esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fresh Performance
#### 3.1.3 16-Bar J-Ring Test  
The values of the slump flow, \(t_{500}\) time, and blocking ratio, obtained in the 16-bar J-ring test, are shown in Figure 10.  
The results regarding slump flow (Figure 10a) and \(t_{500}\) time (Figure 10b) were affected by the presence of obstacles simulating concrete reinforcement, which hindered SCC flow [4]. Therefore, slump flows obtained in this test were lower than in the slump-flow test. Furthermore, only the _RW_ and \(W\)1.5 mixes were of J-ring SF1 class, as the _W3.0_ mix did not reach the minimum required value of 550 mm [61]. Similarly, the \(t_{500}\) times were higher than in the slump-flow test, with all mixes being J-ring VS2 class. On the other hand, the results of the 16-bar J-ring test corroborated the trends regarding the effect of increasing the amount of RCWTB in SCC found in the slump-flow test. An amount of 1.5% RCWTB caused a notable decrease in the slump flow (14.4% loss), while the increase in the \(t_{500}\) time that it caused was only 3.4%. These percentage variations for the \(W\)3.0 mix were 21.6% and 30.9%, respectively. Therefore, the reduction in the free water [46] and the subsequent increase in the internal friction between the SCC components [14] due to the addition of low RCWTB content only notably affected the free flow of the mixtures when high slump-flow values were reached, even despite the presence of bars simulating concrete reinforcement, such as in the J-ring test.  
Figure 9: Experimental results of the 3-bar L-box test: blocking ratio.  
The J-ring blocking ratio (Figure 10c) was measured as the difference in SCC height between the center and the outside of the ring when SCC movement ended [61]. This value remained approximately constant among the three SCC mixtures, with results between 32.5 mm (\(W3.0\) mix) and 37.0 mm (\(W1.5\) mix). RCWTB did not impact SCC passing ability under free flow, as it is thought that the cement paste could successfully drag the GFRP fibers through the bars of the J-ring. Other research has also reported such phenomena regarding different types of fibers [28; 72].  
The relationships between the different results of the 16-bar J-ring test corroborated all the aspects mentioned above, as can be seen in Figure 11. No precise linear relationship was detected between the \(t_{500}\) time and the slump flow, as regarding the slump-flow test (Figure 8). Similarly, the blocking ratio did not show a clear dependence with the other two properties, although this relationship was slightly more precise with the \(t_{500}\) time.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 4 Results and Discussion
(**a**) The prediction results of BP network; (**b**) The prediction results of LSTM network; (**c**) The prediction results of 1 DCNN; (**d**) The prediction results of BP-LSTM parallel network; (**e**) The prediction results of CNN-LSTM parallel network; (**f**) Performance evaluation results of various models. dealing with the point-to-point mapping relationship. The CNN network can accurately capture the subtle information between the data but finds it difficult to deal with the feature sequences with large fluctuations. The LSTM is good at considering the dependencies before and after the sequence. However, it finds it difficult to extract information with weak temporal information. Therefore, optimizing the hybrid model structure according to the actual feature of the field data is the key to improving the performance of the model.
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 3 Results
### Results on Synthetic Data
The exact value of \(Gini\) depends on the travel demand, as for \(p=0.2\), i.e., when the central node is a destination in 80% of the journeys, \(Gini\) is higher than when \(p=0.8\). This is because with fewer destinations, there are also fewer possible path trajectories, and thus more passengers use the same part of the network. We can also see how \(Gini\) decreases for higher \(w_{2}\), i.e., when traveling by tram is not much faster than traveling on the road network. Finally, we can notice the drop in \(Gini\) compared to the shortest path-like scenario \(\beta_{1}=\beta_{2}=1\). In this case, the traffic distribution is the most hierarchical, suggesting that possible traffic congestion can be avoided by setting lower values of \(\beta_{1}\). Our model can be used to simulate traffic distributions under various conditions. In fact, tuning \(p\), \(\{w_{\alpha}\}\) and \(\{\beta_{\alpha}\}\), one can simulate disparate scenarios. For instance, in Figure 2 we show results for different parameters' choices on a particular realization of a 2-layer synthetic network. Several conclusions can be drawn from this simple experiment. For instance, the second layer, which ideally can represent a tram network, is only partially used when \(\beta_{2}=1.5\). This value encourages traffic to consolidate on fewer main connections, simulating the scenario where building the rail infrastructure is expensive. Our model can guide a network manager to decide what edges should be prioritized when designing the network. In this example, we can distinguish which set of edges are the most utilized. These are mainly central edges, but the exact set can change depending on the other parameters. For example, if the travel demand, tuned by \(p\), switches from a monocentric to a more heterogenous set of entry-exit stations, one of the main central edges changes from connecting a periphery to the center, to connecting two locations in the periphery.
## 7 Discussion
### Possible Improvements or Extensions  
Here are the possible improvements of the proposed method:  
* Hybrid approaches
* Integrate the proposed method with machine learning techniques, such as neural networks or reinforcement learning, to enhance the adaptability and generalization capabilities of the model.
* Dynamic adjustment
* Develop algorithms that allow for real-time adjustment of the membership functions and rules based on incoming data, improving the method's responsiveness to changes in system dynamics.
* Utilize parallel computing techniques to distribute the computational load, particularly for large-scale systems, to improve processing speed and efficiency.
* Robustness enhancement
* Incorporate robust statistical techniques to handle noise and outliers in the data, ensuring that the model remains accurate even with imperfect data.
* Create user-friendly software tools and interfaces that simplify the implementation process, making the method more accessible to practitioners without extensive expertise in fuzzy logic or system identification.
## 3 Model for Vehicle Routing Problem  
### Basic Assumptions and Constraints for Building Model  
We consider a vehicle routing problem in which there are a fixed number of vehicles and demands served by these vehicles. Each demand represents the number of trays. Furthermore, we also consider time windows within which the deliveries of trays are requested. We assume that the time window constraints are \"soft\" ones, that is, it is allowed that the realized deliveries can violate the constraints with appropriate penalties for either early or late arrivals.  
A vehicle delivers anything requested by some customers in trays, but the number of trays are limited by \(C\), which denotes the capacity of vehicles. It has been assumed that each vehicle startsits service at the depot and returns to the depot after completing the pick-up and delivery service. Also, each customer can be served by only one vehicle. It means that the number of trays loaded in a vehicle from the depot is equal to the sum of the number of trays which should be delivered to the customers included on the route of the vehicles.  
Once again, it should be noted that all of the items will be delivered packed in the trays. Thus, it is required that there are enough empty trays at the depot. The empty trays can be collected from the customers the day after delivery. Therefore, the optimal picking and delivery plan should be devised considering the number of trays delivered and those that will be delivered at the same time. As the maximum capacity of the vehicles is fixed as \(C\), some of the delivered trays may be left with the customer if there is not enough space in the vehicle. Note that the capacity represents the number of trays a vehicle can load, and it includes both empty and non-empty trays. The picked-up trays are returned to the depot and the total number of picked-up trays should be greater than a lower bound, \(L\).  
In this devising our method there were two factors: (1) the sequence of customers that a vehicle visits; and (2) the number of empty trays picked up from each customer when the vehicle visits. These factors can be merged into a single problem to minimize the total cost, which consists of the travelling cost for visiting customers, the left-over cost which comes from the empty trays not returned from the customers, and penalty cost from the early or late arrivals of vehicles. This problem of vehicle routing can be denoted as VRPSDP-TW-LO, which means the vehicle routing problem in which delivery and pick-up are simultaneously considered with the time window and left-over cost.
# Impact of Heat Exchanger Effectiveness and EGR on Energy and Emission Performance of a CI Engine
## 1 Introduction
These are particularly relevant in high-power-density applications like motorsports or hybrid powertrains [39; 40]. However, the trade-offs involved in thermal management strategies--such as increased cooling load, turbocharger efficiency, and fuel-air mixing dynamics--necessitate further investigation into how varying HEE impacts overall engine behavior under realistic operational conditions, both from an energetic and ecological standpoint. The objective of this study is to evaluate the combined effect of intake air heat exchanger effectiveness and EGR operation on the combustion process and performance parameters of a CI engine under part-load conditions. The analysis integrates combustion modeling carried out using AVL BOOST software (Version: v2021.2.0.0) simulation with detailed experimental data. The aim is to identify potential heat exchanger control strategies that simultaneously enhance combustion efficiency and reduce NOx and soot emissions. The novelty of this study lies in the combined assessment of heat exchanger effectiveness and EGR performance under part-load conditions using a comprehensive experimental--simulation approach, which enables a more holistic improvement in energy efficiency and emission reduction.
# Altered Trends in Light Use Efficiency of Grassland Ecosystem in Northern China
## 3 Results
### Interannual Dynamic Characteristics of Climate Factors  
In the grasslands of northern China, precipitation, temperature, and VPD have all increased over the past 40 years at rates of 0.594 mm/yr (\(p>0.05\)), 0.034 \({}^{\circ}\)C/yr (\(p<0.05\)), and 1.7 Pa/yr (\(p<0.05\)), respectively (Figure 5a,b). Solar radiation has been trending downward (\(-1.675\) MJ/yr, \(p<0.05\)) (Figure 5b). Climate factors have shown varying degrees of change during the two time periods of 1982-1997 and 1998-2018. The precipitation trend has shifted from increasing to decreasing, and the trend for VPD is the opposite. The temperature's trend of increasing has slowed down, while the increasing trend of radiation has sped up (Figure 5a,b).  
Figure 4: The spatial patterns of significant (\(p<0.05\)) changes in LUE during 1982–2018 (a), 1982–1997 (b) and 1998–2018 (c). The insets depict the interannual variation trends of the six grassland types, \(\star\) indicates a significant trend (\(p<0.05\)). AM, alpine meadow; AS, alpine steppe; TM, temperate meadow; TS, temperate steppe; SG, sparse grassland; SS, sparse shrubland.  
The interannual trends of climate factors in grasslands show spatial heterogeneity over time. AM, SG, and SS have shown significant increasing trends in terms of precipitation, temperature, and VPD from 1982 to 2018, while SG showed a decreasing trend in solar radiation. The temperature, precipitation and solar radiation of all grassland types showed no significant change from 1982 to 1997, while VPD in AM and AS exhibited a markedly declining trend (\(p<0.05\)). In contrast, there were no discernible trends in precipitation and temperature in all grassland types from 1998 to 2018. Solar radiation increased significantly in TM, TS, and SS, while VPD increased notably in AM, AS, TS, and SG (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **AM** & **AS** & **TM** & **TS** & **SG** & **SS** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **S** \\
# Minkowski Island and Crossbar Fractal Microstrip Antennas for Broadband Applications
## 3 The Crossbar Fractal Geometry  
The microstrip antennas based on crossbar fractals offer the enhanced frequency band but also the improved radiation antenna pattern. [PERSON] et al. presented such fractal antenna, receiving the reflection coefficient better than \(-10\) dB in the range of 3.1-11.5 GHz [12]. Particular applications for such a fractal with different three-stages (configurations) were discussed in [12; 13; 14]. The advantage of a crossbar fractal is that the number of stages can be adjusted to obtain the required multi band characteristics.  
In this work, stage-2 of crossbar fractal tree structure, diagonally lying on the square unit cell of 4 \(\times\) 4 mm\({}^{2}\) has been taken into consideration--Figure 2a.  
The following antenna configurations have been analyzed:  
1. Configuration #1. The upper layer is based on a crossbar fractal structure and the lower layer is a solid conductive strip as in a conventional antenna. The upper patch consists of the 4 \(\times\) 3 square unit cells with an edge of 4 mm in a periodic arrangement.
2. Configuration #2. The patch is a solid strip, but the ground layer is composed of 7 \(\times\) 6 square unit cells with the crossbar fractal.
3. Configuration #3. Upper and lower layers are based on a crossbar fractal.  
The above configurations of antennas have been presented in Figure 2b-d.  
The reflection coefficients (S11 [dB]) for the above-mentioned configurations of antennas with crossbar fractal where depicted in Figure 3.  
Reflection coefficients for analyzed antennas indicate the narrowband behavior for all configurations. Such property is practically similar to the conventional microstrip antennas. The only small displacements in frequency can be observed, depending on the proportional participation of the  
Figure 3: Simulated values of S11 for the antennas with crossbar fractals, for the three configurations.  
Figure 2: (a) the crossbar fractal based elementary unit cell of the antenna (dimensions are in mm); (b–d) geometry of the three configurations (mentioned in the text) of microstrip antennas with crossbar fractals.  
metal being etched out. Figure 3 shows that the crossbar fractal cannot make it possible to broaden the frequency band of the antennas. Nevertheless, one property of this fractal should be underlined--the deepness of the S11. Curves presented in Figure 3 have significantly smaller depth compared to the conventional antenna (Figure 1b). Such crossbar fractal smoothing property will be used with the final version of an antenna with double different fractal layers.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 3 Main Synthesis Methods of Magnetic and Maghemite Nanoparticles  
Since the first synthesis of magnetic iron oxide nanoparticles in the 1980s, a lot of synthesis methods have been developed (Table 2) to control their sizes and shapes [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. The control of these physicochemical parameters will depend on the synthesis way used. Mastering the separation of germination and growth stages of nanoparticles via the control of pH in the case of coprecipitation (which will be describe in details at the end of this part) [20; 21; 22; 23] or the control of temperature in the case of hydrothermal synthesis [24; 25; 23], by thermal decomposition [28; 29; 30] or by polyols process [31; 32; 34] will allow then the modulation of the size and the shape of nanoparticles. In the case of synthesis by microemulsion [26; 27] or by sol-gel process [35; 36], the control of the size of nanoparticles is insured by the limitation of the space left for the growth of these.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Oxide** & **Saturation Magnetization** & **Mono-Domain Critical Diameter** &
\begin{tabular}{c} **Limit Diameter** \\ **Superparamagnetism** \\ \end{tabular} \\ \hline
**Magnetite** & 92 emu/g & 30 \(\pm\) 5 nm & 20 \(\pm\) 5 nm \\ \hline
**Maghemite** & 74 emu/g & 30 \(\pm\) 5 nm & 20 \(\pm\) 5 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the magnetic properties of magnetite and maghemite iron oxides.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** & \begin{tabular}{c} **Shape and Size** \\ **Saturation Magnetization** \\ \end{tabular} \\ \hline \multirow{6}{*}{**Co-precipitation**} & - & \begin{tabular}{c} Ferrous and ferric salts, inexpensive \\ reagents \\ \end{tabular} & - & \begin{tabular}{c} Large distribution of size. \\ Non controlled oxidation. \\ \end{tabular} & - & \begin{tabular}{c} Sphere 10–55 nm \\ 16 to 82 emu/g \\ \end{tabular} \\  & - & \begin{tabular}{c} Possible large-scale production. \\ Easy modification surface \\ Synthesis in aqueous solution. \\ \end{tabular} & - & \
\begin{tabular}{c} Low reproducibility. \\ Aggregation of NPs. \\ \end{tabular} & - &
\begin{tabular}{c} Sphere 12–40 nm \\ Disk 250–1000 nm \\ 53 to 82 emu/g \\ \end{tabular} \\ \hline
**Hydrothermal** & - & \begin{tabular}{c} Better size control. \\ Narrow size distribution. \\ \end{tabular} & - & \begin{tabular}{c} High temperature. \\ Long synthesis time. \\ \end{tabular} & - & \begin{tabular}{c} Sphere 12–40 nm \\ Disk 250–1000 nm \\ 53 to 82 emu/g \\ \end{tabular} \\  & - & \begin{tabular}{c} Aqueous solution. \\ \end{tabular} & - & \
\begin{tabular}{c} Possible aggregation of NPs. \\ \end{tabular} & - &
\begin{tabular}{c} 53 to 82 emu/g \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of advantages, disadvantages and morphological characteristics and saturation magnetization of the magnetic iron oxide nanoparticles synthesized by the main synthesis methods.
## 7 Conclusions  
In this paper, an MFI scheme, based on amplitude histogram distributions was proposed for use in autonomous digital coherent receivers in EONs. The feasibility of the proposed scheme was verified by numerical simulations for 28 GBaud PDM-QPSK/-8 QAM/-16 QAM/-32 QAM/-64 QAM signals over a wide range of OSNRs. Following the numerical simulations, to further demonstrate the effectiveness of the proposed scheme, proof-of-concept experiments were undertaken in 28 Gbaud PDM-QPSK/-8 QAM/-16 QAM, and 21.5 Gbaud PDM-32 QAM systems under back-to-back and long-haul fiber transmission links. Simulation and experimental results showed that the proposed scheme was robust against both linear and nonlinear noise. Equally importantly, such performances were obtained with significantly reduced complexity. The proposed scheme was dependent on amplitude features only, and was, therefore, insensitive to carrier phase noise. In addition, the proposed MFI scheme was also capable of identifying PS-QAM signals when slight modifications to the procedure and corresponding thresholds were made. According to the tradeoff between the identification performance and computational complexity, the proposed MFI scheme may have good potential for implementation in future EONs.  
Conceptualization, M.H. and X.J.; methodology, M.H., X.J., XX. and J.T.; software, M.H., X.J. and W.H.; validation, M.H. and X.J.; formal analysis, M.H., X.J. and J.T.; writing--original draft preparation, M.H. and X.J.; writing--review and editing, J.T. and R.G; supervision, J.T., R.G and X.X; project administration, M.H., J.T. and R.G.; funding acquisition, M.H., J.T. and R.G. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Sichuan Science and Technology Program (2023 YFH0067), the DSP Centre, funded by the European Regional Development Fund through the Welsh government, and also by the North Wales Growth Deal through Ambition North Wales, the Welsh and UK governments, and the Opening Project of Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things (2021 WYJ03).  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
This work was supported by the program of China Scholarship Council (No. 202008515061) and the Innovation Fund of Postgraduate, Sichuan University of Science & Engineering (y2021070).  
The authors declare no conflict of interest.