorption at 593 nm. The increase of absorption is related to the concentration of antioxidants in the sample. Trolox is used as standard and results are expressed in Trolox equivalent concentration [72; 75; 77]. [PERSON] et al. [60] evaluated the potential of wine lees polyphenols as novel functional bioactive compounds in the protection against oxidative stress and hyperlipidemia. The antioxidant capacity of wine lees determined by FRAP was 45.7 \(\pm\) 1.05 mM of Trolox equivalents per 100 g, respectively.
# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 1 Introduction
### Cyber-Physical System (CPS)  
Cyber involves computation, communication and control that is discrete, switched and logical. Physical represents natural, man-made systems that are governed by the laws of physics and operate continuously. A Cyber-Physical system (CPS) connects information technologies to the physical (mechanical, electronic) world (objects). The communication between them takes place by using data infrastructure such as the Internet [6].  
According to [6], CPS consists of two main functional components:  
* Advanced connectivity, which provides real-time data acquisition from the physical world while providing information feedback from cyberspace;
* Intelligent data management, computational and analytical capabilities that create cyberspace.  
Technologies that are closely connected to CPS include IoT, Cloud Computing and wireless sensors, which are considered an important part of CPS [6; 13] while in their integrity they can be defined as the centre of Industry 4.0 innovation [28]. More devices, machines, vehicles and production tools are now embedded with sensors [20].  
Cyber-Physical systems connect the physical world with the virtual world of information technology and software through the use of data, communication, the Internet and services. The application of CPS by leveraging the Internet of Things in industrial organizations contributes to the generation of a large amount of information that is necessary yet complex to analyse. For the effective analysis of a large amount of data, Big Data analysis is used through which meaningful outputs can be obtained and the real value of Big Data can be uncovered [20].
## 1 Introduction
Noise also produces false undulations, which are undulations in the current that do not belong to the ripple component of the current. There are studies that detected ghost undulations and discarded false undulations [27; 28], but the employed methods were complex and had a high computational cost. The ripple component depends directly on the number of coils in the motors, because this component is produced by the rectification of the brush-commutator system. Then, when the number of coils in the motor is small, the rectification done by the system brush-commutator is far from the ideal, whereas when the number of coils in the motor is large, the rectification done by the system brush-commutator is close to the ideal, in other words, just consisting of a dc component. In this way, sensorless observers based on the ripple component can be used in brushed dc motors with a small number of coils, such as fractional horsepower brushed dc motors, because the ripple component is large enough to be detected. In contrast, sensorless observers based on the ripple component cannot be used in brushed dc motors with a large number of coils because the ripple component is not large enough to be detected. This paper presents a new method that measures the current of brushed dc motors and analyzes the position of its spectral components in order to estimate the motor speed. This method can be used in motors with a large number of coils, as in integral horsepower dc motors. Section 2 describes the different spectral components present in the current and establishes which ones are important in brushed dc motors with a small number of coils, as in fractional horsepower brushed dc motors, and which are important in brushed dc motors with a large number of coils such as integral horsepower brushed dc motors. Section 3 presents the proposed sensorless method for estimating the speed of brushed dc motors. Section 4 presents experiments used to validate this method. Finally, Section 5 presents the conclusions derived from this research.
# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods
### Data and Pre-Processing
#### 2.2.2 Pre-Processing and Collection of Samples
For example, a Normalized Difference Vegetation Index (NDVI) value greater than 0.2 is reported as high for the dry season in Sudanian savanna as found in the study area [95]. If a pixel fulfills this criterion throughout all images it was considered as forest. Information from near infrared (NIR) and short wave infrared (SWIR) bands was used for criteria of the classes grassland and shrubland but also for thresholds for the abiotic surfaces of bare rock and soil. As reference for urban areas, the extent of camp Djabal in the year 2007 was digitized from the Landsat image. These areas are observed to be stable over time as the camp reached a stable phase. To prevent misclassifications, we removed areas smaller than 500,000 m\({}^{2}\) from the identified sample areas. Recognizing the these kinds of savanna ecosystems are highly affected by wildfires [96], we calculated the Normalized Burn Ratio (NBR [97]) for all years and scenes. This characterizes burnt areas which were excluded from the identification of sample areas because they no longer reveal which land cover was present before the fire. Furthermore, clouded areas were excluded throughout all scenes. In a second step, a number of 1600 random points was generated within the remaining areas as sample locations for the SAR classification (see Section 2.3). This technique guarantees that the sample points were chosen both stratified and random while partially respecting the spatial occurrence of each class within the study area. Table 2 lists the criteria used for their identification of the sample areas and the final number of samples per class. Figure 2 shows the remaining areas which were used for the stratified random sampling. It shows the result of the criteria given in Table 2 which helped to identify stable LULC areas over the investigated period. Clearly visible are camp Djabal in the middle, surrounded by mostly bare soil resulting from less vegetation cover and agricultural use, the bare rocks of Hadjer Arkop and the denser forests of Wadi Aouada in the Northwest. As indicated in Figure 1 grassland and shrubland cover the wide plains in the North and East. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Samples** & **Criterion** & **Source** \\ \hline Urban area & 161 (7.2\%) & Area of the camp in 2007 & - \\ Bare soil & 390 (17.6\%) & Red \(<\) 0.25 \& SWIR1 \(>\) 0.35 & Drury (2001) [98] \\ Bare rock & 352 (15.9\%) & SWIR1/SWIR2 \(>\) 1.5 & Drury (2001) [98] \\ Grassland & 374 (15.6\%) & NDVI \(>\) 0.15 \& NDVI \(<\) 0.25 & [PERSON] et al. (2012) [95] \\ Shrubland & 126 (5.6\%) & Red \(<\) 0.2 \& NIR \(>\) 0.3 \& SWIR \(<\) 0.3 & [PERSON] et al. (2016) [99] \\ Forest & 172 (7.8\%) & NDVI \(>\) 0.2 & [PERSON] et al. (2012) [95] \\ Burnt areas & 0 (0\%) & NBR \(>\) 0.15 & [PERSON] \& [PERSON] (1991) [97] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Land-use and land cover (LULC) classes and identification of sample areas in the study area.