esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Compressor Test Rig for Immobilization of the Stall Cell
## 5 Conclusions  
To facilitate flow measurement within a stall cell, a low-speed counter-rotating compressor test rig was meticulously designed, fabricated, and subjected to rigorous testing. Specifically, the findings have substantiated the following:  
1. The initial results validate our assertion that immobilizing the stall cell at a specific circumferential position can be achieved by employing an appropriate rotating speed ratio. The identified location of the stall cell is situated in the top-left quadrant, encompassing approximately 40% of the entire circumference. Moreover, precise adjustment of its circumferential position can be accomplished through the utilization of a circumferential distortion plate positioned near the compressor outlet.  
2. In order to gain comprehensive insights into the near-wall flow structure within the stall cell, oil flow visualization measurements and dynamic wall pressure measurements were conducted. The experimental results demonstrated the presence of a distinct stall cell structure on the casing wall, while the hub wall exhibited only a certain deflection and gas backflow due to the inability to establish streamline flow between the blades. The circumferential extent of the stall leading edge area is greater than that of the stall trailing edge area, with vortex structures observed in the central region of stalling.  
3. Dynamic wall pressure sensors were employed for measuring stall pressure signals in close proximity to test rig walls. Notably, it is evident from these observations that post-stall fluid exhibits non-uniform circumferential pressure distribution extending up to 100% upstream and downstream.  
Writing--original draft preparation, Q.Z.; writing--review and editing, G.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.  
Figure 24: The pressure contour diagram of the casing rotating stall flow field.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 3 An Enhanced Generative Adversarial Network for SR  
In this section, we begin to describe our network for SR tasks. As shown in Figure 1, the structure of our algorithm mainly consists of two networks, including the generative network structure and the adversarial network structure. According to the typical SR approach using GAN methods, our generative network can be divided into three modules. On the one hand, inspired by the LapGAN [20], we employ an improved input using the Laplacian pyramid as our pre-trained module to refine image features. Since the feature maps of convolutional extraction may be \"influenced\" by the next layer, we add some convolutional skip-connections to obtain more latent information. Additionally, the loss function is perceptual for our generative network to optimize its parameters. On the other hand, we improve the discriminative network summarized by [PERSON] et al. [18] to design our structure.  
Our discriminative network is constantly trained to distinguish the synthesized images from the generator, and the real images from the data distribution.
## 4 Discussion  
In this study, we investigated the production of Cantonese tones by advanced Mandarin-speaking learners who had been immersed in a Cantonese-speaking environment for an average of seven years. Both acoustic analysis using computer software and perceptual evaluation by native listeners were employed to analyse the speech data. The results partially supported the two hypotheses that we proposed. Firstly, while the acoustic data showed that none of the six tones produced by L2 Cantonese speakers was native-like, their production of T1 and T2 was relatively good. This observation was confirmed by the native listeners' judgements, as the production accuracy of T1 and T2 was above 80% and the accuracy of the remaining tones was extremely low (ranging from 8.9% to 52.5%). Secondly, our data supported the hypothesis that the perceptual evaluation accuracy (correct or incorrect) can be predicted based on the acoustic features of the tones. The effect of total distance on the native listeners' perceptual evaluation was robust, and the listeners' judgements were also influenced by the mean F0 (\(DCT1\)), the F0 slope (\(DCT2\)) and the F0 curvature (\(DCT3\)) of the produced tones. The acoustic and perceptual results revealed differences in the L2 learners' Cantonese tone production from that of native speakers. Note that the L2 learners had been immersed in a Cantonese-speaking environment for an average of seven years at the time of the recordings, and they were all identified as fluent speakers of Cantonese. Despite sufficient linguistic input from the environment, they still failed to acquire the tonal system of Cantonese, indicating that being speakers of an L1 tonal language does not necessarily guarantee the successful acquisition of a different tonal system in an L2 [2]. Regarding the source of difficulty, we believe that it was due to the phonetic similarity/dissimilarity between the L1 and the L2 tonal systems. As detailed in Section 1.2 and Table 1, T1 and T2 are phonetically similar in Cantonese and Mandarin, and the other tones in the two languages differ in both slope and level; thus, it is possible that the immigrants had assimilated Cantonese T1 and T2 to their L1 counterparts. Once the link had been established, the immigrants would use only one category to represent the L1 and L2 tonal category (e.g., T1 or T2). This explains the high accuracy of their T1 and T2 production, as evaluated by the Cantonese listeners. One exception was the T4 in Cantonese, which was judged as being pronounced correctly in more than 50% of the tokens. If we consider the actual pronunciation of Mandarin tones, the half-third sandhi tone in Mandarin resembles the F0 level and the slope of Cantonese T4, both of which share the value of 21. Consequently, some immigrants might also have created a merged tonal category for the Mandarin half-third
## 5 Discussion
### Analysis of Performance Limitations and Worse Case  
The PCC\({}^{2}\)LS algorithm demonstrates excellent performance in solving the MCDSP, particularly in terms of solution quality. Experimental verification shows that it consistently finds better solutions or achieves the best-known solutions compared to other state-of-the-art heuristic algorithms across a wide range of standard instances. However, despite PCC\({}^{2}\)LS's efficiency and stability, it is essential to consider its performance limitations and potential worst-case scenarios.  
Regarding processing time for large-scale graphs, PCC\({}^{2}\)LS typically finds the best solution within 30 s on most benchmark instances, performing exceptionally well in smaller-scale scenarios. Yet, as graph sizes increase further, although it can usually be completed within 70 s, it may approach or exceed acceptable waiting time thresholds for some real-world applications with stringent response-time requirements. This limits its applicability in real-time systems demanding extremely high response times.  
In terms of parameter sensitivity, the algorithm's performance hinges on various parameters such as iteration count, probability parameter (_fp_), and perturbation vertex ratio. Improper parameter settings can diminish algorithmic efficiency or degrade solution quality, necessitating meticulous parameter tuning tailored to specific problem instances, which can be time-consuming.  
In worst-case scenarios, particularly with extremely dense graphs, finding a smaller set of connected dominants becomes exceptionally challenging. Each new vertex addition brings a significant number of neighboring vertices, potentially prolonging the algorithm's exploration of the solution space or even preventing it from finding a satisfactory solution within the given time constraints.  
However, for instances with unique structures (e.g., sparse graphs or those with specific fixed patterns), PCC\({}^{2}\)LS may not be optimized for specialized purposes, resulting in performance inferior to algorithms explicitly designed for these structures.  
In summary, while PCC\({}^{2}\)LS generally performs well, it encounters performance limitations and challenges when faced with extreme conditions or specific problem types. Further research and optimization efforts are necessary to address these limitations effectively.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 3 Wind Speed Characteristics
### Correlation Between RS and WS  
The high-resolution wind speed of HSR is mapped to the time scale of WSs while considering the various monitoring intervals of wind speed at RSs and WSs. To acquire the correlation of wind speed in the same time period, we created four groups of RSs and WSs, namely, K1005-58237, K1066-58242, K1245-58352, and K1256-58356. For quantitative characterization, the Pearson correlation coefficient was utilized. The correlation coefficients for the four groups of wind speeds from 5 min to 24 h are displayed in Figure 3. Each group's linear fitting is represented by a straight line, and the goodness of fit is shown by \(R^{2}\). Overall, as the interval sampling time is increased, the connection between WS and RS grows. The best correlation was found for K1256-58356. Due in large part to the K1005  
\begin{table}
\begin{tabular}{c c c c} \hline
**HSR Stations** & **Weather Stations** & **Distance (km)** & **Surrounding Environment** \\ \hline K1005 & 58,237 & 7.2 & Yangtze River, bridges \\ K1066 & 58,242 & 7.7 & Mountain valley \\ K1245 & 58,352 & 11.8 & Yangcheng Lake, Tunnel \\ K1256 & 58,356 & 8.6 & Yangcheng Lake \\ \hline \end{tabular}
\end{table}
Table 1: HSR stations, weather stations, the distance between them, and the surrounding environment.  
Figure 2: The 10 min average wind speed of the HSR station and the ground WS at the same timeperiod.  
station's location on a bridge 50 m above the ground, K1005-58237 exhibits the poorest correlation. The correlation between the locations is modest, all lower than 0.4, for the time period below 6 h. The association between the wind speed at the RS and the WS dramatically increased across the time range of 6 h to 24 h, displaying a fluctuating increase. The connection between RS and WS can approach 0.7 when the wind speed time scale is extended to 24 h.
## 3 Modifications to the Reference Network
### Small-Target Detection Layer  
The blueberry dataset used in this paper includes scenes with long distances and severe branch and leaf occlusion, with a large number of small-sized blueberry fruits. The feature maps at the lower pyramid levels, P1 and P2, of the backbone network contain rich information about small targets; however, this information has not been fully utilized. To improve on this point, a small-target detection layer is added in this paper. The proportion of small targets in the image is very small, so when detecting, the model needs a higher spatial resolution to capture the features of small targets; that is, a 160 \(\times\) 160 feature map detection layer needs adding to the original network to detect small blueberry targets larger than 4 \(\times\) 4 pixels, thereby improving the detection capability for small blueberry targets at long distances and in severe occlusion scenes. The structure diagram is shown as a dashed box in Figure 1.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair
### Time Consumption Analysis  
We used Tensorflow [26] to implement our proposed network model. During testing, the average processing time of a single HomographyFpnNet model on a GPU was 3.48 ms. Using the HomographyFpnNet model in each stage of the hierarchical method, the total computational complexity is given by,  
\[d_{e}=(l_{m}+l_{w})*n \tag{1}\]  
where \(d_{e}\) is the end-to-end delay of the whole hierarchical model, \(l_{m}\) is the average latency for each HomographyFpnNet model, \(l_{w}\) is the warping over-head to prepare a new image pair, and \(n\) is the number of stages used in the hierarchical model.  
Table 1 shows the time consumption of our hierarchical HomographyFpnNet model on a GPU. Our three-stage hierarchical HomographyFpnNet model is shown to have an average processing time of 10.8 ms on a GPU. The real-time processing speed satisfies the requirements of wall-climbing robots.
## 1 Introduction
Moreover, the products of NH3-containing biogas combustion (NOx) are among the most relevant compounds for air pollution. Due to its high solubility in water in the form of NH4+, NH3 can be easily removed by water and acidic-water washing or, alternatively, adsorbed onto an activated carbon system [16]. Water vapor contained in biogas can lower its heat value, thus reducing the energy performance of combustion engines. Moreover, the condensation of water and hydrophilic compounds, such as H2S, inside the combustion engines can lead to corrosion problems and equipment malfunctions. Different methods can be used to dehydrate biogas water, but the most used are condensation by cooling and adsorption by dryers [16]. From an industrial point of view, the choice of the biogas pre-treatment system is not only related to the highest yield of impurity abatement efficiency but also to a series of considerations and needs for the full-scale AD plant. Of course, the first evaluation is linked to the level of contaminant concentration to be achieved as a function of biogas or biomethane end-use. Then, economic evaluation based on the initial costs of the equipment and the fixed management costs is another important factor. Management costs depend on the number of operators necessary for the equipment functioning, machinery maintenance, and days of plant downtime, as well as energy, water, and chemical consumption. Using automated technologies that do not require specialized operators and are easy to maintain is certainly advantageous. Likewise, using space-saving equipment is preferable. Consequently, the adoption of a specific biogas pre-treatment system is dictated by the balance of all the plant's needs. The raw biogas purification process has been extensively studied on a laboratory and pilot scale, but only a few data on an industrial scale are available. Furthermore, the published studies relating to industrial-scale plants have specifically analyzed only some steps of the entire biogas purification process [31]. The aim and novelty of this work is to describe the logical path followed for the choice of the technological solutions adopted by a currently working large-scale FW AD plant for the pre-treatment of biogasbefore its upgrading to biomethane. Compared with other published studies, a detailed analysis of each cleaning step of the biogas purification process at an industrial-scale plant is reported in this work. The reasons underlying the adopted technological choices and the optimization of the various steps making up the biogas pre-treatment system are discussed. Starting from the analytical characterization of raw biogas, the best operating conditions have been determined through the quantification of biogas components after each stage of the cleaning system.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### Research Progress of Piezolectric Materials
Overall, the piezoelectric material has great potential for development and application. To protect the environment, piezoelectric ceramics are now developing towards lead-free direction such as NaNbO\({}_{3}\) and K\({}_{0.5}\)Na\({}_{0.5}\)TiO\({}_{3}\)[33]. The sintering of these kinds of material is a challenge. In 2005, [PERSON] et al. [34] prepared (Na\({}_{0.5}\)K\({}_{0.5}\))NbO\({}_{3}\)-LiTaO\({}_{3}\) using conventional mixed oxide route and found it was a good lead-free piezoelectric ceramic. Its piezoelectric constant \(d_{33}\) was up to 200 pC/N, and the plane electromechanical coefficient \(K_{p}\) reached 36%. The doping amount of LiTaO\({}_{3}\) affected the phase of the material. In 2006, [PERSON] et al. [35] studied (Na\({}_{0.5}\)K\({}_{0.5}\))NbO\({}_{3}\) by using the spark plasma sintering (SPS) method with advantage of low-temperature sintering. The Curie temperature was 395 \({}^{\circ}\)C and the highest dielectric constant \(\varepsilon_{r}\) was 606, but the piezoelectric constant (148 pC/N) was lower than that in the report of [PERSON] et al. In the same year, [PERSON] et al. [36] studied the [Bi\({}_{0.5}\)(Na\({}_{1-x-y}\)K\({}_{x}\)Li\({}_{y}\))\({}_{0.5}\)]TiO\({}_{3}\). They found that the piezoelectric constant (231 pC/N) and the maximum dielectric constant (1190) reached the peak when \(\chi/\gamma=0.2/0.1\). It should be noted that doping could increase the Curie temperature or the quality of grain growth, but \(d_{33}\) and \(K_{p}\) would decrease slightly [37; 38]. In 2016, [PERSON] et al. [39] studied the effect of Fe on the piezoelectric properties and fatigue behavior of (Bi\({}_{0.5+x/2}\)Na\({}_{0.5-x/2}\))\({}_{0.94}\)-Ba\({}_{0.06}\)Ti\({}_{1-x}\)Fe\({}_{x}\)O\({}_{3}\) (BNBT-xFe). They found that the induction of Fe in the electric field resulted in the decrease in the ferroelectric-relaxor transition temperature, but the strain would increase. In addition to doping, optimizing the preparation of materials could also improve the performance of piezoelectric materials [40]. Table 1 lists some parameters of the piezoelectric materials. From the table, traditional piezoelectric ceramics, lead zirconate titanate (PZT), mostly have high piezoelectric constants. The piezoelectric properties of lead-free piezoelectric materials can be enhanced by doping. There are many reports on doping showing the importance of doping for piezoelectric materials. The max piezoelectric constant \(d_{33}\) stood at 295 pC/N. The commonly used preparation method is the solid-state liquid-phase sintering technique. In addition, the planar \(K_{p}\) and thickness \(K_{t}\) electromechanical coupling coefficients were between 30% and 50%. However, the maximum dielectric permittivity differed significantly. The reason for this might because of the difference properties of the piezoelectric ceramic or ratio.
### Objectives  
The objective of the present study was to compare the functional contributions of the thermal inertia of a water-covered roof with that of an inverted roof, as well as to examine improvements in comfort levels of the interior environment and the impact on the energy demand of the areas adjacent to the roof. First, two roofs of two buildings located in Alicante [4,34] and San Vicente del Raspeig (Spain) were monitored during the full cycle of 2014. Surface temperature sensors were placed in all the material layers of both enclosures [35]. In the case of the inverted roof, the sensors were installed when the roof was under construction. In the case of the water-covered roof, the sensors were incorporated using a non-destructive system which avoided damage to the waterproof layers. It was therefore possible to graph curves of temperature gradients, relative humidity, solar radiation, etc., at each time of the year, as well as the variations in dynamic regime. Based on previous parameters obtained from diverse sources and laboratory tests on the building materials used in both architectural works, weproceeded to calculate the thermal parameters in dynamic regime that affected the fluctuations of the indoor air temperature \(T_{i}\) and their impact on annual energy demand.  
Subsequently, an energy simulation [36] was performed in the Design Builder tool, which uses the EnergyPlus calculation engine, after the prior calibration of the model. Some modifications were designed in the roofs' construction sections. The inverted walkable flat roof was modified and turned into a water-covered roof. In addition, the position of the insulation was reversed and transformed into a conventional roof. The water-covered roof first became an inverted flat roof and was then turned into a conventional flat roof. This is how 6 scenarios were comparatively analyzed. By simulating the possible combinations of roof sections for each space, we could thus obtain various conclusions regarding the comfort of the indoor environment and annual energy demand, based on the hypothesis of the benefits of the water-covered roof. The following objectives were pursued:  
* Analyze whether incorporating materials in the construction section implies a possible accumulation of heat and regulation over time of indoor air temperature variations.
* Confirm whether water-covered roofs, which present greater thermal inertia than inverted flat roofs, improve thermal behavior.
* Quantify the values of thermal loads and energy demand for heating and cooling, in order to approximate the feasibility of this type of roofing in climates such as that of the Spanish Mediterranean coast in subsequent studies.
## 4 Digital Model of the Area of Interest
### Steps for Software Implementation  
Steps for implementation of the application:  
1. step of the software implementation is the selection of significant points in the monitored area.
2. step is determining the coordinate system (determining the points \(O\), \(O^{\prime}\), \(O_{x}O_{y}\) and \(Origo\) ) (with this step, we locate the task).
3. step is obtaining a digital terrain model of the monitored area. Determination of input parameters (\(Origo\), \(T_{Origo}\), \(S_{Origo}\)). This input information is necessary for the application's software implementation to design the network of sensory units.
4. step--the output of the computations are tables of polygons \(P_{Origo}\), \(Q_{Origo}\). \(P_{Origo}\) is the polygon of the area of interest. \(Q_{Origo}\) is a polygon of sensory units network.  
Several solutions are available for the software implementation of a GIS network of sensory elements. One solution is to apply MATLAB v. R2022b and above with the mapping toolbox. This system houses digital models of the globe with functions that enable geographic calculations. Another option is to use the Python programming language. There are extensions for this language, such as ArcGIS, for geographic applications, see [13].
## 4 Computer Simulations
### Simulation: Effects of \(\hat{b}\) ( \(\hat{f}_{S}\))  
In this simulation, estimated effective coherence length \(\hat{L}_{C,eff}\) was set as 36 fringes, _SNR_=30 dB and estimation error of the zero crossing period \(\hat{b}\) was varied from -10 % to 10 % when \(b\)=10 (\(f_{S}\) =20 samples/fringe) to see the effect of estimation error of the zero crossing period \(\hat{b}\) on the performance of the proposed signal processing algorithm. The estimation error of the zero crossing period \(\hat{b}\) is defined by:  
\[\text{Estimation error of }b=\frac{\hat{b}-b}{b}\times 100[\%] \tag{27}\]  
where \(b\) is the half the sample rate \(f_{S}\). In Figure 14, it can be shown that RMS error of fine tuning was not sensitive to the estimation error of \(\hat{b}\) within the range of \(\pm\) 6 % estimation error of \(\hat{b}\) and especially, RMS error of 0.001 [fringe] was obtained within the range of 2% estimation error of \(\hat{b}\). But, on the range of \(\pm\) 7 %\(\sim\pm\)10 % estimation error \(\hat{b}\) RMS error increased dramatically to 0.25 fringe.  
Figure 14: Change of RME error as a function of estimation error in zero crossing period.  
This is presumably due to the fact that \(i(n)\) and test cross-correlation \(i_{test}(n)\) (Equation 24 or Equation 25) started to be out of phase and RMS error increased fast.