esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### A Model for the Uncertainty Analysis of the Measurements
#### 3.3.2 Uncertainty Analysis for LTM Measurement Setup
The term \(\delta_{\alpha_{t}}\) is composed by two terms, the first \(\delta_{\alpha_{p}}\) is the angle measurement uncertainty due to the OPS and LTM system, associated to the variability of the parameters related to the manual measurement of the lengths \(\Delta x_{a}\), \(\Delta x_{b}\),\(\Delta y_{a}\), and \(\Delta y_{b}\), carried out by the operator; while the other \(\delta_{\alpha_{s}}\), is the angle measurement uncertainty due to the template-matching algorithm of image processing software. This last contribution is evaluated [44] by means of a Monte Carlo Simulation (with 10,000 iterations) implemented in MATLAB(c); \(\delta_{\alpha_{s}}\) corresponds to \(\pm 0.02^{\circ}\), at 95% confidence level. \[\delta\alpha_{t}=\sqrt{\left(\frac{\delta\alpha_{p}}{\alpha}\cdot\alpha \right)^{2}+\left(\delta\alpha_{s}\right)^{2}} \tag{5}\]  
The angle measurement uncertainty \(\delta_{\alpha_{p}}\) has been measured by means of the triangular properties as shown in Figure 10. For the measurement of values \(\Delta x_{a}\), \(\Delta x_{b}\), \(\Delta y_{a}\) and \(\Delta y_{b}\), 10 tests were carried out for each of the segments and their mean value has been considered. The uncertainty related to the light microscope and profilometer error can be obtained as approximated in [44]:  
\[\frac{\delta\alpha_{p}}{\alpha}=\sqrt{\left(\frac{\delta a}{a}\right)^{2}+ \left(\frac{\delta b}{b}\right)^{2}}\, \tag{6}\]  
Figure 10: Angle measurement for the uncertainty evaluation related to the profilometer image, on the **left** and microscope image, on the **right**. where  
\[a =\sqrt{{\Delta{x_{a}}^{2}}+{\Delta{y_{a}}^{2}}} \tag{7}\] \[b =\sqrt{{\Delta{x_{b}}^{2}}+{\Delta{y_{b}}^{2}}}\]  
and  
\[\delta a =\sqrt{\left(\frac{\partial a}{\partial x}\delta x\right)^{2}+ \left(\frac{\partial a}{\partial y}\delta y\right)^{2}}=\sqrt{\left(\frac{ \Delta x_{a}}{\sqrt{\Delta x_{a}^{2}+\Delta y_{a}^{2}}}\delta x\right)^{2}+ \left(\frac{\Delta y_{a}}{\sqrt{\Delta x_{a}^{2}+\Delta y_{a}^{2}}}\delta y \right)^{2}} \tag{8}\] \[\delta_{B}=\delta_{\alpha}=\sqrt{\left(\frac{\partial\theta}{\partial V} \delta_{V}\right)^{2}+\left(\delta\alpha_{t}\right)^{2}} \tag{3}\]0  
The quantities \(a\) and \(b\) in (7) depend on the image resolution and size. For this study, the lengths of the comb-drive triangle in the profilometer image are considered for the maximum rotation (\(1.3^{\circ}\)), i.e., \(\Delta x_{a}=25\) pixel, \(\Delta y_{a}=150\) pixel, \(\Delta x_{b}=680\) pixel, and \(\Delta y_{b}=150\) pixel, therefore \(a=152\) pixel, and \(b=696\) pixel. Instead, the lengths of the comb-drive triangle in the microscope image are considered for the maximum rotation (\(0.93^{\circ}\)), i.e., \(\Delta x_{a}=130\) pixel, \(\Delta y_{a}=330\) pixel, \(\Delta x_{b}=1550\) pixel, and \(\Delta y_{b}=450\) pixel, therefore \(a=355\) pixel, and \(b=1614\) pixel.
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 5 Conclusions  
This work highlights the successful implementation of a P.I. controller in a peristaltic pump for hemodialysis, achieving significant advances in flow precision, stability, and reliability. Through the application of a polynomial model and non-linear least squares fitting, the relationship between RPM and volumetric flow rate was characterized with high accuracy, resulting in a minimal RMSE of 0.9193 mL/min. These findings demonstrate the importance of precise system modeling for achieving optimal control.  
Compared to a commercial hemodialysis machine, the developed system consistently achieved lower flow errors and a ratio index closer to the ideal value of 1, confirming its superior performance. Additionally, the statistical analysis conducted, particularly the paired \(t\)-test with a \(p\)-value of 0.021, provided robust validation of these findings, emphasizing the system's significant improvements in flow control under realistic conditions.  
The integration of synthetic blood and hemodialysis-specific components allowed for realistic testing conditions, ensuring that the findings were applicable to clinical scenarios. While the study achieved its primary objectives, limitations such as the inability to reach flow rates above 600 mL/min and the reliance on synthetic blood highlight areas for future exploration.  
In conclusion, this study represents a contribution to the development of precise and reliable control systems for peristaltic pumps in hemodialysis. The methodology and results, reinforced by statistical validation, provide a strong foundation for future innovations aimed at enhancing the safety and efficacy of these critical medical devices.  
Conceptualization, C.H.S.-S. and J.A.S.-C.; data curation, J.M.B.-F. and A.G.-H.; funding acquisition, J.A.S.-C. and N.A.R.-O.; investigation, C.H.S.-S.; methodology, C.H.S.-S., J.M.B.-F. and A.G.-H.; project administration, J.A.S.-C. and N.A.R.-O.; resources, J.A.S.-C.; software, C.H.S.-S. and J.M.B.-F.; supervision, J.A.S.-C.; validation, J.A.S.-C. and N.A.R.-O.; visualization, N.A.R.-O.; writing--original draft, C.H.S.-S., J.A.S.-C. and [PERSON]-F.; writing--review and editing, C.H.S.-S. and A.G.-H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by CONAHCYT project F003-322623 (LANITEM).  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank the _Laboratorio Nacional CONAHCYT de Investigacion y Tecnologias Medicas (LANITEM)_.  
The authors declare no conflicts of interest.
# Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation
## 5 Conclusions  
Machining error is an inevitable but non-negligible aspect in the design process of an RV reducer cycloidal gear. This paper quantitatively analyzes the influence of the machining error on the tooth profile of the cycloidal gear. Considering the randomness of machining errors, several computer simulations of machining errors are performed based on the Monte Carlo method. After simulation, mathematical statistics are derived based on the simulated sampling data. The variation in the cycloidal gear profile caused by the machining error is obtained. The variance of the statistical result is obtained from the statistics as 0.000145 mm\({}^{2}\), demonstrating the particular feasibility of the model. The tooth profile after machining error compensation is fitted by the combined modification method of equidistant and radial movement, and an optimization model of error compensation is established. At the same time, the carrying capacity optimization model is established, considering the carrying capacity of the RV reducer. The multi-objective optimization algorithm of NSGA-II is utilized to optimize the models, and the equidistant modification amount of \(\Delta r_{rp}=0.1311\) mm and the radial movement modification amount of \(\Delta r_{p}=0.1111\) mm can be obtained. These optimizations meet the existence conditions of the anti-bow tooth profile, and show good carrying capacity and error compensation effects.  
There are still some deficiencies in this paper, which will serve as the direction for future work: (1) The machining errors in this paper do not come from actual machine tools. Thus, the machining errors of the actual machine tools will be adopted in the future; (2) The modification parameters obtained by the proposed method have not been verified by experiments. The proposed method will be employed to process cycloidal gears and conduct experiments with RV reducers to evaluate the modification effect; (3) Errors caused by part fixture and part defects and the influence of dispersion will be considered in the future.  
Conceptualization, H.L.; methodology, H.L. and J.W.; theoretical analysis, H.L. and J.W.; validation, H.L. and J.W.; writing--original draft preparation, J.W.; writing--review and editing, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shanghai, grant number 20 ZR1401300, and the Interdisciplinary Program Project of Donghua University (202212).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Simulation And Analysis
### Experimental Measurement Metrics  
For evaluating the performance of the FPRDM routing algorithm comprehensively, the final result obtained from this experiment is the average value of the four testing results from the above mobility models, and we will analyze the experimental results in terms:  
(1) Delivery ratio: This measurement metric represents the probability of neighbor and destination nodes acquiring data packets, which is expressed as  
\[D_{node}=D_{receive}/D_{send} \tag{23}\]  
where \(D_{receive}\) represents the number of messages obtained from the neighbor nodes in the same online community, and \(D_{send}\) denotes the number of messages transmitted by mobile users. There may be data packets loss and packet damage phenomenon in the data dissemination process, so \(D_{send}>D_{receive}\) and \(D_{node}<1\).  
(2) Average end-to-end delay: This parameter is the average delay of routing selection, waiting for messages, node mobility, and message forwarding. It could be computed as  
\[A_{node}=A_{sum}/N \tag{24}\]  
in which \(A_{sum}\) denotes the total delay of data transmission from source nodes to destinations and \(N\) represents the total number of mobile users in the communication area.  
(3) Network overhead on average: This parameter denotes the average overhead of a successful message forwarding between a pair of mobile users. The network overhead on average is the network overhead of each mobile node in data transmission, including memory consumption, time delay between nodes, energy consumption, etc. Therefore this parameter can be expressed as the ratio of total network overhead to the number of nodes in the communication area, which could be formalized as  
\[O_{node}=O_{for}-O_{sec}/N \tag{25}\]  
where \(O_{for}\) is the total time of data transmission and \(O_{sec}\) represents the total time of successful message forwarding between mobile users, and \(N\) denotes the total number of mobile nodes.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 3
#### 3.3.3 Case 3: Analysis of 10,000-100,000 Frequency Range (Hz)  
The mid-frequency region (10,000-100,000 Hz) is primarily influenced by axial and radial winding deformations, insulation compression failure, and variations in inter-winding capacitance (Table 7). These defects arise when the transformer's windings shift under electrical or mechanical stress, leading to uneven capacitance distribution.  
In Figure 19a, a sharp downward trend is observed in the 40 kHz to 80 kHz range, with the FRA response falling significantly below the \(LCL\). This pattern is characteristic of axial or radial displacement in the winding structure, potentially due to loss of clamping force or dielectric compression failure.  
The \(C_{p}\) and \(C_{pk}\) process capability indices, displayed in Figure 19b, drop below 1.3, indicating a marginal stability scenario (Table 4). Additionally, the range chart shows a broadening of variance, confirming that the transformer is undergoing irregular dielectric compression.  
Remarks: It should be noted that the transformer shows clear signs of axial or radial winding displacement. Immediate diagnostic assessment is required to recalibrate winding positions and evaluate clamping pressures.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios
#### 6.1.6 Results of Simulation Scenario 3  
The third simulation scenario deals with data collection of MNM concept. The MANET layer, which is hierarchically located above the WSN layer, is responsible for data collection in case of disruption of the fixed infrastructure. When disruption of the fixed infrastructure occurs, the WSN sensors transfer urgent data to the nearest WSN gateway sensor, which tries to locate MANET nodes if AP is unavailable. If the MANET node is not close to the WSN gateway sensor, the gateway stores the urgent data in its cache and waits for contact with MANET node. Therefore, it is crucial to compare the times needed to collect data by MANET nodes from WSN nodes and WSN gateway sensors. Those results are depicted in Figure 24.  
Figure 23: The average number of hops from source WSN sensor to AP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**The Average Data Rates in 100 Kb Urgent Data Delivery [Mbps]**} \\ \cline{2-5}  & & **WSN** & **WSN-MANET-DRONET** \\ \hline
**AP Position** & **Average** & **Standard Deviation** & **Average** & **Standard Deviation** \\ \hline Position 1 & 0.0278 & 0.0034 & 44.51 & 16.26 \\ Position 2 & 0.0274 & 0.0032 & 66.89 & 12.24 \\ Position 3 & 0.0279 & 0.0024 & 87.29 & 11.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average data rates in 100 Kb urgent data delivery from source WSN sensor to AP.  
Since the number of WSN gateway sensors is lower than the number of WSN sensors, results show that time needed to cover the last WSN sensor by MANET nodes is higher compared to the time needed to cover the last WSN gateway sensor. It can be also observed that with an increasing number of nodes, the time required to cover all nodes is lower. With twice the number of nodes, the time is even more than twice as low. This is due to the fact that the highest percentage of WSN nodes and gateways are covered with the same radio range of MANET nodes during the initial node distribution. More importantly, the time required to collect data from each WSN gateway sensor is approximately 30% lower than the time required to collect data from each WSN sensor.  
A further reduction in the number of sensor gates would contribute to even less time, but a low number of gates would also increase energy costs and traffic at these nodes, as a \"sink mobility\" model is considered, where all communication converges to a single point. In the end, this result proves that the system of the gateway in the WSN network is useful for data collection in terms of time.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Simulation Setting
#### 5.1.1 Datasets  
Nine kinds of interference signals, which include single-tone jamming, multitone jamming, periodic Gaussian pulse jamming, frequency hopping jamming, linear sweeping frequency jamming, second sweeping frequency jamming, BPSK modulation jamming, noise frequency modulation jamming and QPSK modulation jamming, are generated by matlabR2020a. The details of the parameters of each interference type are shown in Table 1. The sampling frequency is 10 MHz, and the number of sampling points is 1024. The added noise is additive white Gaussian noise, and the JNR ranges from \(-\)10 dB to 18 dB, with an interval of 2 dB. The number of data samples of each type per JNR is 1500, including 500 training samples, 500 verification samples and 500 test samples.  
All datasets consist of the above nine interference signal types. Jamming-N is used to indicate that the dataset contains N known classes and 9-N unknown classes, which are randomly sampled and combined. During training, the input of the model is the known  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interference Types** & **Parameter Setting** \\ \hline Single-tone & The center frequency \(f_{c}\) is between [100, 500] kHz, and the phase \(\varphi\) is between [0, 2\(\pi\)]. \\ \hline Multitone & The number \(N\) of audio is [2; 10], and \(f_{c}\), \(\varphi\) are the same as single-tone jamming. \\ \hline Periodic Gaussian pulse & The pulse period \(T\) is 2.5–10 μs, and the duty cycle is 1/8–1/2. \\ \hline Frequency hopping & N = 20, \(\{f_{c}\}\) is between [100, 500] kHz, the frequency hopping period \(T_{h}\) is between 3.2–6.4 μs, and the phase \(\varphi\) is between [0, 2\(\pi\)]. \\ \hline Linear sweeping frequency & The starting frequency \(f_{c1}\) is [50, 100] kHz, and the ending frequency \(f_{c2}\) is [300, 1000] kHz. \\ \hline Second sweeping frequency & The frequency is quadratic, and other parameters are the same as linear sweeping frequency jamming. \\ \hline BPSK modulation & The information symbol is a 32-bits 0,1 random sequence, the symbol period is 3.2 μs, and the modulation signal is sinusoidal signal. \\ \hline Noise frequency & The frequency modulation coefficient is between [0.125, 0.933], and the carrier signal parameters are the same as single-tone jamming. \\ \hline QPSK modulation & The information symbol is a 32-bit 0,1 random sequence, the symbol period is 3.2 μs, I-channel modulation signal is sinusoidal signal, Q-channel modulation signal is cosine signal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interference signals and their parameter settings.  
class samples. During testing, the input of the model is both the known class samples and unknown class samples, and the model must assign appropriate labels to the test samples of the known classes and reject the test samples of the unknown classes as the N + 1 class.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 5 Results
### Convergence Speed  
The convergence speed determines the amount of symbols needed to obtain adequate performance of the equalizer, allowing the successful compensation of the signal impairments. Figure 7 shows the convergence speed of the FD implementation for both SSA and LMS. Figure 7a presents the \(2\times 2\)  
\begin{table}
\begin{tabular}{c c c c} \hline  & **64 Taps** & **512 Taps** & **1024 Taps** \\ \hline \(2\times 2\) FD-LMS & \(10^{-3.4}\) & \(10^{-4.3}\) & \(10^{-4.6}\) \\ \(2\times 2\) FD-SSA & \(10^{-4.6}\) & \(10^{-5.5}\) & \(10^{-5.7}\) \\ \(6\times 6\) FD-LMS & \(10^{-3.5}\) & \(10^{-4.5}\) & \(10^{-4.7}\) \\ \(6\times 6\) FD-SSA & \(10^{-4.6}\) & \(10^{-5.5}\) & \(10^{-5.8}\) \\ \hline \end{tabular}
\end{table}
Table 1: Step sizes used for the different equalizers considered.  
Figure 5: Step size optimization of different multiple-input multiple-output (MIMO) algorithms. (**a**) step-size dependence of achievable bit-error rate (BER) for FD 2 \(\times\) 2 least mean square (LMS) and Stokes Space Algorithm (SSA); and (**b**) for FD \(6\times 6\) LMS and SSA. Optical signal-to-noise ratios (OSNR) = 14 dB.  
Figure 6: BER performance of different MIMO algorithms. (**a**) BER vs. OSNR for FD \(2\times 2\) LMS and SSA and (**b**) for FD \(6\times 6\) LMS and SSA.  
case while Figure 7b shows the 6 \(\times\) 6. For both FD-LMS and FD-SSA the number of symbols required for equalization is increased when switching from 2 \(\times\) 2 to 6 \(\times\) 6 equalization. Furthermore, in both cases, SSA equalizer is more sensitive to the number of taps of the equalizer, i.e., its convergence speed reduces significantly with increasing the number of taps.  
Table 2 shows the required number of symbols to achieve convergence in each case.
# Long-Term Monitoring of the Behavior of Modified Bitunens of RC Type
## 2. Asphalt Mixtures with PmB and RAP  
The use of RAP in asphalt mixtures with PmB entails several specificities and limitations. The first and probably most important information is related to determining whether non-modified or modified bitumen is used in the RAP, or determining the extent of degradation of the binder modification system in the PmB-RAP [10; 11]. This involves the question of how the addition of RAP containing PmB or RAP, in general, will affect the final functional properties and behaviour of the asphalt mixture. Nevertheless, it must be considered that the binder in the RAP used may not be modified at all. If this fact is not considered in the mixture design, there is a risk of \"dilution\" of the resulting content of the modifying additive (usually a polymer) in the newly produced asphalt mixture [12].  
The binders contained in the RAP can be very heterogeneous. They can vary in terms of quality and composition. These differences and specificities cannot be detected by the conventional RAP tests described in CSN EN 13108-8 of 2008 [13]. This standard specifies, among other things, only the requirements for empirical tests of binders, i.e., the needle penetration test according to CSN EN 1426 [14] and the softening point test according to CSN EN 1427 [15]. However, the results of these two tests alone cannot even be used to simply determine whether the RAP itself is modified or not. Only the calculated value of the penetration index can provide some guidance, with modified binders often exceeding 0.7 units (a common maximum value for non-modified binders). If a verification of the use of the modified binder in the RAP were required, it would be necessary to apply, for example, the elastic recovery test according to CSN EN 13398 [16], the tensile properties test using the force ductility method according to CSN EN 13589 [17], the measurement on a dynamic shear rheometer according to CSN EN 14770 [18] with subsequent evaluation using, for example, a Black diagram, or the use of one of the modern analytical methods, including infrared spectrometry (FTIR) [19].  
Ideally, an RAP containing PmB should be used for the production of the asphalt mixture with PmB. During research [20; 21], it has been shown that even aged modified binder contained in PmB-RAP can contribute positively to the behaviour of the resulting asphalt mixture. On the other hand, when designing the binder for the asphalt mixture, it can be assumed that the RAP does not contain a modified binder at all and the residual effect of the degraded modifier would only be a kind of benefit [22].