esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Element Maintenance Decision Model
#### 4.2.3 Maintenance Effectiveness Matrix  
For the 21 elements of the case study bridge, the 4 maintenance strategies described in the previous section were implemented individually. The changes in state before and after maintenance were recorded and are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Number of** & **Number of** & **Number of** & **State after** & **State after** & **State after** \\  & **Concrete** & **Exposed** & **Surface** & **Surface** & **Maintenance** & **Implementing** & **Implementing** \\  & **Cracks** & **Concrosion** & **Defects** & **A1** & **A2** & **A3** \\ \hline R-8-1 & 0 & 0 & 3 & 2 & & & 1 \\ R-2-1 & 0 & 2 & 0 & 3 & & 1 & \\ R-5-1 & 4 & 0 & 0 & 3 & 1 & & \\ R-6-1 & 2 & 0 & 0 & 3 & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the technical conditions after different maintenance schemes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Maintenance Plan** & \multicolumn{2}{c}{**Maintenance Measures and Costs**} \\ \cline{3-4}  & **Description** & **Number** & **Maintenance Measure Description** & **Unit Cost (USD)** \\ \hline A\({}_{0}\) & No maintenance & / & / & / \\ \hline \multirow{2}{*}{A\({}_{1}\)} & Repairing concrete & a\({}_{1}\) & Automatic low-pressure grouting & 13.66 \\  & cracks in elements & a\({}_{2}\) & Pressure grouting & 15.55 \\ \hline \multirow{4}{*}{A\({}_{2}\)} & Repairing exposed & b\({}_{1}\) & Manual derusting + Reinforcement protective agent + Manual diseling + Cleaning + Polymer cement mortar & 35.71 \\  & reinforcement & & Manual derusting + Reinforcement protective agent + Manual diseling + Cleaning + Polymer cement mortar & 44.15 \\  & corrosion in elements & b\({}_{2}\) & Manual derusting + Reinforcement protective agent + Manual diseling + Cleaning + Polymer cement mortar & 44.15 \\  & & c\({}_{1}\) & Manual diseling + Cleaning + Polymer cement mortar & 23.89 \\ \cline{2-4}  & Repairing surface & c\({}_{2}\) & Manual diseling + Cleaning + Cast-in-place repairs & 133.50 \\  & defects in elements & d\({}_{1}\) & Manual diseling + Cleaning + Reinforcement protective agent + Polymer cement mortar & 35.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maintenance strategy set of prestressed reinforced concrete box girders.  
By substituting the statistical results from Table 4 into Equation (2), the maintenance effectiveness matrix can be obtained as shown in Equations (19) to (22).  
\[M_{A_{0}}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{bmatrix} \tag{19}\]  
\[M_{A_{1}}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&0&0\\ 0.75&0.25&0&0&0\\ 0.08&0.17&0.33&0.42&0\\ 0&0.33&0&0.67&0\end{bmatrix} \tag{20}\]  
\[M_{A_{2}}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&0&0\\ 0&0&1&0&0\\ 0.08&0.25&0.67&0&0\\ 0&0&0&1&0\end{bmatrix} \tag{21}\]  
\[M_{A_{3}}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&0&0\\ 0.25&0&0.75&0&0\\ 0&0&0.17&0.83&0\\ 0&0&0&0.67&0.33\end{bmatrix} \tag{22}\]
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
## 3 VSG Control
### Two-Stage Circulating Current Suppression Strategy  
In the MMC-RPC system, the capacitor voltage balance control of the submodule can maintain the balance of capacitor voltage. However, the frequent charging and discharging  
Figure 4: Control chart of virtual synchronous generator.  
Figure 5: Control chart of MMC-RPC.  
of capacitors still make the voltage fluctuate, which leads to the instantaneous energy imbalance between the bridge arms and the double frequency circulation. And the circulating current will flow into the DC component, causing voltage and current fluctuations and reducing the reliability of the MMC [28; 29]. Therefore, Quasi Proportional Complex Integral control (quasi-PCI, QPCI) and improved VSG stator electrical equation are established to suppress the circulating current in the system.  
For the circulating current in the bridge legs of the MMC-RPC, QPCI controllers are used on both sides of the MMC to suppress it. QPCI has a better response speed than the Quasi Proportional Resonance control (quasi-PR, QPR), and it can maintain a higher accuracy in the case of frequency fluctuations.  
The circulation current of bridge legs in MMC-RPC is [28]:  
\[i_{\text{cir}}=\left(i_{\text{kp}}+i_{\text{jn}}\right)/2-I_{\text{dc}}/2, \tag{16}\]  
The transfer function of the QPCI controller is shown in Equation (17), and its control block diagram is shown in Figure 6[26]:  
\[G_{\text{QPCI}}(s)=K_{\text{P}}+\frac{K_{\text{I}}\omega_{\text{C}}}{s-\text{ j}\omega_{0}+\omega_{\text{C}}}, \tag{17}\]  
As shown in Figure 7, when there is a large double frequency circulation at the bridge leg, the double frequency component between the bridge legs is quickly and fully suppressed after the circulation suppression control is put into operation. Among them, when QPR is put into operation at 0.65 s, circulation suppression is achieved at 0.75 s, while QPCI suppresses circulation at about 0.7 s. The adjustment time under QPCI is shorter.  
The output current is feedback to the input of the stator electrical equation module for the traditional VSG. When the load is unbalanced, the input current and the expected output value may have a double frequency AC component, aggravating the voltage imbalance.  
Figure 6: The control structure of circulating current suppressing.  
Figure 7: Simulation waveforms of double frequency circulating current suppressing effect.  
In this paper, a PI and QPR compound control strategy is applied to the stator electrical equation module of the VSG, to realize the accurate tracking of the DC component and the zero-error tracking of the AC component of double frequency.  
The improved VSG control strategy is shown in Figure 8:  
Figure 9 shows the suppression effect of the double frequency circulating current component in the QPR link of the stator electrical equation; it can be seen from the figure that the improved composite PI-QPR control can realize fast and effective suppression of the double frequency circulating current component compared with the unimproved VSG control.
# Formation of Conductive Networks in Polysulfone Filled with Graphite-Derived Materials
###### Abstract  
Composites based on polysulfone materials filled with various types of graphite and carbon black were studied and a solution technique was used to create composite materials with filling degrees ranging from 30 to 70% by weight. High filling levels with graphite fillers enabled thermal conductivity of 17.4 W/m\(\cdot\)K to be achieved. The addition of carbon black as a filler to the composite helped to improve its mechanical characteristics, and its influence on electrical and thermal conductivity has also been explored. Natural graphite provides the best effect on composite thermal and electrical properties, whereas using artificial graphite allows to improve the mechanical behavior of composites. The relationship between sample density, porosity, and composition, as well as the effect of these factors on composite conductivity, has been studied and addressed.  
composite material; thermoplastics; polysulfone; carbon black; natural graphite; artificial graphite; electrical conductivity; mechanical properties; thermal conductivity +
Footnote †: journal: Applied sciences
# Volatile Profiles of Sparkling Wines Produced by the Traditional Method from a Semi-Arid Region
## 1 Introduction  
Wine sector is an important socioeconomic activity for the Southern and Northeastern regions of Brazil. The Sao Francisco Valley (SFV) is located in the Northeast, between the 8-9\({}^{\circ}\) parallels in the South Hemisphere, in a tropical semiarid climate. Climate conditions allow the grape production to be scheduled throughout the year, due to high temperatures, high solar radiation and water availability for irrigation, and one vine is capable of producing two crops annually [1; 2; 3]. Products from this region are classified as tropical wines [4].  
Sparkling wines comprise the greatest part of the current commercial production in the SFV and it is produced only by Asti and Charmat methods. These wines represent approximately 70% of thetotal production of fine wines (from _Vitis vinifera_ L.) in this region, while red wines represent 29%, and white wines 1% of the total [3]. The main varieties being used for white sparklings are Chenin Blanc, Verdejo, Sauvignon Blanc and Viognier, while rose sparklings are produced using Syrah, Tempranillo and Grenache, in both cases using the Charmat method [3; 5].  
In general, the typicality and the chemical and sensory characteristics of wines are depending on factors such as climate, soil type, grape variety, and winemaking process. These elements comprise the set of effects described as the terroric [6]. In the SFV, wineries use several grape varieties, of which Syrah and Chenin Blanc are the most adapted to the local conditions [3; 5]. Even wines in the region have been produced for more than three decades, commercial sparkling wines are not produced by traditional method (\"Champenoise\"). By this method, sparkling wines are produced with two consecutive fermentations, resulting more complex products with higher added value [6; 7].  
The aroma is one of the most important compound linked to wine quality, influencing its typicality and acceptability. The aromatic profile of sparkling wines from traditional method is associated to volatile compounds belonging to esters, alcohols, acids, and some terpen, in a very complex matrix [8; 9; 10].  
The study of the volatile composition of sparkling wines can lead to a better understanding of their quality and typicality, highlighting the possibilities for adjustments in the production process and final composition for commercial products. It can reveal the distinct and unique characteristics of regional wines, associating them to their geographic origin [9]. In Brazil, studies have been carried out to characterize volatile composition of wines produced in the Southern region [8; 11; 12; 13; 14], however, no study reported about volatiles in traditional sparkling wines from the SFV.  
In this context, the objective of the study was to characterize the volatile profiles of sparkling wines produced by the traditional method, using the Chenin Blanc and Syrah grapes grown in the SFV, a tropical semi-arid climate in Brazil.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Energy Model in ns-3  
This subsection establishes the _energy model_ in ns-3, as explained in [40]. Since the energy model was developed for ns-3 and not for ndnSIM, the model can be applied to regular, non-NDN nodes, too. The energy model consists of three main components: _energy consumption_, _energy sources_, and _energy harvesting_. These submodules are investigated next.  
An _energy source_ (class EnergySource) represents the power supply of a node, measured in joule. A node can have one or more energy sources, and an energy source can be connected to one or more device energy models (explained next). The subclass BasicEnergySource is the most general model and is therefore suitable for the simulations done in this work. It increases and decreases the remaining energy linearly.  
_Device energy models_ (class DeviceEnergyModel) are state-based models, where each state is associated with a specific power consumption value. Such states are different Wi-Fi states, in particular, idle, transmit, receive, and sleep. States with a higher current also have a higher power consumption because power is proportional to electric current. In order to simulate Wi-Fi energy consumption, we use the class WiFiRadioEnergyModel, one of the subclasses of DeviceEnergyModel. Unfortunately, ndnSIM does not support an energy model for P2P connections (e.g., Ethernet), so we have to limit our measurements to Wi-Fi connections. The only topology in which non-Wi-Fi connections occur is the connected-star topology. Here, we assume the edges between the center nodes to be stationary P2P connections, which do not have to use Wi-Fi. So, energy consumption on these nodes is less relevant than for all the other nodes.  
Finally, an _energy harvester_ (class EnergyHarvester) enables an energy source to recharge itself. However, analyzing networks with such capability is beyond the scope of this work since that would significantly increase the complexity of the scenarios.  
Now that the implementation of the critical parameters has been demonstrated, we show how grid search was implemented to iterate combinations of parameters efficiently.
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
## 2 New Electro-Hydraulic Servo Point Load Instrument
### Key Technologies  
In view of the actual problem of the discontinuous loading process for conventional point load instruments, the technical research is carried out as follows:  
1. Develop a miniaturized electro-hydraulic servo loading system; break through the precise control technology of the battery circuit; realize multichannel capability, small flow rate, and high-pressure stable output under smaller volume and lighter weight, with the ability to intelligently detect pressure changes and maintain the target pressure; and create a loading rate that is stable and adjustable. Electro-hydraulic servo point loaders for the first time achieve variable-rate, continuous, and uniform loading, thus reducing test data dispersion.
2. The test data are uploaded to the cloud platform in real time through 5G technology, with instant test results and instant transmission back to the cloud server.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Technical Parameters** \\ \hline Loading-rate range (kN/s) & 0.1–2.0 \\ Rock sample clamping thickness (mm) & 10–60 \\ Maximum loading force (kN) & 50 \\ Oil pressure sensor measurement accuracy (MPa) & 0.01 \\ Case size (mm) & \(610\times 380\times 220\) \\ Weight (kg) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main technical parameters of the new electro-hydraulic servo point load instrument.  
Figure 1: Physical view of the new electro-hydraulic servo point load instrument.
## 3 Results
### Cost Analysis  
Based on the previously acquired results, for the improvement of clay soil with the addition of CaCO\({}_{3}\) at an optimum ratio of 5% or nano-CaCO\({}_{3}\) at 0.75%, as an example, 0.05 m\({}^{3}\) CaCO\({}_{3}\) or 0.0075 m\({}^{3}\) nano-CaCO\({}_{3}\) is needed for 1 m\({}^{3}\) of clay soil. From an economical point of view, 0.582 USD (United States Dollar) is needed for the usage of plain CaCO\({}_{3}\) while only 0.112 USD is needed for nano-CaCO\({}_{3}\) (Table 5). Therefore, it was deduced that the implementation of the nano version of the same product stands out to be the better alternative in terms of both the economic aspects and the utilization of resources.
## 3 Laboratory Experiment
### Grouting Ratio Experiment
Figure 6 shows the bleeding rate of the cement slurry in the test. The bleeding rate of the slurries with different water cement ratios gradually increased to a certain value over time and then became stable, and the larger the water-cement ratio, the greater the bleeding rate of the slurry. When the water-cement ratio increased from 0.4 to 1, the bleeding rate increased from 0.1% to 22.5% at 140 min, an increase of 22.4%, and the bleeding rate reached a stable level at about 50 min. When the water-cement ratio was 0.4, the bleeding rate was always maintained at 0.1%, indicating that the sample almost did not separate water. When the water-cement ratio was 0.6, the bleeding rate increased from 1% to 3.9% at 140 min. When the water-cement ratio was 0.8, the bleeding rate increased from 1.8% to 15% at 140 min. When the water cement ratio was 1, the bleeding rate increased from 3% to 22.5% at 140 min, indicating that the sample had strong water separation. Figure 7 shows the compressive strength of cement samples under different water-cement ratios. The sample strength decreased linearly with the increasing water-cement ratio. When the water-cement ratio increased from 0.4 to 1.0, the strength decreased from 9.8 MPa to 3 MPa, which decreased by about 30.6%. The fitting relationship between strength and water-cement ratio is shown below:  
\[f_{\text{c}}\ =\ -11.089\eta+14.313, \tag{2}\]  
where \(f_{\text{c}}\) is the strength of the cement sample (MPa), and \(\eta\) is the water-cement ratio. The correlation coefficient R\({}^{2}\) of the fitting result was 0.993. Thus, the fitting result was good. Figure 5: Relationship between water–cement ratio and initial setting time. The water-cement ratio has different effects on the viscosity, bleeding rate, and initial setting time of cement slurry, and the reasonable water-cement ratio cannot be determined only by experiments--the influence of onsite conditions should be considered. To obtain a reasonable water-cement ratio, further numerical simulation research was performed.
## 3 4-SSB System Design
### Four Single-Sideband Modulation  
This section describes 4-SSB modulation model using M-QAM [10; 11]. The proposed model uses the ideal Hilbert transform of the baseband signal \(x\). The analytical mathematical model of the upper sideband generated from baseband signal \(x\) denoted as \(x_{\text{USB}}\), can be expressed as:  
\[x_{\text{USB}}(t)=\frac{1}{2}[x(t)+j\hat{x}(t)]. \tag{8}\]  
Since the lower sideband is similar to the analytical value of upper sideband but with a different sign, we can define \(x_{\text{LSB}}\) it in the same way as of the upper side:  
\[x_{\text{LSB}}(t)=\frac{1}{2}[x(t)-j\hat{x}(t)]. \tag{9}\]  
These two expressions lay down the concept of SSB modulation which was used for generating 4-SSB modulation in our prior work [10]. Figure 2 shows the model configuration of 4-SSB in which the 4-SSB modulating expression can be illustrated by considering four independent real discrete sequences, denoted by \(u,v,p\), and \(r\). The Hilbert transforms of original discrete signals for the designed modulation system are also described in Figure 2.  
Specifically, the combined signal is transformed by the Hilbert transform, and in I/Q modulation of two symbols, the imaginary part is decided as the result of the Hilbert transform. The 4-SSB signal is generated by these four signals corresponding to the following equation:  
\[S_{\text{4 SSB}}=S_{\text{4 SSB,I}}+jS_{\text{4 SSB,Q}}, \tag{10}\]  
where \(S_{\text{4 SSB,I}}\) and \(S_{\text{4 SSB,Q}}\) denote the In phase 4-SSB and quadrature modulation phase 4-SSB, respectively (Figure 2), in which:  
\[S_{\text{4 SSB,I}}=u-\hat{v}+p+\hat{r}, \tag{11}\]  
\[S_{\text{4 SSB,Q}}=-\hat{u}-v+\hat{p}-r, \tag{12}\]  
where \(\hat{u},\hat{v},\hat{p}\) and \(\hat{r}\) denote the Hilbert transforms of original discrete signals.  
From the previous work of 4-SSB-based modulation technique over QPSK, these equations can be applied for two binary phase shift keying (BPSK) signals or two complex modulated signals like QPSK and QAM. As a result, the two symbols \(d_{1}\) and \(d_{2}\) of the M-QAM can be represented as follows:  
\[d_{1}=u+jv, \tag{13}\]  
Figure 2: The Four Single-sideband (4-SSB) Modulation Model.  
\[d_{2}=p+jr. \tag{14}\]  
The 4-SSB signal then occupies the two-complex signal bandwidth as of \(d_{1}\) or \(d_{2}\) but carries twice amount of information. This means that the 4-SSB 16-QAM has the same information as of 64- QAM or the like for the high-order modulation.
## 2 A Rapid Output Matching Optimization Method with Dual-Objective Orientation
### Determination of the Optimal Impedance Domain
Since \(g_{\text{m3}}\) is determined by the IMD3 products shown in Equations (6) and (7), the IMD3 value is also related to the load impedance [27]. Figure 2 depicts the IMD3 load-pull contours for a two-tone sinusoidal signal with a power level around the 1 dB compression point, a center frequency of \(f_{\text{i}}\), and a 100 MHz tone spacing. As can be observed, the contours enclose a relatively wide impedance domain that extends into the lower half of the Smith chart and shifts toward the short-circuit point as the frequency increases. The IMD3 contour of \(-22\) dBc at 32 GHz passes through \(Z_{\text{opt,s}}(f_{\text{i}})\) with the maximum imaginary part, overlapping with the previously identified P-OID to form a strict OID (S-OID) constrained by four performance metrics. Importantly, although the OID is progressively reduced by an increasing number of constraints, it is purposely maintained to encompass all \(Z_{\text{opt,s}}(f_{\text{i}})\) to ensure the consistency and compatibility of the optimization objectives.
## 5 Characterization Using the Time Domain Measurement Data
### Modeling of Time-Varying Characteristics  
In the welding shop, electromagnetic noise occurs and disappears due to the work conditions of the mechanical equipment. The continuous hidden Markov model is a commonly used method for statistical modeling of non-stationary signals, such as speech signals. We used the continuous hidden Markov model (CHMM) to characterize the time-varying characteristics of the electromagnetic noise.  
We consider that there are two states (state 1 and state 2) of electromagnetic noise in the factory. State 1 and 2 represents the equipment is not working or is working, respectively. We assume that both states are Gaussian distributed variables and the mean and variance are \(\mu_{1}\), \(\sigma_{1}\), \(\mu_{2}\), and \(\sigma_{2}\), respectively. Thus, a two-state first-order CHMM can be used to characterize the generation and extinction behavior of the electromagnetic noise in industrial scenarios.
# Characterization of Asphalt Binders Modified with Bio-Binder from Swine Manure
## 2 Materials and Methods
### Materials
#### 2.1.2 Bio-Binder Production  
Bio-binder was produced through a hydrothermal liquefaction (HTL) process applied to swine manure collected from a pig farm located in the Anoia region, Spain. It initially had 70% water content and was stored in a freezer at \(-15\)\({}^{\circ}\)C. For its further characterization and processing, it was diluted in water to a total solids content of 5%. For each batch, 500 g of swine manure were introduced into a 1 L stainless steel autoclave with heated casing and a rotary impeller system without magnetic coupled packing. The reactor was heatedto a temperature of 300 \({}^{\circ}\)C and a pressure of 9.0 MPa for a residence time of 0 min. The processing gas used to purge the residual air in the reactor was nitrogen, and no catalyst was used in the process. After the reaction, the heating shell was removed, and the reactor was cooled down to room temperature using water.  
The different phases of the HTL process were defined. The gaseous phase was measured by a gas flow meter and afterwards collected and stored in plastic bags for later analysis. The aqueous phase was collected by pouring it into a flask. Part of the solid phase, which was contained in the aqueous phase, was also collected in the previous step after filtration. The rest of the solid phase, together with the biocrude, was stuck to the walls and to the agitation module of the reactor. Acetone was used to recover them and to clean the reactor. The solids were washed with acetone to remove biocrude to obtain the bio-binder. Acetone was afterwards evaporated with a rotary evaporator.  
The organic elemental analysis of the obtained bio-binder is detailed in Table 2.