esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Methods like Proper Orthogonal Decomposition (POD) [40] which is effective for identifying dominant coherent structures and their energy distribution, Dynamic Mode Decomposition (DMD) [41], which excels in tracking dynamic flow features and their temporal evolution, or Particle Image Velocimetry (PIV) [42], which offers richer visual or quantitative insights into extracting velocity field data, demand complex setups and computational resources. Instead, in this paper, the traditional Schlieren imaging paired with a code-based image processing is adopted, which represents a practical trade-off in directly linking flow patterns to acoustic measurements. This combination offers several advantages compared to modal extraction methods by providing a straightforward, visually intuitive representation of flow structures by highlighting iso-density gradients and flow features. The iso-level segmentation and contour mapping are tailored specifically to enhance Schlieren data, making it more accessible for qualitative and semi-quantitative visual insights into the interplay between turbulence and density gradients, which align well with the acoustic results and noise-reduction mechanisms explored in this paper. Unlike POD or DMD, this combination does not decompose the flow into modes, which could limit its ability to isolate dominant coherent structures. In this study, ejector and chevron nozzle configurations are installed at the exhaust nozzle of a micro turbojet engine. The analysis focuses on the maximum operating regime, defined by the rotational speed, as it is the most critical. The baseline configuration (without chevrons or other passive control technique applied), the ejector configuration and the configuration with 16 triangular-shaped chevrons are examined. In addition to optical diagnostics, sound levels in the vicinity of the engine are recorded and subsequently processed for analysis. This paper explores the mechanics and effectiveness of these passive noise-reduction devices, emphasizing the performance of ejectors and chevrons in mitigating sound. Through comparative analysis and sound pressure level (SPL) measurements, the efficiency of these devices in reducing noise emissions, particularly in high-performance applications, such as jet engines, is assessed.
## 3 Analysis
### Economic Study  
The main aim of the life cycle analysis is to exhibit the economic feasibility of the dual-fluid PV/T system in comparison with single-fluid PV/T systems. The net present value (NPV) of a PV/T system is calculated using the annual discounted cash flow (DCF) parameter [1].  
\[NPV_{y}\ =\ \sum_{y\ =\ 1}^{n}DCF_{y} \tag{12}\]  
\[DCF_{y}\ =\ \frac{CF_{y}}{\left(i_{d}\ +\ 1\right)^{y}} \tag{13}\]  
where \(i_{d}\) is discount rate, \(CF_{y}\) is the cash flow for year \(y\) and it is calculated through:  
\[CF_{y}\ =\ E_{s,y}\ -\ MC_{y} \tag{14}\]  
where \(MC_{y}\) is the maintenance cost for year \(y\) and \(E_{s,y}\) is annual electricity saved per year by the PV/T system.  
\[MC_{y}\ =\ MC(1\ +\ i)^{y} \tag{15}\]  
where \(i\) is inflation and annual maintenance cost (\(MC\)) can be evaluated using the expression given by [PERSON] and [PERSON] [1]:  
\[MC=2.76\%\ of\ the\ PV\ cost+2.3\%\ of\ heat\ exchanger\ cost+0.75\%\ of\ pump\ cost \tag{16}\]  
\(E_{s,y}\) can be expressed as:  
\[E_{s,y}\ =\ \left[E_{el}(1\ +\ D_{el})^{y}+E_{th}(1\ +\ D_{th})^{y}\right] \times\ COE_{i}(1\ +\ i_{el})^{y} \tag{17}\]  
where \(E_{el}\) and \(E_{th}\) are the annual electricity saved by electrical and thermal loads, respectively. \(D_{el}\) and \(D_{th}\) are the annual degradation of electrical and thermal performances, respectively, of the PV/T system. \(COE_{i}\) and \(i_{el}\) are the domestic electricity cost and the inflation rate, respectively. The energy production factor (EPF) is calculated using the following expression:  
\[EPF\ =\ \frac{P_{lifetime}}{P_{ex}} \tag{18}\]  
where \(P_{lifetime}\) is the lifetime energy performance of the PV/T system and \(P_{ex}\) is the embodied energy or energy utilized by the PV module during its production processes. Based on the embodied energy, the net CO\({}_{2}\) mitigation over the system's lifetime can be calculated as:  
\[Net\ CO_{2}\ mitigation\ =\ (P_{tot}\ \times\ lifetime\ -\ P_{ex})\ \times\ 0.98\ \times\ 10^{-3} \tag{19}\]  
where \(0.98\ \times\ 10^{-3}\) is tons of CO\({}_{2}\) emission per kWh when generating electricity from coal [19]. Finally, the net CO\({}_{2}\) credit can be earned through carbon emission trading as follows [20].  
\[Net\ CO_{2}\ credit\ =\ Net\ CO_{2}\ mitigation\ \times\ 20 \tag{20}\]  
The net CO\({}_{2}\) is traded at 20 euro/ton [20].
# Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller
## 7 Results and Discussion
### Dynamic Analysis of the DSC vs. the NSC  
Firstly, supplying a 3 kW + j2 kvar grid-connected load is performed by the NSC and DSC-based converter RERs. Then, a sudden disconnection of the PV sources with 4 kW + j3 KVar happens at the PCC at \(t\) = 0.1, which is not instantaneously supplied by the DSC and the NSC until \(t\) = 0.25 s. After that, at \(t\) = 0.25, both the DSC and the NSC start their responsibilities toward the power grid stability. In this sub-section, three RERs as power converter-based PV sources are used.  
In this operating condition, the grid frequency of both the DSC and the NSC can be seen in Figure 12a. After a load increment at \(t\) = 0.1 s, the DSC does not allow the grid frequency to have more overshoot than the NSC, as depicted in Figure 12a. It happens for the dynamic response of the DSC and the NSC grid frequency when both the proposed controllers operate at \(t\) = 0.25 s, according to Figure 12a.  
The grid voltage magnitude due to the DSC and the NSC during this dynamic condition can be observed in Figure 12b. As can be seen, the high damping properties of the DSC can help the grid voltage not to see any significant drop, especially, compared with the NSC. It causes a grid voltage magnitude of the DSC to be kept in an acceptable region. Also, the DSC is able to present a fast-dynamic response in its operation time at \(t\) = 0.25 in which the grid voltage magnitude will follow its reference value with a slight transient response. Furthermore, the NSC does not have a fast-dynamic response in comparison with the DSC, and also it is not able to reach its desired value after \(t\) = 0.25 s, as shown in Figure 12b.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline dc-link Voltage (v\({}_{\text{dc}}\)) & 850 V & J\({}_{1}\) & 1e3 s \\ \hline Phase ac voltage & 220 V & Pm & 3.3 kW \\ \hline Fundamental frequency & 50 Hz & P & 3 kW \\ \hline Switching frequency & 10 kHz & Q & 2 kVAr \\ \hline Interfaced converter resistance (R1) & 0.1 Ohm & Interfaced converter inductance (L1) & 45 mH \\ \hline Interfaced converter resistance (R2) & 1 Ohm & Interfaced converter inductance (L2) & 15 mH \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 12: Comparison between the DSC and the NSC during disconnection of RER penetration: (**a**) grid frequency, and (**b**) grid voltage magnitude.
## 3 Group Decision-Making with Consensus Based on Subjectivity and Sentiment Analysis  
The newly designed group decision-making process and the associated consensus measures presented in this paper are shown in this section. Our method automatically extracts experts' preferences by analysing the transcripts of the discussion conducted. For this purpose, we use sentiment analysis. Then, using the preferences and the information provided by the sentiment analysis process, we identify the degree of consensus among opinions. We can also measure the degree of subjectivity that experts have when giving their opinions. Finally, Z-numbers allow us to calculate a degree of reliability on the extracted preference value. Thus, if an expert provides both positive and negative expressions about a given alternative, then the degree of reliability will be low. If the expressions associated with an alternative are all positive or negative, then the degree of reliability will be high. The method developed follows these steps:  
1. Decision-making process initialization: First, it is necessary to define the parameters of the decision-making process to be carried out. To do this, the topic of discussion, the set of possible alternatives, the participants, and the posts to be extracted are defined.
2. Selection of the data to be analysed: Next, we extract the data from social network X. We must identify which alternatives each text is referring to. To do this, we can make use of hashtags. Each text has the alternative and the expert it refers to assigned.  
Preference extraction: Using sentiment analysis techniques, we extract the experts' preferences from the texts. For this, we use two sets of words, one with positive and accepting expressions and the other with negative or rejecting expressions. We also use Z-numbers to obtain a measure of the reliability of the information obtained.
* Consensus calculation: Based on the text provided by the experts, the results of the sentiment analysis process and the preferences obtained, we calculate the consensus reached among the experts. For this purpose, we propose two measures, one that calculates the consensus based on the preferences obtained and another that is based on the subjectivity of the comments provided to the system.
* Final ranking of alternatives calculation: Once the consensus reached is high, we aggregate the preferences and, using the selection measures, the final ranking of the alternatives is calculated.  
The proposed method has been implemented in R and tested using real data obtained from the social network X using the X API. X is a very interesting social network for this type of analysis as each post is limited to 140 characters. Thanks to this, users of the network are forced to be specific and concise about their contributions. In Figure 2, we can see an outline of the process that the method carries out. In the following subsections, we will describe each of the mentioned steps in detail.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology  
SCT-IoT proposes a reduced set of interaction patterns to facilitate the development and interoperability of IoT systems. In order to provide the editing services of the SCM-IoT model in the BPEL service composition model, the producer-editor-subscriber pattern of interaction is designed, modeled, and implemented. The new constructs provided by the SCM-IoT model correspond to the new XML mark-up elements added.  
The representation of the extensions in CPN provides a theoretical framework to formulate and verify that the system has desirable properties such as progress, boundedness, and invariance. For the presentation of the proposed extensions, they are described with different levels of abstraction, from the conceptual level of the problem domain to the detailed level of its implementation as follows:  
* Conceptual model;
* Platform independent computational model;* Platform dependent computational model.  
The conceptual model defines the data model of the IoT system described by means of tables, including entries for data produced by sensors, for data monitored and modified by the editor, and for data to be notified to the actuators. In the platform-independent model, an abstract computational model such as CPN is used to formally describe both the tables designed in the conceptual model and the instances of the CPNs corresponding to the design patterns of SCM-IoT operations. Finally, in the platform-dependent model, an orchestration language such as BPEL is used to code the CPNs obtained from the platform-independent model. These models are described in more detail in the following subsections.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Applications of MFCs
#### 3.4.2 Wastewater Treatment
Additionally, the system promoted growth of _Geobacter sulfurducens_ and _\(\beta\)-Proteobacteria_ while inhibited Archaea growth in anode. [PERSON] et al. [225] found from their study that a coupled constructed wetland-\(\beta\) MFC system performs better in removing organic substances than normal constructed wetland systems. For a more detailed review of coupled constructed wetland and MFC system, please see the review by [PERSON] et al. [190, 226]. In [190], the authors particularly stressed the importance of maintaining anaerobic anode and oxygenated cathode configuration separated. [PERSON] and [PERSON] [226] indicated that constructed wetland systems naturally offer aerobic conditions in the upper layers and anaerobic in the deeper ones and results in a favorable environment for MFC power generation system implementation. [PERSON] et al. [191] identified high internal resistance as one of the limiting factors of such coupled systems. The researchers tested a new strategy, called capacitator engaged duty cycling, with an open air bio-cathode constructed wetland MFC system and obtained 19.81% more electric charge than the conventional continuous loading system. MFC integrated wastewater treatment processes have been used to remove sulfur [227], sulfide [106; 199; 228; 229], nitrogen [230], chromium [10] and salt components [172]. While metal contaminated waste possess great health and environmental risks, it also provides possibilities of precious metal recovery [231]. Combination of MFCs and microbial electrosynthesis have emerged as a method of choice for such metal recovery systems [30]. [PERSON] et al. [232] reported that heavy metal ions (for example Zn\({}^{2+}\), Ni\({}^{2+}\), Cr\({}^{2+}\), V\({}^{5+}\) or Co\({}^{2+}\)) are often present in industrial wastewater and can be extracted using similar combined configurations. [PERSON] e al. [233] reported a self-sustained combination of MFC and microbial electrosynthesis capable of extracting three heavy metals including chromium (Cr), lead (Pb) and nickel (Ni).
## 2 Related Work
### Data Classification and Grading Framework  
In the realm of data classification and grading, scholars typically commence their endeavors by establishing policies to ensure data compliance, which serves as a foundational step in constructing programs and frameworks for data classification and grading. For instance, [PERSON] et al. introduced a compliance analysis framework based on data source and data usage, aligned with the UK Data Protection Act [24; 25]. Their approach focuses on averting the misuse of personal sensitive data and evaluating the propriety of its utilization. However, they overlooked the potential risk stemming from the exposure of personal sensitive data under unforeseen circumstances. Subsequently, [PERSON] presented the Gen-DT scheme [26], which leverages legal statutes to establish an external knowledge base. They employed a generalization-enhanced decision tree algorithm to categorize data into regulatory and non-regulatory types. Nonetheless, this scheme solely dichotomizes data without specifying the sensitive classification and corresponding levels within regulatory data, which poses an inconvenience for implementing distinct protective measures for varying levels of sensitive data. Addressing the challenge of information extraction from regulations, [PERSON] constructed a knowledge graph by automatically extracting information from GDPR and PCI DSS [27; 28]. Building upon this, [PERSON]. [29] introduced the GENONTO framework, which autonomously extracts data classification and grading information from enacted regulations to construct a knowledge base. These frameworks expedite the extraction of classification and grading data from regulatory guidelines, facilitating their application in our module following calibration. Expanding beyond compliance considerations, academics have introduced additional metrics to optimize classification and grading outcomes. For instance, [PERSON] et al. [30] introduced data value evaluation indicators to enhance data grading results within the context of classification criteria. This optimization was assessed within the new energy automobile industry.
## 3 Results and Discussion
### Experiments and Discussion
This result provides a new idea for the research of AMC that other types of distance measurement functions can also achieve good results in addition to softmax. Finally, we compare our proposed model with some existing models, namely VTCNN [28], LSTM model which takes amplitude and phase information as input [30], ResNet [35], Dropout-replaced Convolutional Neural Network (DrCNN) [37], CLDNN [35], Improved convolutional neural network based automatic modulation classification (IC-AMCNet) [36], and ODAC Network [44]. The result is shown in Figure 11. According to our experimental results, our proposed hybrid parallel network is significantly superior to others. The highest recognition accuracy exceeds 93% at 8 dB, and the highest accuracy of other methods cannot exceed 85%. The lowest accuracy of our method exceeds 75% at \(-4\) dB; however, VTCNN and LSTM go a little bit beyond 75% at 18 dB. The average accuracy of our model is at least 5% superior to others. The best performance of other methods is CLDNN which combines CNN with LSTM; thus, our proposed method and CLDNN show that combining CNN with RNN can improve the classification performance. ResNet also achieves good results among these other methods, which indicates that residual structure can also improve classification performance. Our proposed method combines the advantages of these methods and achieves the best among all methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**s** & 1 & 2 & 3 & 4 & 7 & 10 & 13 & 16 & 19 \\ \hline
**accuracy(\%)** & 81.10 & 82.46 & 83.96 & 90.76 & 90.78 & 89.95 & 90.77 & 90.35 & 90.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of AM-softmax with different scales. Figure 10: The comparison between AM-softmax and softmax.
# Coordinated Control after Grasping the Space Targets Using Controllable Damping Mechanism
## 2 Modeling of Spacecraft-Manipulator System with FDCDM  
The full-dimensional controllable damping mechanism (FDCDM) with 4-DOF is shown in Figure 1[20]. In this paper, the spacecraft-manipulator system is composed of a three-dimensional base (6-DOF) and 2-DOF onboard manipulator. Therefore, there will be 12-DOF for the spacecraft-manipulator system with FDCDM (SMS-FDCDM), as shown in Figure 2.
## 3 Results
### Nanoindentation Measurements  
Load-displacement curves were recorded for each membrane before and after PRGF treatment and presented comparatively in Figure 4. According to the load-displacement profiles, in the case of Jason@ membrane, a slight increase occurred for each applied load to reach the same indentation, when the membrane was treated with PRGF (Figure 4a-b). A significant modification occurred in the case of Biocollagen@ membrane, as almost double the applied load was required in order to reach the same indentation (Figure 4c-d). With respect to CovaTMMax membrane, a slight decrease of the applied load was necessary to get the same indentation (Figure 4e-f).  
Figure 4: Nanoindentation measurements: load–displacement curves recorded for each membrane before (**a**,**c**,**e**) and after (**b**,**d**,**f**) PRGF treatment. The images correspond to Jason@ (**a**,**b**), Biocollagen@ (**c**,**d**), and CovaTMMax (**e**,**f**). Legend: MC1/MC2 = Jason@ membrane before/after PRGF treatment; MCP1/MCP2 = Biocollagen@ membrane before/after PRGF treatment; MPP1/MPP2 = CovaTMMax membrane before/after PRGF treatment.  
Comparing the data related to the non-treated membranes, one can observe good fitting results, with low scattering data, in the case of \(\mathrm{Cova}^{\mathrm{TM}}\mathrm{Max}\), as revealed in Figure 5. This type of membrane also exhibited higher stiffness (\(E=4\) GPa), while Biocollagen(r) was the softest one by comparison (\(E=1\) GPa). However, after the PRGF treatment, a significant increase in Young modulus value was observed for Biocollagen(r), a moderate increase for Jason(r), and a small decrease for \(\mathrm{Cova}^{\mathrm{TM}}\mathrm{Max}\). Overall, after the PRGF treatment, the nanoindentation measurements indicated the Young modulus value ranging in the interval 2.8-4 GPa in all the cases (Figure 5).
# 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
#### 4.2.2 2,2-Diphenyl-l-picrylhydrazyl (DPPH) Assay  
The DPPH assay is a mixed-mode HAT/SET, used to determine the antioxidant capacity. DPPH is a free radical that has a deep purple color; it is one of the few stable organic radicals. The assay is based on the reaction of the DPPH radical with an antioxidant, e.g., phenolic compounds, which generates a reduced molecular form of the radical, and results in the loss of the purple color. Thus, the decrease in absorbance at 515 nm depends on the concentration of the antioxidant. Trolox is used as standard in calibration and the results of antioxidant capacity are expressed in terms of Trolox equivalent (TEAC) concentration [72; 73; 74; 75]. [PERSON] et al. [76] evaluated the antioxidant capacity by DPPH in ethanol/water extracts and aqueous suspensions of grape pomaces from four different Portuguese grape varieties. The highest DPPH values were found in Touriga Nacional grape cultivar with 1.09 \(\pm\) 0.13 and 1.12 \(\pm\) 0.04 mmol TEAC g\({}^{-1}\) dry residue, in ethanol/water extracts and aqueous suspensions, respectively.