esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development and Validation  
### Prototype Development  
After theoretical analysis and FEM simulation, a prototype is made and taken to the test bench to conduct the relevant experiment for validation. We take the 1# stage driving gear as an example. Figure 14 shows the tooling and sample, with the corresponding gear hob and a grinding wheel. From the appearance of the sample, the root fillet curve, the involute part, and the chamfer are clearly presented, which meets the expectation of design and process.  
With the process of hobbing and form grinding, the accumulated errors of each gear are shown in Figure 15. The difference in modulus in the 1# and 2# stage gears leads to a slight change in Fr (concentricity deviation) and Fp (total pitch deviation). Usually, the cumulative error increases slightly with the increase in gear teeth number and tip circle diameter. The results show that all the gears are all at the accuracy level of 6. The gear  
Figure 14: Tooling and sample of the gear. (**a**) Gear hob. (**b**) Grinding wheel. (**c**) Sample of the 1# stage driving gear.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tooth Root Bending Stress** & \
\begin{tabular}{c} **Fatigue Life Under S-N** \\ **Curve** \\ \end{tabular} &
\begin{tabular}{c} **Equivalent Damage Rate** \\ **of Every Single Load** \\ \end{tabular} \\ \hline
1\# stage Driving Gear & 200.99 MPa & 16,477,915 & 0.000107182 \\
1\# stage Driven Gear & 186.55 MPa & 87,526,966 & 0.002301166 \\
2\# stage Driving Gear & 384.44 MPa & 250,309 & 0.001189999 \\
2\# stage Driven Gear & 364.47 MPa & 283,375 & 0.000000086 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fatigue damage status of the gear train.  
adopts 20 CrMnTi material, with the surface being carburized, quenched, and tempered. The surface hardness is HRC 62-65, while the core hardness is HRC 30-45, and the contact fatigue limit corresponds to 1500 MPa.
## 3 Results  
During the concentric and eccentric phases of the heel-raise exercise, there were increases of the RMS EMG amplitudes in the soleus and tibialis anterior muscles (Table 2, \(p\)'s ranged between 0.043 and 0.002), lower ankle joint angle (\(p\)'s ranged between 0.145 and <0.001), angular velocity of the ankle joint (\(p\)'s ranged between 0.687 and <0.001), normalized GRF (\(p\)'s ranged between 0.959 and 0.026) and mechanical work (\(p\)'s ranged between 0.959 and 0.001) in the repaired legs when compared to ones of the non-injured legs (Figure 2a-d). The EMG results of the medial gastrocnemius and soleus muscles were correlated with the kinematics or kinetics results (\(\mathrm{r_{s}}=0.490\) and \(-0.560\)) (Table 3). Furthermore, the joint angular velocity and mechanical work data in the eccentric phase were correlated with the normalized jumping performance (\(\mathrm{r_{s}}=0.575\) and \(-0.471\)) (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RMS EMG** & & **Repaired Leg** & **Non-Injured Leg** & \(p\)**Value** \\ \hline \multirow{2}{*}{TA} & CON & 0.20 (0.10–0.55) & 0.16 (0.05–0.36) & 0.027 * \\  & ECC & 0.13 (0.07–0.48) & 0.12 (0.04–0.32) & 0.049 * \\ \hline \multirow{2}{*}{SOL} & CON & 0.99 (0.55–1.53) & 0.86 (0.48–2.45) & 0.023 * \\  & ECC & 0.75 (0.31–1.28) & 0.49 (0.34–1.13) & 0.002 * \\ \hline \multirow{2}{*}{MG} & CON & 0.95 (0.59–1.37) & 0.89 (0.47–1.47) & 0.351 \\  & ECC & 0.64 (0.45–0.95) & 0.54 (0.28–1.06) & 0.204 \\ \hline \multirow{2}{*}{LG} & CON & 0.87 (0.43–1.25) & 0.91 (0.48–1.51) & 0.647 \\  & ECC & 0.57 (0.29–0.86) & 0.54 (0.35–0.93) & 0.744 \\ \hline \multirow{2}{*}{nOLH (\%)} & \multicolumn{2}{c}{0.75 (0.40–1.01)} & \multicolumn{2}{c}{0.95 (0.68–1.10)} & \multicolumn{1}{c}{<0.001 *} \\ \cline{2-5}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\ \end{tabular}
\end{table}
Table 2: Results of normalized RMS EMG data during the concentric and eccentric phases of the one-legged heel-raise exercise, and normalized hopping distance.
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 4 Matheuristics Based on Compact MILP Formulations
### VND Local Search Matheuristic
#### 4.5.2. MILP Neighborhoods  
Multiple types of large neighborhoods can be defined with the variable fixing strategies previously defined. We consider three main types of neighborhoods:  
* \(\mathcal{N}_{i}^{\mathit{lcc}}:\) For a given technician \(i\), the neighborhood reoptimizes the routing of technician \(i\) considering any job \(j\), even those that were planned in other routes. In other words, \(\mathcal{F}_{i,j}^{\mathit{assign},\ll}(x)\) and \(\mathcal{F}_{i,j,\prime}^{\mathit{order}}(x)\) are applied on the current solution \(x\) for all \(i^{\prime}\
eq i\) and for all \(j,j^{\prime}\in\mathcal{J}\).
* \(\mathcal{N}_{i,i^{\prime}}^{\mathit{pair}}:\) For a given pair of technicians \(i,i^{\prime}\), the neighborhood reoptimizes only the routing of technicians \(i\) and \(i^{\prime}\) considering only the jobs \(j\) planned in the current solution for \(i\) and \(i^{\prime}\) and the jobs that are not assigned. In other words, the planning of the other technicians \(i^{\prime\prime}\) is not changed, and \(\mathcal{F}_{i,j}^{\mathit{assign},\ll}(x)\) and \(\mathcal{F}_{i,j,\prime}^{\mathit{order}}(x)\) are applied on the current solution \(x\) for all \(i^{\prime\prime}\
otin\{i,i^{\prime}\}\) and for all \(j,j^{\prime}\in\mathcal{J}\).
* \(\mathcal{N}_{j}^{\mathit{jobs}}\): For \(J\) a subset of jobs, planning is reoptimized fixing the assignments and the order of the jobs in \(\mathcal{J}-J\). In other words, \(\mathcal{F}_{i,j}^{\mathit{assign},\ll}(x)\) and \(\mathcal{F}_{i,j,\prime}^{\mathit{order}}(x)\) are applied on the current solution \(x\) for all \(i\) and for all \(j,j^{\prime}\in\mathcal{J}-J\). We note that classical neighborhoods are extended with previous MILP neighborhoods. The 2-opt and k-opt neighborhoods are included in neighborhoods \(\mathcal{N}_{j}^{\mathit{jobs}}\). Insertion neighborhood, moving a job from one technician route to another, can be provided by \(\mathcal{N}_{i}^{\mathit{lcc}}\) or \(\mathcal{N}_{i,\prime}^{\mathit{pair}}\) neighborhoods.
## 4 Experimentation and Results
### Comparison of Compression Effects under Different Metrics
We observed that at lower compression ratios (below about 30), SZ compression has higher reconstruction performance than our method, reaching over 60 dB. However, as the compression ratio increases, to maintain the compression performance of lossy compression, SZ gradually loosens its error control, leading to a decrease in reconstruction performance. Therefore, at higher compression ratios, the reconstruction performance of our method is superior to SZ.
## 2 Materials and Methods
### Characterization of the Polymeric Systems
#### 2.3.5 KineticDrug Release Studies  
The release of the active substances (Gly, LA) from the polymeric systems, CS-Gly, CS-LA, and CS-Gly-LA, was studied according to the methods described in the literature with slight modifications [40; 41; 42]. Simulated gastric (SGF, pH = 1.6, sodium taurocholate 0.08 mM, lecithin 0.02 mM, peppsin 0.1 mg/mL, sodium chloride 34.2 mM and hydrochloric acid 25.1 mM) and intestinal (SIF (simulated intestinal fluid), pH = 6.5, sodium taurocholate 3 mM, lecithin 0.2 mM, maleic acid 19.12 mM, sodium hydroxide 34.80 mM, and sodium chloride 68.62 mM) fluids were used [43].  
A weighted amount of CS-Gly, CS-LA, and CS-Gly-LA microparticles, was introduced in 1.8 mL simulated media (SGF, SIF) and kept at \(37\pm 1\,^{\circ}\)C, under stirring. At constant timelines (at each 60 min during 2 h for SGF and at each 60 min for 8 h for SIF), 0.5 mL of simulated media was extracted and analyzed in the chromatographic conditions mentioned above, using a HPLC apparatus type Agilent Technologies 1200 Series, equipped with UV detector type DAD G1315A and injector type Rheodyne. Based on the peak area corresponding to Gly and LA and on the corresponding specific calibration curve, the amount of active substances (Gly, LA), released from polymer matrix at each time interval was determined (\(\mathrm{m_{t}}\)). The degree of release (DR%) was calculated using the following formula:  
\[\mathrm{DR\%=(m_{t}/m_{0})\times 100} \tag{5}\]  
where:  
\(\mathrm{m_{0}}\) = the initial amount of active substance (Gly, LA) loaded into microparticles (mg)  
\(\mathrm{m_{t}}\) = the amount of active substance released, at a specific time, in simulated media (mg)  
All of the experiments were performed in triplicate and the results were expressed as mean \(\pm\) SD.
## 6 Conclusions  
This study analyzed the synchronization noise immunity of Vilnius and Colpitts chaos oscillators in different configurations. The results showed that the Vilnius oscillator with \(v_{\text{C2}}\) as the synchronization signal exhibited the most robust synchronization, maintaining low cross-correlation between information-carrying signals and higher noise resistance. The Colpitts oscillator with \(v_{\text{C1}}\) also performed well but required compensation during detection due to synchronization imbalance. In contrast, configurations using \(v_{\text{R1}}\) as the synchronization signal were highly noise-sensitive, leading to weaker synchronization stability and higher BER.  
A QCSK communication system was implemented using these synchronization configurations, demonstrating that the choice of synchronization signal directly impacts BER performance. The Vilnius \(v_{\text{C2}}\) and Colpitts \(v_{\text{C1}}\) configurations achieved the lowest BER, making them the most suitable for chaos-based communication systems.  
A threshold-based correction mechanism was introduced to address synchronization imbalances. Without a threshold, correlation coefficient disparities at higher SNR values caused an imbalance in detected bits. The applied threshold successfully reduced detection errors and improved BER, demonstrating the importance of compensation techniques in ensuring balanced data detection in chaos-based systems. These findings highlight the need to carefully select synchronization configurations and optimize detection strategies to enhance the reliability of QCSK communication systems.  
Conceptualization, D.C. and D.K.; Data curation, D.K.; Formal analysis, D.C. and R.B.; Funding acquisition, D.K.; Investigation, D.C.; Methodology, D.C. and D.K.; Project administration, D.K.; Resources, D.K.; Software, D.C.; Supervision, D.K.; Validation, D.C. and R.B.; Visualization, D.C.; Writing--original draft, D.C.; Writing--review and editing, R.B. and D.K. All authors have read and agreed to the published version of the manuscript.  
This research and publication was supported by 2024 Riga Technical University's Doctoral Grant program.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** This research was performed at Riga Technical University, Space Electronics and Signal Processing Laboratory--SpacESPro Lab.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 1 Introduction
### Real-World Challenges to Drone-Based MANETs  
As well as remaining airborne (which draws on the limited battery power), drones must (a) carry the payload (which also incurs operational cost in the form of increased energy demand for flight operations) as well as (b) operate said payload (further adding to the power demand of the drone). And while the power issue (i.e., the aspect of physically moving, operating, and keeping a drone airborne) has at least potential solutions (such as, e.g., powering/recharging drones with High Energy Lasers (HELs) from the ground [21]) there are additional functional aspects to consider. For example, maintaining a fully connected network with the desired redundancy requires cooperation between the nodes. Depending on the scenario (see Section 2.3) this may have to happen under sub-optimal or even adversarial conditions. There is furthermore the practical issue of allocating bandwidth to clients who may, depending on the application, generate a significant demand (in the case of, e.g., real-time high-definition video transmissions). Finally, there is the matter of regulation and legal frameworks [22]. It stands to reason that a larger drone-based MANET would rely on autonomously operating drones, which is currently a problematic setup due to the lack of clarity or permissions (countries are currently scrambling to put the necessary laws and oversight institutions in place [23; 24; 25; 26]). The next section discusses some of the most prominent challenges and provides a context and scope for the focus and the modeling choices of our work.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO
### Impedance Sensitivity Analysis
#### 3.1.1 Impedance Sensitivity of Voltage Drops  
When the fundamental voltage drops from 1.0 pu (636.4 V) to 0.6 pu while the other parameters in (22) keep the same as Appendix A, the impedance characteristics dynamics is shown in Figure 10. The reactance \(X_{PMSG}\) and resistance \(R_{PMSG}\) are listed below separately. It can be concluded that the \(X_{PMSG}\) changes from inductive to capacitive, crossing zero at 50 Hz. \(R_{PMSG}\) are negative around 50 Hz. To be noticed that \(X_{PMSG}\) gets larger along with voltage drops lower, while the resistance curve moves to the positive direction. Larger \(X_{PMSG}\) means lower resonant frequency but larger \(R_{PMSG}\) brings bigger total resistance, which means the influence on stability depends on which one is dominant.  
Figure 10: Impedance sensitivity of voltage (Drops). (**a**) Reactance \(X_{PMSG}\); (**b**) Resistance \(R_{PMSG}\).  
Figure 9: Frequency components in output currents. (**a**) Magnitude; (**b**) Phase.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 3 Methodology
### Dataset Description  
The dataset we used in this study is images from a few railway sleepers collected from Busan station, South Korea. We captured 113 images with a resolution of \(3500\times 2500\) pixels by using a Sony a7r III mirrorless; manufactured by Sony Electronics, San Diego, CA, USA. camera. As the resolution of the images is so high, it may increase the computational complexity, so we could not use the high-resolution images directly in the Deep Learning model. Furthermore, it was also not convenient to resize the images into some smaller resolutions because of the need to maintain the image quality. To overcome this issue, we split each image into nine different parts and got 1017 images. For splitting the images into nine different parts, we first divided the image into three different rows and three different columns. As a result, we obtained 9 different 3 \(\times\) 3 grids from one single image. After that, we collected the coordinates of those grids and cropped the images based on the grids. As we split the images by dividing them into independent grids based on the coordinates, it helped us to generate non-overlapping image patches from one single image. However, some images among these 1017 images do not contain sleepers. Instead, they contain the lab floor, bucket, and other noises. So, we eliminated those images, and finally, our dataset included 660 images. Later we manually annotated our dataset for the segmentation task.  
Figure 1: Methodology of the proposed work. (The vector images in this diagram are completely random and do not reflect the original data. The photos were collected from [58]).  
Examples of a few images (original and the mask) of our utilized dataset are displayed in Figure 2.
## 4 Results and Discussions
### Effect of Different Injection Temperatures on CO2-ECBM  
#### 4.3.1 Variation of Reservoir Temperature  
Figure 15 illustrates the reservoir temperature distribution. When the injection temperature is the same as the reservoir temperature, the temperature increases near the injection well due to the adsorption of CO2, and the temperature near the production well decreases due to CH4 desorption. After the gas injection, CO2 adsorption induced an exothermic effect and the reservoir temperature near the injection well rose sharply [48]. Meanwhile, the range affected by temperature gradually increased due to heat conduction. CH4 desorption promoted a reduction in reservoir temperature near the production well.  
In addition to the thermodynamic effect of Hydraulic-Mechanical interaction and the temperature effect of non-isothermal adsorption, the internal energy change in the reservoir also has the Thermal-Hydraulic coupling of the fluid itself caused by the change of CO2 phase state, that is, the phenomenon of liquefaction exothermic and gasification  
Figure 14: (**a**): Cumulative production. (**b**): Permeability change at point B.  
Figure 15: Reservoir temperature distribution.  
endothermic [49]. In thermochemistry, it is the energy absorbed or released by a substance during a change of state (phase change) without a change in temperature that we call the latent heat of phase change. The heat which is required to melt a certain quantity of a solid at the melting point into a liquid at the same temperature is called the latent heat of fusion. The conversion of the liquid into vapour requires an amount of 'latent heat' which is generally much greater than the latent heat of fusion of the same substance [50]. In the process of CO\({}_{2}\) vaporization, the distance between molecules increases, the molecular force does negative work, and the molecular potential energy increases; where the molecular potential energy is the energy that molecules have with respect to their relative positions due to the mutual forces that exist between them. This process may release or absorb large amounts of heat, but this phenomenon has not been considered in this and previous studies.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Single Measurement  
#### 3.1.1 Metric of RSS  
In this section, we select the metric of RSS to conduct the indoor localization. For simplicity, we select the COST231 model to depict the propagation property, which is also considered in [32; 33] as a good compromise among practicability, precision and simplicity. The COST231 model is shown in (10).  
\[\widehat{P_{ik}}=P(d_{0})-10\beta\log_{10}(\frac{d_{ik}}{d_{0}})-P_{w}-\chi_ {1} \tag{10}\]  
where \(\widehat{P_{ik}}\) and \(P(d_{0})\) are the RSSs received (in dBm) at the locations with \(d_{ik}\) and \(d_{0}\) meters, respectively, from the AP. \(d_{0}\) and \(d_{ik}\) are the reference distance and distance between the \(k\)-th AP, \(\left(x_{k}\,\ y_{k}\right)\) and \(\theta_{i}\). \(\beta\) is the path loss exponent. \(P_{w}\) is the path loss caused by the walls. \(\chi_{1}\) is a random variable following the Gaussian distribution with the mean zero and variance \(\sigma_{1}{}^{2}\), \(N\left(0,\sigma_{1}{}^{2}\right)\). The PDF of \(\mathbf{P}_{ik}\) with respect to \(\theta_{i}\) is:  
\[f_{\theta_{i}}\left(P_{ik}\right)=\prod\limits_{k=1}^{m}\frac{1}{\sqrt{2\pi} \sigma_{1}}\exp\left(-\frac{\xi_{i1}{}^{2}}{2\sigma_{1}{}^{2}}\right) \tag{11}\]  
where \(\xi_{i1}=\widehat{P_{ik}}-P(d_{0})+10\beta\log_{10}(d_{ik})+P_{wf}\). \(m\) is the AP number. Based on (5), we can calculate that:  
\[\left\{\begin{array}{c}J\left(\theta_{i}\right)=\left[\begin{array}{cc}J_{ xx}\left(\mathrm{RSS}\right)&J_{xy}\left(\mathrm{RSS}\right)\\ J_{yx}\left(\mathrm{RSS}\right)&J_{yy}\left(\mathrm{RSS}\right)\end{array} \right]\\ J_{xx}\left(\mathrm{RSS}\right)=\rho_{1}\sum\limits_{k=1}^{m}\left[\frac{\cos \alpha_{ik}}{d_{k}}\right]^{2}\\ J_{xy}\left(\mathrm{RSS}\right)=J_{yx}\left(\mathrm{RSS}\right)=\rho_{1}\sum \limits_{k=1}^{m}\frac{\sin\alpha_{ik}\cos\alpha_{ik}}{d_{k}{}^{2}}\\ J_{yy}\left(\mathrm{RSS}\right)=\rho_{1}\sum\limits_{k=1}^{m}\left[\frac{\sin \alpha_{ik}}{d_{k}}\right]^{2}\end{array}\right. \tag{12}\]where \(\alpha_{ik}=\arctan\frac{\left|y_{i}-y_{k}\right|}{\left|x_{i}-x_{k}\right|}\). \(\rho_{1}=\left(\frac{10\theta}{\sigma_{1}\ln 10}\right)^{2}\). The geometric relationship between the locations \((x_{i},y_{i})\) and \((x_{k},y_{k})\) is shown in Figure 1.  
Therefore, based on (8), the localization error bound by using the metric of RSS with respect to \(\theta_{i}\) equals:  
\[\begin{array}{c}V_{m}\left(\text{RSS}\right)=\frac{I_{yy}(\text{RSS})+I_{xx }(\text{RSS})}{I_{xx}(\text{RSS})\cdot I_{yy}(\text{RSS})-I_{yy}(\text{RSS})}\\ =\frac{1}{\rho_{1}}\cdot\frac{\sum\limits_{k=1}^{n}\left[\frac{\cos\alpha_{ik}} {d_{ik}}\right]^{2}+\sum\limits_{k=1}^{m}\left[\frac{\sin\alpha_{ik}}{d_{ik}} \right]^{2}}{\sum\limits_{k=1}^{m}\left[\frac{\sin\alpha_{ik}}{d_{ik}}\right]^{ 2}}\times\sum\limits_{k=1}^{m}\left[\frac{\cos\alpha_{ik}}{d_{ik}}\right]^{2} -\left[\sum\limits_{k=1}^{m}\frac{\sin\alpha_{ik}\cos\alpha_{ik}}{d_{ik}^{2}} \right]^{2}}\end{array} \tag{13}\]
## 3 Cylindrical Cavity Expansion in the SCC Soil
### Determination of the Radial Position and Excess Pore Pressure  
For the completeness of the present solution, the relationship between \(\xi\) and \(r\) needs to be established. For the undrained and plane-strain conditions, the incompressibility of the soil skeleton and pore fluid renders the current position of any soil element \(r\) as related to its initial position \(r_{0}\), its initial cavity radius \(a_{0}\), and its current cavity radius \(a\), which can be formulated as  
\[a^{2}-a_{0}^{2}=r^{2}-r_{0}^{2} \tag{45}\]  
Combining Equation (45) with \(\xi=(r-r_{0})/r\), the relationship between \(\xi\) and \(r\) can be obtained as:  
\[\frac{r}{a}=\sqrt{\frac{\left(a_{0}/a\right)^{2}-1}{\left(1-\xi\right)^{2}-1}} \tag{46}\]  
Figure 4: Elastic stress trajectory and over-consolidation ratio of structured soils.  
In particular, the position of the elastoplastic boundary \(r_{p}\) can be obtained by setting \(\xi\) in Equation (46) as \(\xi_{p}\), and then the explicit relationship between \(r_{p}\) and the cavity radii (\(a\) and \(a_{0}\)) can be written as  
\[\frac{r_{p}}{a}=\sqrt{\frac{\left(a_{0}/a\right)^{2}-1}{\left(\frac{\sigma_{i}^{ \prime}(\xi_{p})-\sigma_{h}^{\prime}}{2C_{0}}\right)^{2}-\frac{\sigma_{i}^{ \prime}(\xi_{p})-\sigma_{h}^{\prime}}{C_{0}}}} \tag{47}\]  
Once the effective stress variations are determined, the pore pressure \(u\) at any radial position \(r_{x}\) can be calculated from the equilibrium equation. By integrating Equation (10) from \(r_{p}\) to \(r_{x}\), \(u\) is expressed as  
\[u(r_{x})=u_{0}+\sigma_{rp}^{\prime}-\sigma_{r_{x}}^{\prime}-\int_{r_{p}}^{r_{x }}\frac{\sigma_{r}^{\prime}-\sigma_{p}^{\prime}}{r}dr \tag{48}\]  
Therefore, the excess pore pressure \(\Delta u(r_{x})\) generated around the cavity can be easily obtained as \(u(r_{x})-u_{0}\).
## 4 Results
### Comparison of the Experimental and Design Performance Curves  
The main differences between the experimental and design conditions are as follows:  
1. The experimental inlet condition, located in the supercritical-liquid-like region rather than the critical line.
2. The tip clearance of the experimental impeller is 0.43-0.45 mm, which is larger than the design value (0.25 mm) caused by the abrasion of the metallic coating at shroud, as is shown in Table 8.  
In order to explore the influence of tip clearance and inlet condition on the performance, Figures 15 and 16 give the comparison of the experimental and design curves of the single-stage centrifugal compressor. Obviously, due to the larger tip clearance, the leakage loss of inner clearance of impeller increases, and the isentropic head coefficient and isentropic efficiency of the experimental results are significantly reduced compared with the design value [17; 18; 19]. Specifically, the isentropic head coefficient of the experimental curve is 0.2-0.25 lower than the design curve as a whole; the isentropic efficiency is 0.15-0.2 lower than the design efficiency in the blockage and stall boundary zone, and 0.05-0.01 lower near the design point.