esa-sceva/satcom-chunk-collection huggingface dataset:
### Measured Data Analysis  
According to stratum conditions, in this tunneling section, the designer of the slurry TBM from Herrenknecht set the control line for the positive rolling angle (clockwise) to 5 mm/m, and the control line for the negative rolling angle (counterclockwise) to 10 mm/m based on the Chinese Standard GB 50446-2017 [34]. Through Herrenknecht's own monitoring system, the rolling angle and the cutterhead torque were monitored, and the direction of cutterhead rotation (DCR) during each ring tunneling was recorded (see Figure 5).  
Figure 5 shows the change curve of rolling angle; the rolling angle of the shield is strictly controlled within \(-10\) mm/m to \(+5\) mm/m from Ring No. 480 to Ring No. 530. When the rolling angle reaches the control limit value, in the current project, the rolling angle is generally regulated by changing DCR. The change point of DRC is marked in Figure 6. Figure 6 shows that the rolling angle gradually returns to 0 after DCR is changed. It can be observed from Figure 7 that when the DCR has changed, CT will drop rapidly. As shown in Figure 7, the reduction of shield rolling angle can relieve the load-bearing capacity of the shield machine.  
Figure 5: Cutter rotation direction change.  
Figure 6: The change curve of the shield rolling angle.  
Figure 7: Comparison curve of cutterhead torque and rolling angle.  
Through analyzing the changing trend of field data, it can be seen that there is a certain correlation between the rolling angle and CT. Nevertheless, the degree of correlation between the two parameters needs further analysis and discussion.
## 6 Conclusions  
This paper presents a study on the response of the spectral clustering algorithm to variations in nonlinear phase noise and signal-to-noise ratio (SNR). The study deals with the simulation of transmissions at a 10 GHz symbol rate for different phase noise, with levels of \(-90\), \(-100\), and \(-110\) dBc/Hz at a frequency offset of 1 MHz, mimicking the real scenarios found in the literature. The algorithm was tested with different modulation formats of 16-QAM, 32-QAM, and 64-QAM, exceeding in size those found in the literature for the use of spectral clustering.  
The spectral clustering algorithm was modified to work with data streams for future real-time implementations. It was also integrated with a demapping stage for m-QAM to correct labeling inconsistencies when working with windowed data in the demapping stage.  
The spectral clustering algorithm outperforms k-means in the presence of nonlinear phase noise and performs well in a noise-dominated channel, as shown in the SNR vs. BER plots presented in this paper. In future work, we will explore the design of a heterogeneous FPGA-based architecture for real-time implementation of the spectral clustering algorithm as a coherent receiver in an optical communication system.  
Conceptualization, D.M.-V. and A.E.C.-O.; methodology, M.S.-S., D.M.-V. and A.E.C.-O.; software, M.S.-S.; validation, M.S.-S., D.M.-V., A.E.C.-O., E.R.-V., N.G.-G. and J.B.-V.; formal analysis, M.S.-S., [PERSON]-V., [PERSON]-O., [PERSON]-V., [PERSON]-[PERSON] and [PERSON]-V.; investigation, [PERSON]-S., [PERSON]-V. and [PERSON]-O.; resources, [PERSON]-[PERSON] and [PERSON]-V.; data curation, M.S.-S.; writing--original draft preparation, [PERSON]-S., [PERSON]-V. and A.E.C.-O.; writing--review and editing, [PERSON]-S., [PERSON]-V., [PERSON]-O., [PERSON]-V., [PERSON]-G. and [PERSON]-V.; visualization, [PERSON]-S., [PERSON]-V. and [PERSON]-O.; supervision, [PERSON]-V., [PERSON]-[PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Office of Naval Research Global (ONRG) and the Air Force Office of Scientific Research/Southern Office of Aerospace Research and Development (AFOSR/SOARD) grant number N62909-24-1-2088 and the project PF24211 \"Fortalecimiento de Capacidad Tecnico-Cientifica del Grupo Sistemas de Control y Robotica 2024\".  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
SNR Signal-to-noise ratio  
BER Bit error rate  
QAM Quadrature amplitude modulation  
AWGN Additive white Gaussian noise  
FWM Four-wave mixing  
XPM Cross-phase modulation  
RBF Radial basis function
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 3 Results  
### Evaluation of Volumetric Properties  
Figure 6 presents the results obtained for void content and VMA (average of four Marshall specimens) for the reference asphalt mixture and the asphalt mixtures with the four different types of plastic waste in different percentages of bitumen.  
The Portuguese specification for asphalt mixtures requires a minimum of 14% for VMA. Regarding void content, the range requirement is 3% to 5% for a conventional AC 14 surf 35/50.  
Except for LDPE(uw), the results show that up to 4% of plastic content by mass of binder, the effect of incorporating plastic waste on the VMA and void content is not substantial. However, it must be emphasized that 4% of plastic content corresponds to just 0.2% of the total mixture's mass and the binder represents a little less than 5%. Moreover, the particle sizes and the stiffness of some plastics used may also influence the results. Coarser plastic grains (e.g., HDPE: 0.25 to 8 mm; HDPE500: 0.25 to 10 mm) are likely to increase the asphalt mixture's voids and, consequently, the VMA values.  
HDPE and HDPE500 require higher temperatures to soften. Therefore, for 6% and 8% of plastic content, higher viscosity of the bitumen-plastic blend may happen. More viscous binders cause a thicker film of binder around the aggregates, increasing the voids between the particles. Another possible phenomenon is the higher quantity of stiffer polymer that does not combine with bitumen because of its higher softening temperature.  
Figure 5: Mechanical performance tests: (**a**) four-point bending test; (**b**) indirect tensile strength test; and (**c**) cyclic compression test.
## 1 Introduction
It can be summarized that although the 1 st harmonic approximation method has been mostly adopted in the analysis of the LCL-DAB converter, the error caused by it cannot be negligible. Consequently, the voltage-current characteristics are derived in the time domain to propose the optimized control method in this article, based on the simple and highly controlled DPS modulation scheme. The rest of the article is given as follows. The voltage-current characteristic of the LCL-DAB converter is first analyzed in Section 2. Then, the mathematical model of the LCL-DAB converter with DPS modulation scheme is established in Section 3, according to the analyzed voltage-current characteristic in the time domain. In Section 4, the design regions of no backflow power are graphed so as to estimate an appropriate outer phase shift ratio, and on this basis, a virtual direct current control (VDCC) method of the LCL-DAB converter is proposed to improve the transient performance under no backflow power. The proposed control method is verified by the simulation and experimental results in Section 5, and Section 6 eventually summarizes the article.
###### Abstract
[PERSON] et al. [15] analyzed the process of following automation for hydraulic supports in the middle of the working face and presented a mathematical model for following automated control in the middle of the fully mechanized working face. Despite significant progress achieved in the above studies, the mechanism of spatiotemporal linkages (SPL) of mining and backfilling operations in the solid backfilling working face in fully mechanized mining remains unclear. A method to quantify the time required to complete parallel mining and backfilling operations and reduce the time gap between the two operations to improve the backfilling efficiency is very topical. To this end, the current study analyzes the SPL of different cylinders involved in sequential actions for intelligent backfilling using single supports in parallel mining and backfilling operations in fully mechanized mining. We also investigate the SPL of the positions of each piece of key equipment involved in different steps of intelligent backfilling in the whole face. Formulas for calculating the time of a complete hydraulic support movement-discharge-filling cycle for single supports and the whole face are derived. The influencing factors of the time required to complete a hydraulic support movement-discharge-filling cycle are identified. On this basis, we propose an optimization scheme to improve the backfilling efficiency, which consists in reducing the number of tampings, reducing the time gaps in the actions of single supports and cylinders, increasing the number of hydraulic supports in parallel operation, and intelligent upgrading. Evolution of Intelligent Mining and Backfilling Operations in Fully Mechanized Mining and the Connotation of the Mechanism of SPL
## 1 Introduction  
The direct, and yet complicated, correlation between gut microbiome and host health is well-established in scientific literature [1]. There are numerous ways to influence the microbial composition of the gut to exert beneficial impact on the host. This includes the usage of probiotics--bacterial preparations containing isolated strains of symbiotic bacteria--as well as prebiotics, which are various substances that can avoid digestion in the upper parts of the gastrointestinal tract and provide energy to microorganisms already present in the colon [2]. However, these preparations are not always efficient enough to facilitate the restitution of a disturbed microbiome, nor are they able to effectively treat diseases caused by gut dysbiosis, such as _Clostridioides difficile_ infections and irritable bowel syndrome. This is why in recent years, special attention was given to a procedure dubbed 'f faecal microbiota transplant' (FMT), the central premise of which is the transfer of the entire stool microbiome from a donor to a recipient. The procedure is characterized by consistently high efficacy [3], yet its reliance on the transfer of unfiltered donor faecal material has raised both clinical and public concerns. In response, several modified approaches have been developed to improve safety and refine microbial composition. One such approach is faecal virome transplantation (FVT), which involves isolating and transferring the viral fraction--primarily bacteriophages--from donor material [4]. By excluding bacteria, FVT may reduce the risk of transmitting antibiotic-resistant or pathogenic strains while retaining the potential to modulate bacterial populations indirectly through phage-bacteria interactions. Moreover, as phages can selectively target specific bacterial taxa, FVT may offer a more controlled and mechanistically defined intervention. These features have positioned FVT as a promising alternative or complement to traditional FMT, especially in settings where microbiota-targeted precision is needed. However, appropriate material selection for both these procedures requires deep and thorough understanding of interactions between the host, the microbiome and the microorganisms themselves, which in many regards we still apparently lack due to reasons described in the following sections.
## 3 Results and Discussion
### Scanning Electron Microscopy  
The structure of magnetic polystyrene microspheres was characterized by scanning electron microscopy (SEM) with an FEI QUANTA FEG250 from the United States. Figure 1 presents the transmission electron microscope diagram of these magnetic polystyrene microspheres. It can be seen from the figure that these microspheres are mostly perfectly spherical, with the most common particle size being 10 \(\upmu\)m; these microspheres also have certain dispersion. Some smaller particles and irregular substances may be caused by unwrapped or self-aggregated polystyrene. The smooth surface of magnetic microspheres provides a good condition for further functionalization.
## 2 Materials and Methods
### Empirical Safety Stock Estimation
#### 2.2.5 Extreme Value Theory (EVT)  
This method is a prevailing tool to study the tail behavior of the demand distribution by taking into account the occurrence of abnormal and extreme events due to the complexity of the markets, customers, promotions, and the economic situation. The use of this theory improves and adjusts the SS computing for items that can record extraordinary demands.  
In this method, the maximums and minimums of the data converge to the generalized extreme value (GEV) distribution or the generalized Pareto distribution (GPD). Two traditional approaches are introduced in the literature: the Block maxima method (BM) and the Peack over threshold (POT) method. We are interested in the POT method, in the rest of this work, which considers as extreme each observation beyond a certain threshold u. In this case, several observations of a turbulent period, which records extreme demands, are retained as extremes, while any observation of a calm period is considered as extremes. The determination of u is problematic, and various approaches are used in the literature, such as the mean excess function (MEF) and the hill plot. The reader can consult [PERSON] [41] for more detailed discussions and several examples on the use of hill plots to approximate the threshold value \(\mathrm{u}\).  
The expression for quantiles associated with a given CSL is described in Equation (21).  
\[\mathrm{Q}(\mathrm{CSL})=\mathrm{u}+\frac{\hat{\beta}}{\hat{\xi}}\left(\left( \frac{\mathrm{n}}{\mathrm{N}_{\mathrm{u}}}\mathrm{P}\right)^{-\hat{L}}-1\right) \tag{21}\]  
where  
\(\mathrm{n}\) is the total sample size,  
\(\hat{\beta}\) is the scale factor,  
\(\mathrm{N}_{\mathrm{u}}\) is the number of observations beyond threshold \(\mathrm{u}\),  
\(\xi\) is the tail index of the GPD distribution that can be positive or negative or null,  
\(\mathrm{p}=1-\mathrm{CSL}\).  
We can then deduce the safety stock level using Equation (11).
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Optimization of the MADDPG-VSC Algorithm for Control Design
#### 3.2.2 Exploration and Learning Efficiency  
To ensure adequate exploration in continuous action spaces, the MADDPG-VSC algorithm introduces random noise \(\xi\) to action exploration. This noise introduces variability in action selection, preventing the algorithm from converging prematurely to suboptimal policies. The noise parameter \(c\) is dynamically adjusted during the training process to balance exploration and exploitation. Initially, \(c\) is kept high to encourage extensive exploration, but it gradually decreases as the algorithm converges to a more stable policy, as follows:  
\[u^{\prime}=u+\xi \tag{23}\]where \(\xi\) follows a normal distribution \(N(u,c)\), with mean \(u\) and variance \(c\). The noise parameter \(c\) is defined as  
\[c=\left\{\begin{array}{ll}c&\text{if {episode}}\leq 50\\ 0.995c&\text{if {episode}}>50\end{array}\right. \tag{24}\]  
To enhance learning efficiency, the sweep angle \(\Lambda\) and other state values are normalized in the input layer, yielding dimensionless state value \(\tilde{\Lambda}\). This normalization is defined as  
\[\tilde{\Lambda}=\frac{\Lambda-\Lambda_{\text{min}}}{\Lambda_{\text{min}}\;- \Lambda_{\text{max}}} \tag{25}\]  
where \(\Lambda_{\text{min}}\) represents the minimum sweep angle during UAV flight, and \(\Lambda_{\text{max}}\) represents the maximum sweep angle. Normalization helps in scaling state values to a uniform range, improving the algorithm's training efficiency and stability by ensuring that all input features are on a comparable scale, thus contributing equally to the model's learning process.
## 2 Numerical Model  
### Geometric Model and Physical Parameters  
The geometric structure of the bearing chamber is shown in Figure 1. The structural parameters and values of the bearing chamber involved in the analysis mainly include the rotor radius of the bearing, \(r_{s}=60\) mm; the height of the bearing chamber, \(h_{b}=30\) mm; the width of the bearing chamber, \(w_{b}=30\) mm; and the diameter of oil scavenge \(d_{s}\) (scavenge is shown in Figure 2) and the air vent \(d_{v}\) are both 16 mm. Seal air flows into the bearing chamber from air inlet and discharges through vent. The gap of oil inlet is 2 mm. Moreover, the lubricating oil is shed into the bearing chamber from the clearance between the bearing outer ring and the cage (oil inlet in Figure 1) and discharges through scavenge. The equivalent size of oil inlet is 4 mm (oil inlet in Figure 2).  
Figure 1: The structural diagram of aero-engine bearing chamber.  
In the analysis, the oil brand [17] is aviation lubricating oil 4109. The oil density \(\rho_{l}\) is 926 kg/m\({}^{3}\), the oil dynamic viscosity \(\mu_{l}\) is 0.007 Pa\(\cdot\)s, and the oil surface tension coefficient \(\sigma_{l}\) is 0.035 N/m. The gas density \(\rho_{g}\) is 1.225 kg/m\({}^{3}\) and the air dynamic viscosity \(\mu_{g}\) is 1.789 \(\times\) 10\({}^{-5}\) Pa\(\cdot\)s.
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
## 5 Discussion  
This study tested three kinds of PCMs with different melting temperatures to investigate the energy saving potential they provide under future climate conditions. The weather in Seoul and Tokyo is characterized by high humidity in the summer, which reduced the efficiency of the PCMs. The cold winters in Hong Kong, Tokyo, and Seoul also compromised the effectiveness of the PCMs. The methodology of this study uses predictions of future climate changes to determine the effectiveness of PCMs incorporated into a building envelope at the present, as well as in 2020, 2050, and 2080. Organic PCMs were chosen because they are more chemically stable than inorganic PCMs and are suitable and compatible building materials that melt congruently.  
The results of this study indicate that incorporating a PCM with an appropriate melting phase temperature into a building's envelope has significant potential to save energy during the cooling and heating periods. In this study, the PCM was positioned between the plasterboard and the fiberglass quilt of all four interior walls and in the roof.  
The position of the PCM reduced the number of hours that the building interior spent outside the thermal comfort zone. In addition, assuming a 24 h HVAC schedule, the PCM with a melting phase temperature of 29 \({}^{\circ}\)C provided the highest annual energy savings. Using a PCM under this operational schedule provided benefits during both the cooling and heating periods. The BioPCM was more efficient than the DuPonEnergainPCM and RUBITHERMPCM because of its melting temperature, which was closer to the heating and cooling set-point temperatures in the cooling and heating seasons.  
The simulation results confirm the energy savings potential of the PCMs in humid climates under future climate conditions; the energy savings depend on their chemical and physical properties, their position in the building envelope, and their thickness. The organic PCMs are best for such regions because they are least effected by moisture.  
The energy savings in the heating season are attributable to only partial melting of the PCMs for less of each day (noon until evening) than in the cooling season because of moderate ambient temperature, moderate solar radiation, or both. In addition, the liquid-solid transition (solidification) overnight discharged the stored heat into the interior of the office building. This additional heat helped maintain the interior environment and decreased the HVAC workload.  
The important results of this study are the predicted estimates for energy consumption under future climate conditions, the effects of climate change on the efficiency of PCMs, and the effects of PCMs on energy consumption. Previous studies [4; 16; 19] did not investigate PCM effectiveness under future climate conditions. These results confirm that climate change will have little effect on the energy saving potential of PCMs appropriate to local climate conditions.  
Figure 17: Annual HVAC system gas savings in each region according to the PCM used.
## 4 Mix Design of SPS Asphalt Mixture
### Determination of Optimal Fiber Content and Optimal Asphalt Content
Figures 8 and 9 show that, with the increase of mineral filler content, the air voids of both SMA-II-1 and SMA-II-3 decrease gradually but at different rates, that is, the variation of mineral filler content has a deeper influence on the air voids of SMA-II-1 than that of SMA-II-3. In contrast, the air voids of SMA-II-2 increase with the increase of the mineral filler content. According to the test results in Table 10, the mineral filler content changes from 8.5% to 9.5% and then to 10.5%, while the air voids of SMA-II-1 decrease by 33.6% and 29.3 %, respectively, the air voids of SMA-II-3 decrease by 16.2% and 17.5%, respectively and the air voids of SMA-II-2 increase by 13.9% and 16.6%, respectively. The average slopes of the air voids curves of SMA-II-1, SMA-II-2 and SMA-II-3 shown in Figure 8 are \(-\)1.535, 0.73 and \(-\)0.905, respectively. The changing trends of the air voids of II-1 resemble those of II-3, which is because the amount of coarse aggregate in these two gradations are in the majority, and fine aggregate, especially mineral filler content, plays a significant role in the change of air voids. However, the air voids of the gap-graded are less sensitive to changes in the mineral filler content; hence, a qualified gradation is easier to design. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Gradations** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline SMA-II-1-1 & 100 & 76.7 & 55.6 & 36.3 & 23.3 & 18.3 & 13.5 & 8.5 \\ SMA-II-1-2 & 100 & 76.7 & 55.6 & 36.3 & 23.3 & 18.3 & 13.5 & 9.5 \\ SMA-II-1-3 & 100 & 76.7 & 55.6 & 36.3 & 23.3 & 18.3 & 13.5 & 10.5 \\ SMA-II-2-1 & 100 & 92 & 72.3 & 55.3 & 37.7 & 19.4 & 12.9 & 8.5 \\ SMA-II-2-2 & 100 & 92 & 72.3 & 55.3 & 37.7 & 19.4 & 12.9 & 9.5 \\ SMA-II-2-3 & 100 & 92 & 72.3 & 55.3 & 37.7 & 19.4 & 12.9 & 10.5 \\ SMA-II-3-1 & 100 & 88.2 & 67.6 & 47.1 & 47.1 & 29.4 & 16.5 & 8.5 \\ SMA-II-3-2 & 100 & 88.2 & 67.6 & 47.1 & 47.1 & 29.4 & 16.5 & 9.5 \\ SMA-II-3-3 & 100 & 88.2 & 67.6 & 47.1 & 47.1 & 29.4 & 16.5 & 10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Gradation with different passing rate of 1.18-mm and 0.075-mm sieves. Figure 8: Change curves of the air voids of SMA-II-1, SMA-II-2 and SMA-II-3. For SMA-II-2 with a large amount of fine aggregate, a slight change of the mineral powder content has little effect on the air voids of the mixture; meanwhile, the oil-stone ratio 6.5% is low, which will make the air voids increase with the increase of the mineral filler content. In this case, the average slopes of the air voids curves show that the change in the air voids with mineral filler content is relatively stable; however, the change range of the fine aggregate content (the interval is 8.2% and 10.8%) is much larger than that of the mineral filler content (the interval is 1%). Therefore, the passing rate of the 1.18-mm sieve has a more remarkable influence on the air voids of the mixture.