esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Paper Contributions
### Related Work
The author proposes a loss-recovery enhancement for the Write-Back (WB) database access mechanism to improve the successful call setup probability. In [37; 38], the authors use alarms (the traditional method) to detect the failures. The authors in [39] propose a method to enable recovery from the failure of a Proxy Call Session Control Function in an IMS network. This is conducted through three steps: (i) monitoring the gateway of the packet switched access network used by User Equipment (UE) to access the IP Multimedia Subsystem network, (ii) providing an indication in the gateway if the monitored signals become unacceptable and (iii) performing an action in the gateway, in case the response to the indication is the unavailability of the P-CSCF. This action is sending a message to each UE that is associated with the P-CSCF. In [40], the authors propose a recovery method from the failure of a Serving Call Session Control Function (S-CSCF) within an IMS network. The Proxy Call Session Control Function (P-CSCF) receives a SIP request from the UE for which a given S-CSCF was selected. If the S-CSCF has failed or is unreachable, a reregistration message is sent from the P-CSCF to the UE, forcing the UE to perform an IMS registration procedure with the IMS network. According to [41], the failure recovery in virtual IMS (vIMS) takes up to tens of seconds. Therfore, the authors aim to achieve a high level of resilience in vIMS by continuing to serve existing and new service requests during failures. The authors provide session-level resilience to both control-plane and data-plane operations. This is conducted by enabling every IMS NF to take charge of its directly associated neighbor when it fails. In [42], the authors use the advantage of a big data processing capacity of the core network to connect UE and multi interfaces' big data together to perform call analysis. The paper describes the connection method of UE signals, the multi interfaces' signals of the Evolved Packet Core (EPC) network and the IMS network, to solve the problem of the VoLTE call not being connected. However, in the aforementioned references, using machine learning to detect the failures and conclude the failure root causes, are not investigated.
## 3 Proposed Trust-CH Framework  
Trust-CH's proposal consists of three main phases. The trust-building phase between WSN nodes, the CH selection phase and the security adaptation phase. In the trust-building phase, the CH nodes monitor the trust with the nodes associated with them and then pass the trust file between them (CH nodes). When a node reaches Absolute-Trust, it is nominated to be among the options for selecting the main nodes (CHs) with other factors. The main objective of rotating the clustering heads is to maintain a balanced power consumption and ensure the QoS. The Security adaptation phase also aims to reduce the cost of encoders for power, based on the amount of nodes trust. Figure 2 shows the overall diagram of the Trust-CH.  
From Figure 2, the communication between nodes based on two security frameworks. The first is the proposed model (Trust-CH), through which the CHs communicate with each other and with nodes with a high Trust value. The other model is FlexCrypt [26], which we modified by fixing the number of Rounds and Block size for each encrypted packet. Table 2 shows the notations and their description used in the scheme.  
In trust-based adoption operations, the trust level of a WSN node increases based on the definition of its neighbors. Therefore, in our proposal, a table (T) is used to store the trusted WSN nodes and their respective level. Furthermore, each WSN node shares its table between other WSN nodes in each update by joining, modifying, exiting any WSN node. The CH node updates its table, building the information of each WSN node associated with it and how often it is specified in the other CHs. If a WSN node is active and passes through more CHs or creates new CH groups, the level of trust increases and if its activity decreases, the level of trust decreases. Likewise, if it is missing from the update for extended periods, it will be deleted from the table. Figure 3 shows the composition for the table used.  
Figure 3: Trust-table for each WSN node.  
Figure 2: The Trust-CH framework.
# Bio-Sourced and Biodegradable Membranes
## 3 Biodegradable Antifouling Membrane Properties
### Polyvinyl Alcohol (PVA) Membranes  
Polyvinyl alcohol (PVA) is another biodegradable, non-toxic polymer with inherent hydrophilicity and antifouling properties. PVA is one of the most favorable polymers that can be used for membrane fabrication and exhibits high mechanical, thermal, and pH stability [143, 147, 148, 149, 150]. PVA membranes are useful for wastewater treatment applications due to their oleophobic and highly polar nature, which minimizes foulant adsorption and the adhesion between the membrane surface and water contaminants [143, 147, 151, 152]. Hydrophilic membranes appear to be less prone to fouling formation, especially toward organic matters like proteins in water, and it is much easier to clean the attached particles or recover the permeate flux [153]. Such membranes absorb water molecules much more easily, resulting in the formation of a hydration layer that protects the membrane from particle accumulation [114, 117]. [PERSON] et al. [153] evaluated the protein fouling of PVA ultrafiltration membranes using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). They compared their results with fouling formation on polyethersulfone (PES) as one of the most commonly used ultrafiltration membranes. According to their findings, PVA membranes had a higher resistance to protein fouling. PVA membranes can also be used for oil/water separation. According to [PERSON] et al. [154], the flux recovery ratio of ultrafiltration PVA membrane was about 95% after washing the fouled membrane with pure water, which demonstrates the excellent antifouling properties of the membrane toward the oily wastewater. [PERSON] et al. [155] also fabricated a high performance PVA-based composite membrane for oil/water separation. As the support layer, they prepared a PVA nanofibrous scaffold and coated it with PVA hydrogel as the anti-fouling layer. The hydrophilic nanofibrous membrane was found to have a filtration efficiency of more than 99% with no flux decay over the filtration time, which was similar to the commercial UF membrane with PeBax 1074 coating. However, the permeate flux of the prepared membrane was more than twofold that of commercial UF membrane (about 130 LMH for electrospun composite PVA membrane and 60 LMH for commercial UF membrane). Similar to chitosan, PVA has been employed in several studies to fabricate biodegradable fouling-resistant composite membranes [156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176].
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 3 Fractional Frequency Reuse  
A mobile network usually offers coverage to its subscribers with cells that have a limited frequency band for transmitting and receiving signals. In order to use the spectrum efficiently, a reuse factor of 1 is commonly applied, which means that all of the cells use the entire available bandwidth.  
In order to mitigate inter-cell interference, some networks increase the reuse factor. For example, a reuse factor of 3 creates a topology where adjacent cells do not share bandwidth, as illustrated in Figure 1. However, there is a significant loss in spectral efficiency, given that only 1/3 of the original bandwidth is available to each cell.  
Fractional frequency reuse splits the cell into distinct regions with different reuse factors and transmission power. The goal is to improve the SINR, increasing the reuse factor without compromising the spectral efficiency. Users in the cell edge are usually the target of FFR-based techniques, since they are the most affected by co-channel interference.  
The following section presents some FFR-based techniques, known in the 3 GPP as ICIC techniques. These algorithms were selected because of the following reasons. They are standardized for LTE by 3 GPP (but not limited to it, since they can be applied to other OFDMA-based systems, such as 5G NR), they are already implemented in the simulator used in this paper [19], and they represent a good range of ICIC strategies based on FFR that can be found in deployed networks [20]. The algorithms are fully described in [20; 21; 22].
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 3 Results and Discussion
### Validation with Experimental Data
#### 3.2.3 Effect of Make-Up Flow on Reaction Rate  
In Figure 9, reaction rates (in mol m\({}^{-3}\) s\({}^{-1}\)) are shown over a temperature range from 600 \({}^{\circ}\)C to 850 \({}^{\circ}\)C. Additionally, a variation of the make-up flows (0.2 kg/h, 6.6 kg/h, and 15 kg/h) was included to investigate the influence of sorbent deactivation on all considered reaction rates for the gasification process.  
As the gasification temperature varies with changes of the circulation mass flow, the sorbent residence time differs and, according to Equation (35), the sorbent activity is also affected. It can be seen that deactivation mostly influenced the carbonation (reaction 4) and water-gas shift (reaction 5) reactions. For instance, for a constant temperature and a constant circulation mass flow, the carbonation reaction rate is higher with larger amounts of fresh limestone.  
Considering the influence of the gasification temperature on the reaction rates, it can be seen that the reaction rates of the water-gas shift (reaction 5), the heterogeneous water-gas (reaction 2), and the Boudouard (reaction 3) reactions increased with higher temperatures. In contrast, for the pyrolysis (reaction 1) reaction, a minor decrease can be observed, even though an increase should be expected with higher temperatures. The reason for this behavior can be explained as follows: The amount of biomass in the fluidized bed system is limited by a constant fuel input flow. When the gasification temperature is increased, the reaction rate increases but, at the same time, a higher solid circulation is required for the higher temperature. Hence, more unreacted biomass is extracted from the gasifier through the loop seal, thus reducing the gas yield. Kinetic parameters of the Arrhenius approach influence the gradient of reactions for increasing temperatures. Comparing the Arrhenius parameters listed in Table 1, it can be seen that the influence of temperature on the heterogeneous water-gas shift reaction (reaction 2) was higher than that of the pyrolysis reaction (reaction 1).  
Figure 9: Reaction rates over temperature; influence of effective make-up of fresh limestone (lines for 0.2, 6.6, and 15 kg/h) to bed activity.
###### Abstract
Furthermore, the WTDO was only performed on an individual wind turbine rather than considering the whole wind farm, thus the achieved results may be unrealistic. Currently, offshore wind turbines are designed to fit the large-size trend of high capacity and long blades and, thus, the issue of the high cost of energy is increasingly prominent. This paper aims to obtain the parameters of a cost-effective offshore wind farm. That is, a wind farm with a minimum COE. With regards to the literature, the contributions of this paper are summarized as follows. Firstly, this was the first study to address the COE optimization of wind farms by simultaneously optimizing the design parameters and the layout of wind turbines. Secondly, it established a mathematical description of the COE as a function of two design parameters of wind turbines and their layout, which can be extended to include more design variables. Thirdly, it proposed a composite optimization approach comprising an iterative algorithm and an improved particle swarm optimization (IPSO). As the concerned optimization issue involves two types of design variables, turbine design parameters and turbine layout, and the latter variable depends to some extent on the former, a single optimization approach may not be efficient to solve the issue. In the proposed approach, the iterative algorithm was the main optimization loop used to find the optimal wind turbine parameters, while the improved particle swarm optimization was a sub-optimization loop used to help the construction of the optimized layout. By doing so, the optimal solution could be conveniently determined. Finally, the utilization of the proposed approach was illustrated through three real offshore wind sites, which can be used as references for offshore wind farm designers. The remaining sections are organized as follows: The COE model of offshore wind farms is discussed in Section 2, and Section 3 presents the method for optimizing COE by selecting the optimal wind speed, rotor diameter and the turbine layout. The case studies are described in Section 4 and, finally, Section 5 concludes the study.
## 3 Results and Discussion  
### Effect of Different Water Invasion Energies  
Water supplies an extra mechanism to produce gas reservoirs, and it is important to understand the effects of water energies [22; 23]. The core 4 is chosen to study the effects of different water invasion energies. The gas production rate and original saturation pressure are 1000 mL/min and 30 MPa,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NO.** & **Length (cm)** & **Diameter (cm)** & **Permeability (mD)** & **Porosity (\%)** \\ \hline
1 & 10.105 & 10.438 & 0.001 & 4.10 \\
2 & 9.807 & 10.021 & 0.021 & 4.30 \\
3 & 10.156 & 10.440 & 0.101 & 5.10 \\
4 & 10.350 & 10.159 & 1.010 & 8.80 \\
5 & 10.153 & 10.357 & 15.310 & 10.60 \\
6 & 10.255 & 10.257 & 0.110 & 5.30 \\
7 & 10.250 & 10.257 & 10.120 & 5.40 \\ \hline \end{tabular}
\end{table}
Table 1: Some properties of carbonate core samples used in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline
**NO.** & **Fracture** & **Aquifer Size (Times)** & **Production Rate (mL/min)** \\ \hline
1 & no & 0, 3, 7, infinite & 500, 1000, 2000, 4000 \\
2 & no & 0, 1, 3, 7, 12, infinite & 500, 1000, 2000, 4000 \\
3 & no & 0, 3, 7, infinite & 500, 1000, 2000, 4000 \\
4 & no & 0, 3, 7, infinite & 500, 1000, 2000, 4000 \\
5 & no & 0, 3, 7, infinite & 500, 1000, 2000, 4000 \\
6 & yes & 0, 3, 7, 12, infinite & 500, 1000, 2000, 4000 \\
7 & yes & 0, 3, 7, 12, infinite & 500, 1000, 2000, 4000 \\ \hline \end{tabular}
\end{table}
Table 2: Different experiments of water invasion conducted in this study.  
Figure 2: Core samples from the carbonate gas reservoir of Anyue gas field. (**a**) Core sample with vugs and pores; (**b**) Core sample with micro fractures.  
respectively. The experiments to understand water invasion are conducted when only considering the water elastic expansion effect, comprehensively water flexibility and elastic expansion of water soluble gas. The water invasion versus gas recovery in different water invasion energies is shown in Figure 3. From the result of Figure 3, we can see that water invasion with 3 times and 7 times aquifer are 0.021 PV and 0.055 PV when water invasion energy only considers the effect of water expansion. They are far less than water invasion considering water elastic expansion and elastic expansion of water soluble gas, which are 0.182 PV and 0.227 PV, respectively. The water invasion is very small with considering water elastic expansion whether it is 3 times or 7 times aquifer. The decline range of recovery factor is less than 5.0% compared with no aquifer. When the aquifer is 20 times, the dramatic phenomenon of water invasion occurs [21]. While comprehensively considering water invasion energies, the water invasion of 3 times aquifer is 0.182 PV and the recovery factor decreases by 17.6%, which has a strong influence on gas production. Therefore, during the simulation of water invasion we should not only consider the elastic expansion of water, but also consider the elastic expansion of water soluble gas.
## 2 Materials and Methods
### Control Structures
#### 2.2.4 Advanced SISO and MIMO Controllers  
Advanced control techniques use different model-based and optimal controllers. Model-based controllers refer to those control algorithms which include a process model in the control law. The model can be either based on a real simulated process or on a black-box. Usually, the model is used to identify the output of the controller to obtain optimal behavior. Figure 5 shows the control structure. There are two main differences from Figure 4. The first is the treatment that the controller makes with the disturbance. The second refers to the two new inputs in the first controller: a cost function to determine the optimal solution and the constraints on the system. This control structure uses a MIMO controller, that it is to say that each manipulated variable affects several controlled variables, causing loop interactions.
## 3 Results
### Case Study: The Solar Panels of the UPMSat-1
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters; \(\xi\)** & **UPM-5** & **UPM-6** \\ \hline \multirow{4}{*}{[PERSON] and Haneefa} & \(\gamma\) & 9.942\(\cdot 10^{-01}\) & 9.912\(\cdot 10^{-01}\) \\  & \(m\) & 1.401\(\cdot 10^{-01}\) & 1.392\(\cdot 10^{-01}\) \\ \cline{1-1}  & \(\xi\) & 3.35\(\cdot 10^{-02}\) & 3.71\(\cdot 10^{-02}\) \\ \cline{1-1}  & \(k\) & 1.401\(\cdot 10^{-01}\) & 1.393\(\cdot 10^{-01}\) \\ \cline{1-1} \cline{2-4} Das & \(h\) & 6.526\(\cdot 10^{-03}\) & 9.568\(\cdot 10^{-03}\) \\ \cline{1-1}  & \(\xi\) & 3.33\(\cdot 10^{-02}\) & 3.69\(\cdot 10^{-02}\) \\ \cline{1-1} \cline{2-4} Pindado and Cubas & \(\phi\) & 2.6605 & 2.5879 \\ \cline{1-1} \cline{2-4}  & \(\xi\) & 2.04\(\cdot 10^{-02}\) & 2.12\(\cdot 10^{-02}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameters for the explicit models proposed by [PERSON] and [PERSON], [PERSON] and Pindado and Cubas, for the UPM-5 and UPM-6 solar panels of the UPMSat-1 satellite. \(\xi\) is the non-dimensional RMSE for the experimental data. The differences in the current for these models with regard to the experimental \(I\)-\(V\) curves are plotted in Figures 11 and 12. The differences were relatively small, with the maximum always located in a reasonably small range between the MPP and the open voltage point. The values of the non-dimensional RMSE, \(\zeta\), for the fits are plotted in Figure 13. The results show reasonably good agreement with the mathematical approaches proposed in the present work. Figure 12: \(I\)-\(V\) experimental curve (left axis) for the UPM-6 solar panel. Curves for the 1-D/2-R equivalent circuit model (obtained using two procedures: best fit to the parameters obtained numerically and analytical extraction) and the explicit methods of [PERSON] and [PERSON], [PERSON] and [PERSON] and [PERSON] are shown. The differences in the current compared to the experimental data are indicated on the right axis.
## 3 Results and Discussion
### Comparison of Energy Consumption  
To compare energy consumption before and after the water heating system reform, the daily power consumption and hot water consumption of the two systems during the period from 18 February to 2 July were measured. Both student apartments have  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Water Temperature** & **Water Consumption** & **Heat Collection** & **Power Consumption** \\ \hline Instrument & Temperature & Electromagnetic & Electromagnetic & Electric energy \\ sensor & flowmeter & heat meter & meter \\ Model & PT100 & SYA-D800-01X32 & SYA-H200-01X65 & DT862-4 \\ Accuracy & \(\pm\)0.15\% & \(\pm\)0.5\% & \(\pm\)0.5\% & \(\pm\)1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy of the measuring instruments.  
Figure 4: Climate conditions in Tianjin.  
988 users. The energy consumption index comparisons between the two systems are summarized in Table 2. The results indicated that the SA-ASHP system has a high potential to save energy. After the transformation, the unit of hot water power consumption decreased from 37.40 kWh/m3 to 10.51 kWh/m3, and the per capita power consumption decreased from 1.10 kWh/day/person to 0.30 kWh/day/person. In addition, the daily water consumption per person declined significantly, because the water supply temperature was kept at 40-50 \({}^{\circ}\)C after the transformation, and the students could adjust the water temperature to appropriate levels more quickly when using hot water and could reduce unnecessary waste.  
Figure 5 shows the average daily power consumption of the three stages. Both systems had the highest average daily power consumption during the heating period. This is because when the solar fraction is the smallest, the electric boiler and heat pumps need to be turned on for the longest time. After replacing the electric boiler with the air source heat pump as the auxiliary heat source, the average daily power consumption of the three periods decreased. The power consumption decreased from 1790 kWh to 485 kWh in the heating period, and the power saving rate reached 72.91%. During the entire test period, the average daily power saving was 790 kWh, and the average daily power saving rate was 72.70%. Based on these results, it can be estimated that the annual power saving of a hot water supply system in a student apartment would be about 288,350 kWh after the transformation.
## 2 Chirp Transform Spectrometer
### Classical Two-Channel Structure of CTS
The device numbers (such as filters, amplifiers, mixers, and the SAW filters) are doubled in the classical CTS system. The power consumption and mass of the system also increases accordingly, thus increasing the cost of the system. The matching between the two channels need to be precise to  
Figure 4: Time–frequency relation schematic diagram of the input frequencies and output pulses of the two-channel structure. Figure 3: Schematic diagram describing the principle of the chirp transform spectrometer (CTS). The input signal shown in the left side is expanded to wide-band chirp signal by mixing it with the input chirp signal. Subsequently, the modulated chirp signal is compressed to a pulse though the SAW filter, which distributes on the time-domain shown on the right side. ensure the right temporal relations between the obtained pulses. This indicates that the same devices in the system need to have the same characteristics and the time-delay characteristics of the two channels also need to match well. The spectral accuracy will be influenced by the time-delay match between the two channels, and the amplitude consistency of the spectrum will be influenced by the insertion loss of the mixers. High-resolution spectrum detection of the measured signals demands an accurate match of chirp rate between signal expansion and compression. The match between the two SAW filters for pulse compression is also significant. Generally, it is very difficult to get two of the same SAW filters having the same chirp rate over the entire bandwidth. There is a switch before the digital sampling and storage procedure to combine the signals resulting from the two channels. So, an accurate time sequence control system is needed to manage the temporal relations of output pulses between the two channels.