esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 1 Introduction
The proposed method bring Richardson-Lucy (RL) deconvolution with SATV regularization into the ordered subset expectation maximization (OSEM) iteration. Since the OSEM is a fast iterative algorithm, which has the advantages of good spatial resolution, strong anti-noise ability, and fast reconstruction speed, it is widely used in CT image reconstruction. Thus, this paper chooses this algorithm for image reconstruction. In the iteration process, image reconstruction and motion artifact reduction are completed. The iterations are stopped when the difference measure between two successive iterations is smaller than a threshold. The proposed method does not have to estimate the motion and is not limited by scanning mode. The efficacy of the proposed method is demonstrated on a modified 3D Shepp-Logan phantom, a head phantom and patient scan. The simulation results show that the proposed method can reduce RMSE by about 30% more than the RL-OSEM method, and increase CC and MSSIM by about 15% and 20% more than the RL-OSEM method. The contribution of this paper is given as follows:  
1. Richardson-Lucy (RL) deconvolution with SATV regularization is brought into the ordered subset expectation maximization (OSEM) iteration. 2. With the proposed method, image reconstruction and motion artifact reduction are completed alternately in the iteration process. 3. The simulation results are given to verify that the proposed method can be applied to any scanning mode. The rest of this paper is organized as follows. Section 2 presents the related algorithm and the proposed motion artifact reduction method. In Section 3, the performance of the proposed method is verified. The results are discussed in Section 4. Finally, the conclusion is given in Section 5.
## 2 Utilization of GNSS
### Multi-Frequency GPS Algorithm
The first data set contains just barely enough block IIF satellites, but the low number of pseudo-range measurements results in the highest standard deviation for the receiver clock bias difference as well as the worst positioning performance. The L2C-L1 C/A combinations exhibit large differences in receiver clock bias when compared to the single-frequency L1 C/A measurements (up to \(-7.5\) m). Consequently, combining these two types of observations results in degraded positioning performance. The third data set is an exception and offers very good positioning performance when processing both L2C-L1 C/A combinations and single-frequency L1 C/A measurements together. This happens because while recording this data set, only very few older GPS satellites (block IIR and earlier) were received. Most of the visible satellites provided L2C signals, causing the solution to be close to the one in which only L2C-L1 C/A combinations were processed. While the difference in estimated receiver clock bias could not be computed reliably when only processing L5-L1 C/A combinations, the low values of mean and standard deviation as well as the good positioning performance when processing both L5-L1 C/A combinations and single-frequency L1 C/A measurements together indicates that there are no significant DCBs for these two types of observations. The poor positioning performance whenever both L2C-L1 C/A and L5-L1 C/A combinations are processed together suggests that substantial DCBs between these two types of observations exist. Subsequent DCB calibration (see Section 4) substantiates these hypotheses. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Processed Code** & **Mean and Std. of** & **Positioning** \\
**Observations** & **Difference (in m)** & **Performance (Ranked)** \\ \hline L1 C/A & 0/0/0 & 2/2/4 \\  & 0/0/0 & \\ \hline L2C(M + L)-L1 C/A & \(-6.5/-7.5/-6.9\) & 3/3/2 \\  & 1.5/ 1.8/ 2.4 & \\ \hline L2C(M + L)-L1 C/A & \(-1.5/\) 0.7/\(-4.3\) & 4/5/1 \\ \(>\) L1 C/A & 1.7/ 1.7/ 2.1 & \\ \hline L5(I + Q)-L1 C/A & 1.6/N/A/N/A & 7/N/A/N/A \\  & 11.9/N/A/N/A & \\ \hline L5(I + Q)-L1 C/A & \(-0.7/-0.5/-0.6\) & 1/1/2 \\ \(>\) L1 C/A & 0.7/ 0.5/ 0.6 & 1/1/2 \\ \hline L5(I + Q)-L1 C/A & 3.0/\(-8.4/-7.4\) & 6/6/6 \\ \(>\) L2C(M + L)-L1 C/A & 8.1/ 7.2/ 2.8 & \\ \hline L5(I + Q)-L1 C/A & 0.3/\(-0.6/-5.3\) & 5/4/5 \\ \(>\) L2C(M + L)-L1 C/A & 2.0/ 1.5/ 2.1 & 5/4/5 \\ \(>\) L1 C/A & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differences in Estimated Receiver Clock Bias (Compared to Single-Frequency L1 C/A Processing) and Positioning Performance for Three Sets of Kinematic Measurement Data. N/A Indicates it was not Possible to Compute a Solution due to a Lack of Available GPS Block IIF Satellites.
# Combination of Models to Generate the First PAR Maps for Spain
Agronomica, Alimentaria y de Biosistemas, Universidad Politecnica de Madrid, 28040 Madrid, Spain 23E. saturation water vapor pressure as input variables [17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Non-linear PAR models [27; 28; 29] have also been carried out in previous works. The variability of solar irradiance and the spatial and temporal variability of the PAR/GHI ratio have previously been addressed [17; 30; 31; 32]. Solar irradiance depends on cloudiness and the presence of aerosols [33]. According to [PERSON]'s law [34], the extinction of irradiance due to aerosols is higher for shorter wavelengths. Therefore, the ultraviolet and visible bands are the bands most affected by the presence of aerosols [35]. Consequently, PAR models are highly dependent on the climatic and atmospheric conditions in which they were developed. Therefore, PAR models only obtain accurate estimates in locations where the climate and atmospheric conditions are similar to those in which they were developed. The present work focuses in PAR modeling in Spain. Previous studies have addressed this topic [17; 18; 19; 36; 37]. The main climates present in mainland Spain according to the Koppen-Geiger classification [38; 39; 40; 41] are oceanic varieties in northern and northwestern Spain, Mediterranean varieties in central, eastern and southern areas, arid climates in the southeast, and mountainous climates in the main mountain ranges of the Iberian Peninsula. In a previous study [17], two PAR models for mainland Spain were developed using two different datasets; the first model was developed using the estimates provided by CM-SAF, while the second was developed using the data set supplied by MODIS. According to [17], the results of the model that used CM-SAF estimates for its development were better in the Mediterranean and arid areas. By contrast, the best results in the oceanic areas were obtained with the model developed from the MODIS estimates. In the present work, a new daily PAR model is presented that covers mainland Spain. This new model is developed using a linear combination of the estimates provided by the two PAR models elaborated in [17]. The model is validated at seven radiometric stations located on mainland Spain. Next, the estimates supplied by the new model are used to create the first monthly and annual PAR maps in mainland Spain. This study is structured as follows. First, the data set employed and the mathematical tools used to develop the PAR model are described. Next, the results of the validation of the model with data provided by seven stations located throughout the study territory are presented. The monthly and annual PAR maps of mainland Spain developed using the estimates provided by the new PAR model are then shown. The results are discussed subsequently and, finally, the most significant conclusions are discussed.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)
#### 3.1.4 FRANC3D  
FRANC3D is a specialized software package that incorporates extended finite element methods (XFEM) for analyzing and simulating crack growth behavior in three-dimensional structures. XFEM is a powerful technique that enhances the traditional finite element method by efficiently capturing complex crack paths and discontinuities without the need for extensive mesh refinement. When combined with FRANC3D, XFEM offers significant advantages for accurate and efficient crack growth simulation [113, 114, 115]. There is no widely available free version of FRANC3D. Users typically need a license to access its full capabilities.
## 4 Empirical Results and Analyses
### Results for Exits after Holding a Fixed Period Viewed by a Heatmap Matrix

Table 3: _Cont._However, a consistent fixed term does not exist; rather, we employ a 60-day exit strategy based on the maximum MA day and average duration day utilized in different BBTS. Consequently, a holding period of 60 days is chosen as our fixed period. The aggregate outcomes of the two heatmap matrices for Bitcoin spot and ETH spot are then presented in Tables 5 and 6. Following this, a comparison is made between the results presented in Tables 3-6. Following that, the highest AHR in red is seen in Table 5 (Table 6), with a return of 29.51% (31.73%), which is much lower than the 52.77% (417.53%) of the conventional design for trading Bitcoin spot (ETH spot) presented in Panel A (Panel B) of Table 2. Furthermore, the maximum AHR reported in Tables 5 and 6 is only 31.73%. Thus, when comparing the results of Tables 3 and 4 to those of Tables 5 and 6, the later results fall short of the former. Thus, using the BBTS (i.e., when the BBTS emits oversold (overbought) signals, investors are encouraged to first buy (and then sell) both spots) may be beneficial for trading such cryptocurrency spots, as satisfactory returns are shown in Tables 3 and 4. However, when investors buy cryptocurrency spots as a result of a BBTS oversold signal and subsequently sell them at a fixed time (e.g., 60 days), they may not obtain the desired outcomes, as presented in Tables 5 and 6. Furthermore, even when the set holding periods are extended to 90 and 120 days (because there are many tables in this study, we then explain the results rather than adding more tables to save space) as exits, the results are still inferior to those shown in Tables 3 and 4. This implies that selling cryptocurrency spots as selling signals would yield larger returns than selecting a fixed
# Experimental Evaluation of a Takagi-Sugeno Fuzzy Controller for an EV3 Ballbot System +
###### Abstract  
In this paper, experimental results about the performance of a Takagi-Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3 BRS) are reported. The physical configuration of the EV3 BRS has the form of an inverted pendulum mounted on a ball. The EV3 BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi-Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3 BRS's TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3 BRS's body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3 BRS's body at its upright position. Measurement and control data from and to the EV3 BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation.  
ballbot; linear matrix inequalities; Parallel Distributed Compensation; Takagi-Sugeno fuzzy control; Takagi-Sugeno fuzzy model; telecontrol; telemetry; underactuated systems
## 6 Conclusions  
Dynamic Brazilian tests were carried out on two igneous rocks at different loading rates with the SHPB system. The incubation times of the two igneous rocks were fitted based on the experimental data. According to the images captured by the high-speed camera, the fragmentized characteristics of the typical results were analyzed. The present study yielded following conclusions:  
1. Based on the experiments with SHPB, the incubation times of the two igneous rocks were fitted. Their incubation times were 61.5 and 124.3 us, respectively. The variation of dynamic tensile strengths with stress rates was predicted by the incubation time criterion.  
2. The full-field strains on the observed surface of the Brazilian specimens preceding fracture were calculated by the DIC technique. The strain concentrations of the Brazilian specimens before they lost the stress equilibrium and at the main crack initiation were revealed through the DIC calculations. The temporal hysteresis of fracture relative to the stress level was discussed in combination with the concept of incubation time.  
3. Using image processing technology, the crack information of typical specimens of Breccia and Andesite in the fragmenting process was extracted. According to the binarization, the main crack opening velocities were estimated as 12.85 m/s for Breccia at 4.86 m/s striking velocity and 20.94 m/s for Andesite at 4.88 m/s striking velocity.  
4. Through FracPaQ, the pixel information of cracks was transformed into the coordinates of fracture traces. The rose diagram was proposed to show the relationship of the angle and the normalized length of fracture traces in fragmentized processes of Brazilian specimens.  
5. Length distributions in the angle of fracture traces were statistically analyzed. The results showed that the central cracking of the Brazilian specimens was described quantitatively by the Gaussian function.  
Conceptualization, L.-Y.C. and J.Y.; methodology, X.X.; software, Q.Y.; validation, L.-Y.C., J.Y. and Q.Y.; formal analysis, X.X.; investigation, X.X.; resources, J.Y. and Q.Y.; data curation, X.X. and L.-Y.C.; writing--original draft preparation, X.X.; writing--review and editing, X.X. and L.-Y.C.; visualization, X.X.; supervision, L.-Y.C. and Q.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant numbers 12002047 and 51774043.  
Not applicable.  
We are grateful to [PERSON] from the University of Oulu. His suggestions for this paper, including identification of the topic, the description of the incubation time criterion, the summary of the conclusion, and the enhancement of the language, are very helpful to us.  
The authors declare no conflict of interest.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 2 Individualized Feature Selection Using Mutual Information
### Analysis of Mutual Information in Interaural Cues  
Figure 1 illustrates the variations in MI that exist between the elevation angle of the source location and the spatial cues for different azimuths and SNRs. From Figure 1a,b, it can be observed that, in high SNR scenarios, the MI in the high-frequency range becomes dominant for elevation localization. However, with the decreasing SNRs, the high-frequency cues no longer provide reliable localization information, unlike the mid-frequency spatial cues. Further, the distribution (in frequency) of the most effective cues varies with different azimuths, and illustrates both the difficulty of decoupling the localization process into separate azimuth and elevation estimation problems, as well as the challenge of localization in the median plane [24].  
Further, comparing the behaviour of the two types of spatial cues, we can observe that the importance of each changes with the SNR. For example, where no or low noise is present, the interaural magnitude cues become dominant, while, in a comparably higher noise environment, the interaural phase cues show more robustness. Collectively, these observations imply that the selection of spatial cues for the creation of a feature vector for localization must be more nuanced than the simple selection of a fixed frequency range; thus, an adaptive noise-dependent feature selection and extraction process becomes a necessity for any noise-robust binaural localization system. A spatial feature learning algorithm that is aware of the MI contained in each spatial cue can satisfy this requirement, and provides superior performance to the former approach, as illustrated in the following sections.
## 3 System Model
### Communication Link Model  
According to Reference [33], the main models of satellite communication channel are the [PERSON]. [PERSON] model, Corazza model, and Lutz model. The [PERSON] model is mainly suitable for rural environments. The received signals are mainly composed of direct shadowing signal components and multi-path signal components which are not shadowed. The Corazza model is applicable to all environments (roads, villages, cities, etc.). The signals received by users are affected by shadows. The Lutz model divides the channel environment between satellite and user into good and bad states. In the good state, there is no shadowing effect. In the bad state, there is no direct signal component. The above three models are represented as model X, model Y, and model Z, respectively. Three main propagation models of satellite communication links are shown in Figure 4.  
Figure 4: Satellite channel model diagram.  
transferred from one state to another state. Let \(J_{\overline{s41542}}(t)\) denote the probability of transition from state \(\overline{S41}\) to state \(\overline{S42}\); then, the transition probability matrix \(\Phi_{i}(t)\) is obtained as follows:  
\[\Phi_{i}(t)=\big{[}J_{\overline{s41542}}(t)\big{]}_{2\times 2}, \tag{20}\]  
where \(J_{\overline{s41542}}(t)=\Pr\big{(}\varsigma_{i}(t+1)=\overline{S42}|\varsigma _{i}(t)=\overline{S41}\big{)}\), \(\overline{S41}\), \(\overline{S42}\in\varsigma_{i}\)[35].
## 3 Results
### Ejection Pressure (P\({}_{\mathrm{ej}}\))  
Through maintaining the ejection temperature at 1588 \({}^{\circ}\)C (the optimal temperature at which the most spherical particles were obtained), the atomization pressure at 15 bar, and the diameter of the ejection nozzle at 200 \(\upmu\)m, atomized powders were prepared for different ejection pressures between 1 and 3 bar. Figure 3 shows SEM images of the powders obtained via gas atomization at different ejection pressures whilst the rest of the atomization parameters were constant.  
Figure 3: SEM images of gas-atomized powders ejected at different ejection pressures. p\({}_{\mathrm{ej}}\) = 1 bar (**a**), p\({}_{\mathrm{ej}}\) = 2 bar (**b**), and p\({}_{\mathrm{ej}}\) = 3 bar (**c**).  
Figure 2: SEM images of gas-atomized powders ejected at different ejection temperatures. T = 1548 \({}^{\circ}\)C (**a**), T = 1568 \({}^{\circ}\)C (**b**), and T = 1588 \({}^{\circ}\)C (**c**).  
An analysis of the SEM images revealed that the size of the powders increases with the ejection pressure. Thus, for p\({}_{\rm{ej}}\) = 1 bar, the average particle diameter is 117 \(\upmu\)m, and for p\({}_{\rm{ej}}\) = 2 bar, the average particle diameter is 163 \(\upmu\)m, while for p\({}_{\rm{ej}}\) = 3 bar, the average particle diameter increases to approximately 192 \(\upmu\)m. These results can be explained by the link between the mass flow rate of the melt and the median diameter of the particles. [PERSON] [40] established the correlation between the average diameter (d\({}_{\rm{m}}\)) of the gas-atomized powders and the atomization parameters using the following equation:  
\[d_{m}=DK[\frac{v_{m}}{v_{g}}\frac{1}{We}(1+\frac{m_{m}}{m_{g}})]^{1/2} \tag{1}\]  
where \(We=\frac{(\Delta U)^{2}\rho_{m}D}{\sigma}\) is the Weber number and represents the ratio of the product of the square of the relative velocity between the melt and the gas (\(\Delta U\)), the density of the metal melt (\(\rho_{m}\)), the diameter of the ejection nozzle (D), and the surface tension of the melt (\(\sigma\)). K is a constant specific to each atomization system, \(v_{m}\) and \(v_{g}\) are the kinematic viscosities of the melt and the gas, respectively, and \(m_{m}/m_{g}\) is the ratio between the mass flow rate of the melt and the gas flow rate. The increase in ejection pressure is reflected in the increase in the mass flow rate of the melt. According to Equation (1), the increase in mass flow leads to an increase in the diameter of the particles. Therefore, to obtain small-sized particles, the mass flow rate should be kept low.
## 3 Multiplier-Free Modified Godard Timing Recovery
### Multiplier-Free Modified Godard Algorithm  
Reducing the number of required multiplications typically leads to a reduced computational complexity. So far, Equation (5) has \(\beta N/\eta\) complex multiplications per timing error estimate. Now, we substitute these multiplications with an approximation. For this, we adapt to Equation (6) were we perform the calculation in polar coordinates.  
\[\hat{\tau}_{\epsilon}=\sum_{k=\frac{1-\beta}{2\eta}N}^{\frac{1+\beta}{2\eta}N- 1}\text{Im}\Big{\{}|\widetilde{r}_{k}|\Big{|}\widetilde{r}_{k+(1-1/\eta)N} \Big{|}\cdot\exp\Big{(}\text{j}\Big{(}\arg(\widetilde{r}_{k})-\arg\Big{(} \widetilde{r}_{k+(1-1/\eta)N}\Big{)}\Big{)}\Big{)}\Big{\}} \tag{6}\]  
Calculating the imaginary part results in:  
\[\hat{\tau}_{\epsilon}=\sum_{k=\frac{1-\beta}{2\eta}N}^{\frac{1+\beta}{2\eta}N -1}|\widetilde{r}_{k}|\Big{|}\widetilde{r}_{k+(1-1/\eta)N}\Big{|}\cdot\sin \Big{(}\arg(\widetilde{r}_{k})-\arg\Big{(}\widetilde{r}_{k+(1-1/\eta)N}\Big{)} \Big{)}. \tag{7}\]  
In a hardware implementation, the multiplier-free CORDIC algorithm performs the transformation from Cartesian to polar coordinates [17].  
Our investigations revealed that neglecting the magnitude of Equation (7) does not significantly decrease the performance of the modified Godard algorithm, but simplifies the calculation. Therefore, we can calculate the timing estimate as described in Equation (8).  
\[\hat{\tau}_{\epsilon}=\sum_{k=\frac{1-\beta}{2\eta}N}^{\frac{1+\beta}{2\eta}N -1}\sin\Big{(}\arg(\widetilde{r}_{k})-\arg\Big{(}\widetilde{r}_{k+(1-1/\eta)N }\Big{)}\Big{)}. \tag{8}\]  
The influence of neglecting the amplitude can be seen in Figure 6. Neglecting the amplitude results in a severe performance decrease for the original Godard (see red dashed curve). The reason for this is that the elements with zero magnitude, which we neglected for the modified Godard algorithm, now have the same influence as the elements that contain the phase information. This clearly decreases performance. Since, for the modified Godard, we only consider elements that are of interest, the amplitude neglect does not have a big influence.  
For the feedback loop, we are only interested in the zero crossing of the estimate. For this reason, the first Taylor expansion of the sine function can be used (see Equation (9)). This adaption eliminates the last computationally heavy calculation, and we are only left with additions.  
\[\hat{\tau}_{\epsilon}=\sum_{\begin{subarray}{c}k=\frac{1-\beta}{2\eta}N-1\\ 2\eta N\end{subarray}}^{\frac{1+\beta}{2\eta}N-1}\arg(\widetilde{\tau}_{k})- \arg(\widetilde{\tau}_{k+(1-1/\eta)N}) \tag{9}\]