esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
###### Abstract  
Due to the advantages of a high total pressure coefficient, large flow coefficient, and high efficiency, the diagonal flow fan is widely used in people's livelihood and industrial fields. However, the design of the diagonal flow fan is mostly empirical, multi-solution, and comprehensive. The traditional optimization design process often consumes huge computing resources. In this paper, the diagonal flow fan blade is parameterized, the design variables are determined, and the accuracy of the parameterization method is verified. The maximum fitting error is controlled at approximately 0.1%. Based on the parametric design of blades, this paper organically integrates the traditional Kriging model and RBF model, and introduces the Ensemble of surrogates model (ES) to verify that the ES model has higher prediction accuracy in the prediction of fan flow and total pressure efficiency than the traditional prediction model. Subsequently, the Pareto optimal solution set of the approximate model within the global design scope is searched by NSGA-II. The numerical simulation and experimental verification show that the actual flow of the fan increases by 10% and the efficiency of the full pressure increases by 3.2% under the design condition of the optimized blade. The optimized model can significantly improve its air performance.  
diagonal flow fan; parametric design; ensemble of surrogates model; experimental verification
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 3 Results
### High-Resolution Parcels  
#### 3.2.1 Results of Threshold Selection for Downward Segmentation Features  
In this study, histogram features and internal edge features are utilized to determine whether a parcel should undergo further subdivision. P reflects the ability to correctly identify positive samples. Therefore, this metric was employed to assess the effectiveness of the thresholds. As shown in Figure 8, the \(p\) values of correctly classified parcels for further subdivision were calculated under different thresholds for both feature types. It was observed that, for histogram width, P increased with width expansion, stabilizing when the width approaches 70. Similarly, for internal edge features, P stabilized when the edge ratio reached 1.5%. Consequently, a histogram width of 70 and edge ratio of 1.5% were selected as thresholds for subsequent evaluations.  
Additionally, the results of an ablation experiment for these two feature types are presented in Table 3. It is evident that when only one feature type was used, although a high P was achieved, R and F1 were moderate. This indicates that a certain number of positive samples were misclassified as negative, leading to omissions. When both feature types were applied, P remained relatively unchanged, but R and F1 experienced significant improvements, demonstrating that the two feature types complement each other, enhancing the robustness and stability of decision-making.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 5 Pseudo Code  
### At Transmission: Embedding  
**Input:** patient information as text data and 1D-ECG signal.  
**Output:** watermarked ECG image.  
The pseudo-code for embedding patient information into the curvelet domain using clustering is given below:  
1. Read patient information, convert this text information into image. To process, the patient information or to make the watermark more imperceptible this patient information image, i.e., in RGB format converts it into a 2-D grayscale image;
2. Read ECG signal as the original signal and detect the QRS wave in this ECG signal to resist the diagnose detection;For detection of QRS wave Pan-Tompkins function was used. Here the parameters of this Pan-Tompkins function are ECG signal, frequency and number; a [qrs_amp_raw, qrs_i_raw,delay,final] = pan_tompkin(Orig_Sig,128,1)
4. To process the ECG signal in time domain first, convert this 1D-ECG signal into a 2D-ECG image;
5. Second, to adapt the environment of frequency domain this 2D-ECG image is converted into the transformed domain by using curvelet transform;
6. After this from this frequency domain, the embedding domain is selected. As discussed coarse-level coefficients maintain most of the energy of image to give more invisibility. The coarse-level coefficients were designated as the embedding domain;
7. Now categorized the selected embedding domain into different clusters as discussed in Section 3.4 above; 1. Find the dissimilarity matrix; 2. Calculate clustering; 3. Making the clusters by obtaining the values of centers and the radius of the cluster;
8. Then from these clusters set, select the cluster to hide the watermark. Let, it be group R\({}_{i}\) to R\({}_{j}\). Change name the particular cluster G\({}_{1}\), G\({}_{2}\)... G\({}_{k}\), where k = 2(I - j) + 2. For each cluster G\({}_{i}\), the radii of the groups are \(\rm{radi}_{i1}=c_{i}-max(G_{i})\), \(\rm{radi}_{i2}=c_{i}-min(G_{i})\) as discussed in Section 3.5 above;
9. Now, using Equations (5)-(8), to calculate the values of I\({}_{0}\), I\({}_{1}\), L\({}_{0}\) and L\({}_{1}\) set;
10. For each coefficient, if the coefficient belongs to the selected cluster G\({}_{i}\) the watermark image bit is inserted by modifying the coefficient value by using Equation (9). Otherwise find the group number of subsequent coefficients of curvelet;
11. With these updated coefficients values the ECG image is converted into the time-domain by applying the inverse curvelet transform. Then change the 2D-ECG image into 1D-ECG signal. The resulted signal is a watermarked signal.
## 2 Literature Review
When a source node is freely allocated, each CH is considered to be anchored at the centroid of a cube through strings. In ANCRP, the source node is in charge of relaying sensed data to their chosen CHs. The primary issues of this research are as follows. Recently, energy efficiency has emerged as a critical concern in wireless sensor networks. Sensor networks are powered by batteries and, as a result, die after a specific amount of time [21]. As a result, optimizing data dissipation in an energy-efficient manner becomes a more difficult task in order to increase the lifespan of sensor devices. Clustering and tree-based data aggregation for sensor networks can improve wireless sensor network lifespan. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability [22]. An energy-efficient clustering and tree-based routing protocol based on hybrid Ant colony optimization (ACO) and particle swarm optimization (PSO) is suggested ([PERSON] et al., 2018). To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, a multi-hop routing technique using grasshopper optimization (MHR-GOA) is derived with multiple input parameters [23].
## 4 Results
### Analysis Results
#### 4.3.2 Analysis of the Vrste and Scale Results for Rail Transit Station Areas  
According to the distribution statistics of the Scale and Vrste values of the seventeen station areas (Figure 10), the specific development of the input-output of each station area can be summarized as follows.  
* Scale and Vrste \(\geq\) 1 in interval I  
Figure 10 shows that Xingzhengzhongxin, Daminggongxi, Nanshaomen and Tiyuchang are at the forefront of efficiency. The Crste, Scale, and Vrste values of these five sites are all greater than or equal to 1. This finding indicates that during the development of these five station areas, the allocation and utilization of input resources by the government or operators exhibited efficient utilization and output.
* Scale \(\geq\) 1 and Vrste < 1 in interval II  
In interval II, the technical efficiency of the eight stations is high, with pure technical efficiency performing well, but their scale efficiency is suboptimal. This indicates a mismatch between scale efficiency and input, leading to diminishing returns to scale. Therefore, it is necessary to enhance the coordination of output and adjust the clustering scale and distribution. By optimizing the spatial layout and adding public facilities, the efficiency of the station areas can be improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
Figure 9: Efficiency gradient map.  
Scale and Vrste < 1 in interval III  
Regarding Kangfulu, Wulukou, Weiyijie, Banpo, and Kaiyuanmen in interval III, their Scale and technical efficiency values are both less than 1, while their comprehensive efficiency falls within the range of 0.6 to 0.8. Compared with the stations in intervals I and II, the stations in interval III exhibit relatively lower efficacy in terms of resource allocation and complete facilities. Thus, to improve station area efficiency, transportation facilities and public facilities should be optimized, and the layout of spatial divisions should be improved.  
Scale < 1 and Vrste \(\geq 1\) in interval IV  
Figure 10 shows that none of the 17 selected research objects meet these criteria.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology  
### Introduction to PIM  
The PIM, based on the stochastic medium theory, regards the mining-induced surface movement as a random event and uses the probability integral (or its derivative) to express the prediction formula (influence function) of the surface movement and deformation caused by the mining of small mining units, so as to calculate the whole movement and  
Figure 1: Distribution of mines in Huainan mining area.  
deformation by the superposition principle. It was proposed by [PERSON] et al. [33] and has been extensively used in China. The qualitative random model of granular medium is shown in Figure 2. The prediction formula of unit basin movement and deformation in unit mining could be obtained through the random medium theory, and the prediction formula of deformation at any point on the surface could be further deduced. There are five parameters in the PIM parameter model, namely subsidence coefficient \(q\), horizontal movement coefficient \(b\), tangent of major influence angle \(\tan\beta\), offset of inflection point \(s\) and propagation angle of extraction \(\theta_{0}\).
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 5 Simulation Analysis
### Simulation Results  
#### 5.2.1 Comparison between Existing Methods and Proposed Method  
Figure 8 compares the MSE performance of the proposed channel estimation method with the two lengths of the CIR scenarios. Figure 8a,b show the length of the CIR at \(L=1\) and \(L=6\), respectively. The performance of the proposed method is compared to the LS, MMSE, and existing ML methods, and all methods improve their performance as the SNR increases. In both scenarios, the LS methods do not take advantage of the statistical information of the channel, resulting in the worst MSE performance. On the other hand, the performance of the MMSE method is better than the LS method in both scenarios, because the MMSE method uses the statistical information of the channel. The existing ML methods and the proposed methods performed better than the LS methods in both scenarios. However, the performance of the existing ML method is better than the MMSE method below about 7 dB in the \(L=1\) scenario, but it reverses above 7 dB. And it is worse than the MMSE method at all the SNRs in the \(L=6\) scenario. The performance of the proposed method without performed weight quantization is better than the MMSE method at all the SNRs in the \(L=1\) scenario but is worse than the MMSE method at all the SNRs in the \(L=6\) scenario. Both the existing and proposed MLs suffer performance degradation depending on the length of the CIR, which is estimated to reduce ML's computational accuracy as the received signal becomes complex due to the convolutional multiplication of the transmitted signal. Furthermore, the performance degradation is expected to be due to the fact that the received signal information does not increase proportionally with the length of the CIR, but rather the longer CIR must be inferred from the same amount of received signal information, leading to more challenging results.  
Figure 8: Performance comparison between existing methods and the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layers**} & \multicolumn{2}{c}{**1 Tap DNN**} & \multicolumn{2}{c}{**6 Tap DNN**} \\  & **Nodes** & \(f(\cdot)\) & **Nodes** & \(f(\cdot)\) \\ \hline Input layer & 96 & - & 96 & - \\ Hidden layer & 32 & tanh & 32 & tanh \\ Output layer & 2 & - & 12 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The structures of each layer for the proposed ML.  
Meanwhile, it outperforms the existing ML method in both scenarios like the proposed method without the quantization. The quantized ML methods have no differences in performance from the proposed ML methods without performing weight quantization below the SNR of 10 dB in both scenarios. It is also observed that there are slight performance differences in the SNR regime which is greater than or equal to 10 dB, but it remained better than the existing ML methods.
###### Abstract  
Error measurement of a rotary axis is the key to error compensation and to improving motion accuracy. However, only a few instruments can measure all the motion errors of a rotary axis. In this paper, a device based on laser collimation and laser interferometry was introduced for simultaneous measurement of all six degrees-of-freedom motion errors of a rotary axis. Synchronous rotation of the target and reference rotary axes was achieved by developing a proportional-integral-derivative algorithm. An error model for the measuring device was established using a homogeneous transformation matrix. The influences of installation errors, manufacturing errors, and error crosstalk were studied in detail, and compensation methods for them were proposed. After compensation, the repeatability of axial and radial motion errors was significantly improved. The repeatability values of angular positioning error and of tilt motion error around the \(y\) axis and \(x\) axis were 28.0\", 2.8\", and 3.9\". The repeatability values of translational motion errors were less than 2.8 \(\upmu\)m. The comparison experiments show that the comparison errors of angular positioning error and tilt motion error around the \(y\) axis were 2.3\" and 2.9\", respectively. These results demonstrate the effectiveness of our method and the error compensation model.  
simultaneous measurement; six degrees-of-freedom errors; rotary axis; error model +
Footnote †: journal: Applied Physics Letters
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 4 Improved Detections
### NFT Causality Property  
The causality property of the NFT refers to the existence of a causality relation between the kernel of the Gelfand-Levitan-Marchenko equation, which is related to the nonlinear spectrum of the signal, and the corresponding time-domain signal that is obtained from the solution of this equation [13]. This property implies that if (i) the NIS modulation Equation (1) is used, (ii) full channel precompensation is employed, and (iii) the QAM signal \(s(t)\) is obtained from a pulse shape with a duration smaller than the symbol time \(T_{s}\), then the optical signal received before a certain time instant \(t_{k}\) depends only on the first \(k\) symbols \(x_{1},\ldots,x_{k}\), and not on the following ones.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup
### ML-Based Models' Stability  
In this work, the k-folds cross-validation technique plays a significant role in assessing the machine learning model stability. The technique first divides the measured data into \(k=k_{1},k_{2},\ldots,k_{7}\) subsets, named folds. Then it trains the model on the data using \(k_{1}\) to \(k_{6}\) of the folds and evaluateing the model's performance on the \(k_{7}\) data. The technique continues repeating the same approach by training the model based on six-folds and evaluates the model's performance according to the seventh (testing) fold. In each cross-validation training, the error estimation is averaged over all k trials to get the total effectiveness of the model. As can be seen, every data point gets to be in a validation set exactly once and in a training set \(k-1\) times. This significantly reduces the bias as the model uses most of the data for fitting and reduces variance as most of the measured data are also being used in the validation set. The use of cross-validation adds a reliable stability to the effectiveness of the machine learning model since the interchange of the measured data is applied between the training and testing sets.
## 4 Results Analysis
### Urban Test Case
Looking at Table 5, this is underlined for code ranges on all L1 frequencies. The number of recorded code observations is always higher compared to the geodetic-grade receiver. Investigating the carrier phase observations on the same frequencies, the numbers reveal an opposite behaviour in most cases. Figure 9: Modified Allan deviations of the L1 carrier phase DDs for one satellite pair of each GNSS system with two different receiver combinations in (**a**). For GLIC, RL1C and EL1X, DDs of PRN 29–26, PRN 15–24 and PRN 1–13 are used, respectively. The elevation angles of these satellites are shown in (**b**). Depending on the GNSS system analysed, the UBX receivers are recording 17% to 26% less carrier phase than code observations while the JVD receivers are recording the exact same number of code and phase observations. The computed DDs are in the C/N\({}_{0}\) range of 25 to 54 dB-Hz, which leads to  
Figure 11: DDs of L1 carrier phase observations from all satellites for different receiver combinations with respect to the C/N\({}_{0}\) values of the non-reference satellites. GPS, GLONASS and Galileo noise levels are shown in blue, green and red colours, respectively. Figure 10: DDs of L1 carrier phase observations from all satellites for different receiver combinations with respect to the elevation angle of the non-reference satellites. GPS, GLONASS and Galileo noise levels are shown in blue, green and red colours, respectively. the assumption that the analysed URX receivers are not capable of continuously tracking carrier phase observations in challenging situations and low C/N\({}_{0}\) scenarios. Figure 12 depicts the cumulative distribution functions (CDFs) for carrier phase DDs on different frequencies. Investigating the JVD DDs, 95% of the values for GPS, GLONASS and Galileo are below 2, 2.8 and 1.7 mm, respectively. For the UBX DDs, 95% of the values for GPS, GLONASS and Galileo are below 4.8, 5.6 and 4.7 mm, respectively. To get an idea of the quality of the carrier phases on the other frequencies, the CDFs of L2 frequencies and L5 frequencies are shown in Figure 12b,c. This is only feasible to analyse with the JVD combination, since the UBX receivers were tracking only one frequency. Compared to the L1 results, the quality of L2 and L5 DDs are relatively poor. For GPS and GLONASS L2 DD, 95% of the values are less than about 3.6 and 5.3 mm, respectively. For GPS and Galileo L5 DD, 95% of the values are below 4.8 and 3.8 mm, respectively. These results underline that the quality of GPS and Galileo carrier phase observations are similar, whereas the quality of GLONASS carrier phase observations is worse in comparison.
## 7 Conclusions and Future Work  
We presented \(r4 dtn\), an opportunistic networks routing algorithm based on Q-learning, that takes advantage of a particular opportunistic network model. This model captures the  
Figure 8: Total data transmitted in the network.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **BSW** & **PRoPHET** & **Q-Learn,r14 dtn** \\ \hline \(E\) & Number of encounters & & From ONM graph (copy edges) \\ \hline \(T\) & Number of message forwardings & & From simulation \\ \hline \(S_{M}\) & Size of message & 1 KB + 4 B & 1 KB & 1 KB + 8 B + 4 B \\ \hline \(S_{R}\) & Routing exchange size & 0 & 1.000*8B & 0 \\ \hline \end{tabular}
\end{table}
Table 3: Data exchange parameters.  
evolution of the network through time using a temporal graph. The network devices build and use this model in a distributed fashion during the network's lifetime.  
This work shows that this model allows our Q-learning-based method to outperform classic Q-learning approaches. By comparing r14 dtn with a conventional Q-learning algorithm, we show that the r14 dtn's advantage comes from the fact that the learning mechanism feeds from temporal data. These temporal data allow learning from the temporal patterns in the distribution of encounters arising from the devices' movement patterns. Temporal patterns are a defining characteristic of ONs, differentiating them from conventional networks.  
At the same time, the resulting algorithm obtains a higher delivery rate with a considerably lower network overhead than representative ON routing algorithms; this is very important for networks with large numbers of devices where the routing data transmissions can represent a significant load.  
Amongst the issues that need more attention, we point out that although the dataset used in this work is typical, more tests are needed as ONs cover a broad spectrum of mobility scenarios.  
Another area of improvement is the management of time slots. In this work, the time-slot size is fixed and experimentally determined. A scheme can be devised where the system adjusts the slots dynamically and automatically without an explicit configuration. For example, the system could adjust the time-slot size to maintain a certain number of encounters per slot, enough to sustain effective Q-learning. As a result, for example, nighttime slots could be longer and peak-hour slots shorter.  
In summary, the proposed routing algorithm builds on top of a previously presented network model, which allows it to outperform classic Q-learning approaches. It achieves higher delivery rates with lower network overhead than representative ON routing algorithms. There are several lines for improvement that must be explored.  
Investigation, J.V. and J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.