esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Parallax Multiview Generation with High-Speed Wide-Angle Dual-Axis Scanning Optics
###### Abstract  
Three-dimensional displays are receiving considerable attention owing to their ability to deliver realistic content. Particularly, a multiview display with temporal multiplexing offers advantages in terms of fewer restrictions for optical alignment and flexibility in forming view density. However, most of studies realize horizontal parallax-only multiview display. In a horizontal parallax-only multiview display the content is distorted in the vertical direction as the observer changes the viewing distance. It is helpful to understand this phenomenon using the Wigner distribution function (WDF). In this study, we divided the viewing zone (VZ) into the sub-viewing zone and integrated viewing zone according to the number of views of the observer. Specifically, the changes in the contents are experimentally evaluated at different viewing distances to validate our expectation. For the experiment, we implemented a full-parallax multiview display with spherical symmetry and designed a high-speed wide-angle dual-axis scanner. This scanner comprises two single-axis scanners connected by high numerical-aperture scanning optics. The proposed system and WDF analysis of VZ will be helpful to evaluate the characteristics of the multiview system.  
multiview display; three-dimensional display; temporal multiplexing; dual-axis scanner +
Footnote †: journal: Applied Sciences  
its large scanning angle and a relatively large beam diameter. In the OXCT, two individual scanners are arranged sequentially. Therefore, OXCT scanners have a pincushion error resulting from the gap between the two rotation axes [21; 22].  
In this study, we propose a display in which a full-parallax image is formed inside a ball lens with spherical symmetry. The proposed display provides undistorted content regardless of change in the observer viewing distance within the VZ. We analyzed the observation characteristics within the VZ using WDF. The obtained results were consistent with the experimental results. This system was implemented by designing a high-speed wide-angle dual-axis scanner comprising two different single-axis scanners, namely a resonance scanner and galvanometer scanner. The former is driven at 240 Hz with a 60-degree maximum optical scanning angle and rotates an image around the horizontal axis, whereas the latter is driven at 12 Hz with an 80-degree maximum optical scanning angle and rotates an image around the vertical axis. The two scanners are connected by scanning optics with a high numerical aperture (NA) and their two rotation axes are effectively placed on the common plane.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 4 Simulations and Results
### Performance Evaluation of the MOTS Method for Channel Rating  
To check how the MOTS method works, the values \(D_{m}=1,T_{s}=0.01,P_{fa}=0.01,S=10\) have been applied in simulation. In the following, three different scenarios are defined for this purpose:  
\[\lambda^{(1)} =[3,6,9,15,20,25,40,50,80,100]\] \[\lambda^{(2)} =[100,80,50,40,25,20,15,9,6,3]\] \[\lambda^{(3)} =[15,15,15,15,15,15,15,15,15,15]\]  
In the first scenario, the PUs arrival rate vary from 3 APS (in sub-channel with channel number index (CNI) equal to 1 (CNI = 1)) to 100 APS (for CNI = 10). In Figure 11, an effectiveness evaluation of the proposed algorithm MOTS on channels contribution to data transfer in three different scenarios is presented as a histogram. In this figure, the horizontal chart contains a sub-channel number (CNI), and the vertical axis represents the number of times that a sub-channel was involved in sending data during the allowed time. As shown in Figure 11a, in the scenario (1), sub-channels 1 through 3 with 14, 12, and 6 repetitions were selected by the MOTS in the transmission of information, respectively. In this scenario, the algorithm intelligently prevents involving channels 4 through 10, which have high arrival rates. Consequently, these channels have no share in the data transmission.  
In the second scenario, the channels are reversed. In this new case, the MOTS has successfully managed to share just suitable channels during the transmission. Here, sub-channels 10, 9, 8, and 7 were involved in data transmission. sub-channel 9 as the most active channels with about 30 iterations carried almost 43% of transmission while sub-channels 10, 8, and 7 share 35%, 19%, and 3%, respectively.  
In the last scenario, all channels have the same arrival rate (\(\lambda_{i}=15,i=1,2,\ldots,10\)). In this experiment, the proposed algorithm has struggled rigidly to distribute data uniformly in a balanced way across all sub-channels. In Figure 11c, 80% of all available channels were deployed in data transmission. The third sub-channel, as the most active channel, was repeated four times while channels 2 and 10 were idle during the transmission. Comparing the results of the three different scenarios shows that the proposed algorithm had tried to only transmit the channels that have the best conditions.  
Figure 10: The comparison of time overhead expected value for \(NSS1=1,4\) and \(NSSI=22=X^{*}\) (the achieved value using proposed JOPSS model).
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Fracture Network Simulation
#### 2.4.3 Simulations with REPSIM Software  
The extracted microfracture parameters were used as the basis for modelling of 10 m \(\times\) 10 m \(\times\) 10 m blocks of varying combinations of VGN and PGR. The approach is fractal geometry based and assumes that the (statistical distribution of) parameters of the larger blocks are equal to the parameters of the microfracture networks, i.e., that the system is scalable.  
The modelling was done using a fractal geometry-based discrete fracture network (DFN) algorithm of the REPSIM software package [14]. The software generates fracture networks stochastically based on the statistical distribution of observed parameters. The input parameters required for each homogenous unit cube are:  
1. Fractal dimension for fracture centres for spatial density distribution.
2. Parameters of the fracture length distribution;
3. Lower and upper extreme fracture length;
4. Aperture as a function of length [29]; and
5. Strike and dip data.  
Several runs of models were conducted with varying gneiss and granite contents, yielding the corresponding number of realisations of the model. The geometry of the models consisting of both gneiss and granite were built to simulate inclusions; accordingly, the granite cells were positioned in an ellipsoid-like shape in the centre of the modelled volume (Figure 6).  
The simulations result in fracture networks that have the same spatial statistics as the input parameters. The generated fracture networks are equally probable realizations of the given stochastic distribution. The number of intersecting, thus connective, fracture groups and the size of these clusters can be detected from the results. It is also possible with REPSIM to visualize the generated fracture networks, as it is shown in Figure 7.  
Figure 6: Schematic image of the models consisting of both pegmatoid granite (PGR) and gneiss (VGN). Volumetric content of PGR increases with 25% increments (**a**–**e**). The size of the models is 10 m \(\times\) 10 m \(\times\) 10 m.
## 3 Results and Discussions
### Accuracy Verification of CORDIC Algorithm
#### 3.2.3 Dynamic Range Transformation  
The absolute error distribution of the MATLAB logarithmic simulation based on the improved scale-free CORDIC algorithm is shown in Figure 10b. As can be seen from the figure, the module can achieve absolute errors below 7.0 \(\times\) 10\({}^{-4}\). To further verify the accuracy of the algorithm, similarly, select the sampling points on the ModelSim simulation diagram to read the data and then analyze the absolute error. Table 3 shows the ModelSim logarithmic simulation data based on the improved scale-free CORDIC algorithm. The absolute error of the ModelSim logarithmic simulation is in the order of 10\({}^{-3}\), which is significantly larger than the MATLAB simulation results, mainly due to the influence of the number of data bit widths or the number of pipeline stages. In summary, logarithmic compression based on the improved scale-free CORDIC algorithm can achieve better accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Value**} & \multirow{2}{*}{**Theoretical Value**} & \multicolumn{2}{c}{**Partitioned-Hybrid**} & \multirow{2}{*}{**Absolute Value**} \\  & & **CORDIC Value** & \\ \hline
1 & 1.0000 & 0.9946 & \(5.4\times 10^{-3}\) \\
2 & 1.4142 & 1.4047 & \(9.5\times 10^{-3}\) \\
3 & 1.7321 & 1.7402 & \(8.1\times 10^{-3}\) \\
4 & 2.0000 & 1.9959 & \(4.1\times 10^{-3}\) \\
5 & 2.2361 & 2.2322 & \(3.9\times 10^{-3}\) \\
6 & 2.4495 & 2.4581 & \(8.6\times 10^{-3}\) \\
7 & 2.6458 & 2.6426 & \(3.2\times 10^{-3}\) \\
8 & 2.8284 & 2.8352 & \(6.8\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ModelSim square root simulation data based on the partitioned\(-\)hybrid CORDIC algorithm.  
Figure 10: (**a**) Absolute error distribution of the MATLAB square root simulation based on the partitioned\(-\)hybrid algorithm. (**b**) Absolute error distribution of the MATLAB logarithmic simulation based on the improved scale\(-\)free CORDIC algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Value**} & \multirow{2}{*}{**Theoretical Value**} & \multicolumn{2}{c}{**Improved Scale-Free**} & \multirow{2}{*}{**Absolute Value**} \\  & & **CORDIC Value** & \\ \hline
1 & 0 & 0.0031 & \(3.1\times 10^{-3}\) \\
2 & 0.6932 & 0.6991 & \(5.9\times 10^{-3}\) \\
3 & 1.0986 & 1.1043 & \(5.7\times 10^{-3}\) \\
4 & 1.3863 & 1.3877 & \(1.4\times 10^{-3}\) \\
5
## 3 System Model
### Markov Decision Process Formulation  
As the throughput at this frame is only related to the data rate selected before transmission, we can transform the above problem into an MDP problem represented by a tuple \((\mathcal{S},\mathcal{A};P;r)\), where \(\mathcal{S}\) is the set of states observed from the environment, \(\mathcal{A}\) is the set of actions from the available selections, the probability distribution of the system is written as \(P:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow\mathcal{R}\), and \(r:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow\mathcal{R}\) is the reward.  
We first define the state at time slot \(t\) as \(s_{t}\). This part consists of two components: the distance \(d_{t}\) of the satellite-to-ground channel and real-time weather \(w_{t}\). As we can obtain the distance \(d_{t}\) from the satellite position \((x_{t},y_{t},z_{t})\) and the UE position \((lat_{t},lont_{t})\). Moreover, rain loss at different latitudes and longitudes around the world varies. Therefore, we retain the original position information of the state. The connection between the satellite and the UE is concise. Even the longest time is less than 3 min in the Starlink scenario. Hence, we use the weather at the beginning of the connection \(w_{t}\) as the real-time weather for each connection in a single connection. In summary, we define \(s_{t}=\{lat_{t},lont_{t},x_{t},y_{t},z_{t},w_{t}\}\).  
Next, we define the action at time slot \(t\) as \(a_{t}=\{DR_{t}\}\), where \(DR_{t}\) is the data rate at time \(t\). It is worth mentioning that the action will not affect the environment itself because the choice of data rate will not affect the location of the satellite and UE, nor will it affect the weather. It will, however, affect the throughput. Although the \(BER_{t}\) will be very low if we choose an overly conservative data rate, the throughput will also be low; on the contrary, selecting an overly aggressive action will cause the receiver to make mistakes, and the \(BER_{t}\) is so high that it cannot communicate normally.  
Figure 2: Different data rates correspond to different BERs at the same SNR.  
Finally, we define the reward at time \(t\) as \(r_{t}=\{DR_{t}[1-PER(DR_{t},SNR_{t})]\}\), where \(PER(\cdot)\) represents the package error rate of the corresponding data rate \(DR_{t}\) and \(SNR_{t}\). In order to transmit more data per time slot, we define \(r_{t}\) as the number of bits transmitted per unit bandwidth. The cumulative reward is \(R=\sum_{l=1}^{\infty}\gamma^{l}r_{t}\), where \(\gamma\) is the discount rate.
## 4 Conclusions  
Ce-based \(RE_{2}\)Fe\({}_{14}\)B magnetic materials were successfully synthesized by a modified reduction-diffusion (R-D) process using \(RE\)FeO\({}_{3}\) precursors prepared through the solid-state reaction, using rare-earth oxides, Fe\({}_{2}\)O\({}_{3}\) and Fe as raw materials. The single-phase Ce\({}_{2}\)Fe\({}_{14}\)B was synthesized at a Ce:Fe ratio of 1:5.5, and (Ce\({}_{0.5}\)Nd\({}_{0.5}\))\({}_{2}\)Fe\({}_{14}\)B and Nd\({}_{2}\)Fe\({}_{14}\)B phases were also successfully synthesized by the modified R-D process. The single-phase  
\begin{table}
\begin{tabular}{c c c} \hline
**CeFe** & \(M\) **at 2.6 T (emu/g)** & \(H_{\text{c}}\) **(Oe)** \\ \hline
1:5 & 117.2 & 86.5 \\
1:5.5 & 118.5 & 85.7 \\
1:6 & 123.5 & 89.9 \\
1:6.5 & 146.1 & 87.0 \\
1:7 & 150.0 & 72.7 \\ \hline \end{tabular}
\end{table}
Table 1: Magnetic properties of samples prepared by the modified reduction-diffusion process with different Ce/Fe ratios.  
Figure 9: M –H curves of samples prepared by the modified reduction-diffusion process with different Ce/Fe ratios (Ce:Fe = 1:5, 5.5, 6, 6.5 and 7).  
Ce\({}_{2}\)Fe\({}_{14}\)B sample exhibited a coercivity of 89 Oe, primarily due to the relatively large particle size.  
The modified R-D process can provide significant advantages in rare-earth magnet production, such as a cost-effective approach through the use of oxide raw materials and simple equipment. Furthermore, the successful synthesis of various \(RE_{2}\)Fe\({}_{14}\)B compounds demonstrates the ability of the modified R-D process for incorporating different rare-earth elements. While further optimization is needed to improve the magnetic properties, such as particle size refinement and the sintering process for bulk magnet fabrication, the results of this work provide an approach to a more economical and sustainable production process for rare-earth permanent magnets.  
S.L., K.L. and S.-I.Y.; methodology, S.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L., Y.-M.K., J.-W.L., J.P., S.-I.Y. and C.P.; supervision, S.-I.Y. and C.P. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program (Project No. P0026154).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 5 Results
### Coupling Compressor and Mixer  
Finally, once the optimization of the independent mixer was carried out, one last step was missing: the coupling of the compressor and the mixer. Moreover, a deeper analysis of the flow evolution was included since, being the ultimate case, it was clearly of interest. Having the averaged values of the main performance values of the compressor, the confirmation of the initial hypothesis had to be checked, so those values had to be compared, in order to assess the impact of the mixer design on shifting the compressor and mixer's working point.  
Table 6 summarises some of the main results in the mixer and in the interface between the compressor and the mixer, as it is the most sensitive part which gives information about the strength of the taken hypothesis. By analysing the obtained data, it can be seen that differences of around 3% and a maximum of 4.5% were found between the simulation of the isolated mixer and the complete geometry, thus backing up quite strongly the adopted hypothesis and, in consequence, the proposed method of independent mixer optimization using a multidimensional approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Compressor Outlet**} & \multirow{2}{*}{**Compressor + Mixer**} & \multirow{2}{*}{**Difference (\%)**} \\  & **BC** & & \\ \hline _in_ (kg/s) & 5.92 & 5.92 & 0 \\ _in\({}_{cor}\)_ (kg/s) & 4.3 & 4.25 & 1.3 \\ _in\({}_{bypass}\)_ (kg/s) & 1.61 & 1.66 & 4 \\ _BPR_(\(-\)) & 2.67 & 2.55 & 4.5 \\ _Pt\({}_{out}\)_ (Pa) & 16,635 & 16,504 & 1 \\ _Pt\({}_{out}\)_ (Pa) & 13,400 & 13,200 & 1.5 \\ _Pt\({}_{cor}\)_ (Pa) & 17,713 & 17,200 & 2.9 \\ _Pt\({}_{bypass}\)_ (Pa) & 17,104 & 16,649 & 2.6 \\ _π\({}_{core}\)_ (\(-\)) & 1.47 & 1.43 & 2.7 \\ _π\({}_{bypass}\)_ (\(-\)) & 1.42 & 1.385 & 2.3 \\ _T\({}_{t,out}\)_ (K) & 328 & 327 & 0.3 \\ _T\({}_{t,core}\)_ (K) & 328 & 327 & 0.3 \\ _T\({}_{t,bypass}\)_ (K) & 329 & 327 & 0.5 \\ _π\({}_{core}\)_ (\(-\)) & 0.84 & 0.8 & 3.5 \\ _η\({}_{bypass}\)_ (\(-\)) & 0.74 & 0.72 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Flow conditions at the interface between the compressor and the mixer.  
Figure 15: Velocity patterns comparison between initial (**left**) and final (**right**) 3D geometries. The top cut is given by the intersection of the geometry and the plane \(Z=-0.5\). The bottom cut is given by the intersection of the geometry and the plane Y = 0 (symmetry plane).  
It was also interesting to check the correct behaviour of the compressor. For that, as mentioned before, the simulated point being a design point, the stream vectors corresponding to the flow at the rotor and stator blades are shown in Figure 16; we observe that the incidence of the flow was slightly positive but very close to zero. Moreover, the relative Mach contours confirmed the correct behaviour of the channels, as the incipient shock waves did not block them.
## 3 Results and Discussion
### Adsorption Performance of the CDM-HCP  
Figure 5 shows that \(q_{e}\) was increased with increasing amounts of \(\beta\)-CD up to 14.28 umol-g\({}^{-1}\). However, \(q_{e}\) decreased when the amounts of \(\beta\)-CD exceeded 14.28 umol-g\({}^{-1}\). It was due to the high \(\beta\)-CD loading amounts, which led to the non-uniformity of the particle size distribution of the CDM-HCP, decreasing the mass transfer of the anilines.  
Figure 6 shows that the changes in ionic strength had no significant effects on the adsorption of anilines, demonstrating that the CDM-HCP has high stability within a wide range of ionic strength. \(q_{e}\) slightly decreased with increasing NaCl concentration in the range of 0.1 to 0.5 mol-L\({}^{-1}\). It may be that the solubility of adsorbate will increase significantly with increasing electrolyte concentration due to the salt effect, weakening the adsorption capacity of the adsorbent.  
The solution's pH is an important factor affecting the adsorption of anilines on the CDM-HCP. The influence of pH on the aniline adsorption by the CDM-HCP was studied in a pH range of 3 to 11. Aniline adsorption was obviously affected by the solution's pH, which affected the surface charge of the adsorbent and the ionization degree and speciation of the adsorbate. As shown in Figure 7, when the solution's pH was less than or equal to 4, \(q_{e}\) for the anilines in the aqueous phase was  
Figure 5: Effect of the amounts of \(\beta\)-CD on the aniline adsorption of the CDM-HCP.  
Figure 6: Influence of NaCl concentration on aniline removal using the CDM-HCP as adsorbent.  
extremely low, which was probably because the cyclodextrin molecules were hydrolyzed under acidic conditions and their structure was destroyed, decreasing the adsorption capacity. When pH was from 5 to 7, the adsorption capacity of the CDM-HCP rose significantly for the anilines. In this case, the adsorbate existed in a molecular state, which facilitates the interaction with the functional groups of the CDM-HCP and had a maximum \(q_{e}\) of about pH 7. However, higher solution pH also decreased the uptake of anilines. It may be due to the formation of soluble hydroxyl complexes by decreasing the free ions. The optimum pH values were 7.0 for aniline, 7.0 for \(p\)-MA, 6.0 for \(p\)-CA, and 6.0 for \(p\)-ABC.
# A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5
###### Abstract  
The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and [EMAIL_ADDRESS] has improved by 4.6%. The improved model exhibits superior performance.  
YOLOv5; fault detection; deformable convolution; BiFPN
## 4 Conclusions  
The simultaneous removal of N-NH4+ from industrial wastewater using immobilized microalgae and zeolite is effective. The rate and efficiency of the N-NH4+ removal was dependent on the type (zeolite, microalgae), form (suspended or immobilized), and dosage of the sorbent. For each single sorbent, a higher maximum sorption capacity for clinoptilolite (24.9 mg/g) and _C. vulgaris_ (13.8 mg/g) was achieved than for _S. armatus_ (5.5 mg/g). The higher the initial concentration of ammonium nitrogen in relation to the concentration of clinoptilolite and _C. vulgaris_, the higher the sorption capacity obtained for these sorbents. The removal efficiency of immobilized microalgae reached 60% (_C. vulgaris_) and 42% (_S. armatus_), and that of suspended microalgae reached 52% (_C. vulgaris_) and 44% (_S. armatus_). A higher removal efficiency was obtained for zeolites (86% and 92%) and zeolites with immobilized _C. vulgaris_ or _S. armatus_ (up to 86% and 79%) than for zeolites with suspended _C. vulgaris_ or _S. armatus_ (up to 85% and 75%, respectively). The recycling of spent hydrogel, zeolite, and wastewater is possible; this represents a circular bioeconomy loop.