esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 2 Preliminaries
### Unscented Kalman Filter
#### 2.2.1 Predict  
First, a set of \(2n+1\) sigma points are computed by the estimated state and covariance matrix of last time step:  
\[\begin{split}\chi^{0}(k-1|k-1)&=\widehat{\mathbf{x} }(k-1|k-1),\\ \chi^{i}(k-1|k-1)&=\widehat{\mathbf{x}}(k-1|k-1)\\ &\quad+\left(\sqrt{(n+\lambda)\mathbf{P}(k-1|k-1)}\right)_{i},i=1,\ldots,n,\\ \chi^{i}(k-1|k-1)&=\widehat{\mathbf{x}}(k-1|k-1)\\ &\quad-\left(\sqrt{(n+\lambda)\mathbf{P}(k-1|k-1)}\right)_{i-n},i= n+1,\ldots,2n,\end{split} \tag{10}\]where \(\left(\sqrt{(n+\lambda)\mathbf{P}(k-1|k-1)}\right)_{i}\) is the \(i\)-th column of the matrix square root of \((n+\lambda)\mathbf{P}(k-1|k-1)\), \(n\) denotes the dimension of the state vector, and \(\lambda\) represents a compound scaling factor defined by  
\[\lambda=\alpha^{2}(n+\epsilon)-n, \tag{11}\]  
where \(\alpha\) determines the extent of the spread of the sigma points, which is usually chosen as \(0<\alpha\leq 1\), \(\epsilon\) is a scaling factor and is usually set to \(3-n\).  
The transformed set is given through the process model  
\[{\chi^{i}}^{*}(k|k-1)=\mathrm{f}\left(k-1,\chi^{i}(k-1|k-1)\right),i=0,\ldots, 2n. \tag{12}\]  
Then, the predicted state mean and associated covariance matrix are calculated by  
\[\widehat{\mathbf{x}}(k|k-1)=\sum_{i=0}^{2n}w_{m}^{i}{\chi^{i}}^{*}(k|k-1), \tag{13}\]  
\[\begin{split}\mathbf{P}(k|k-1)&=\sum_{i=0}^{2n}w_{ c}^{i}\left[{\chi^{i}}^{*}(k|k-1)-\widehat{\mathbf{x}}(k|k-1)\right]\\ &\quad\times\left[{\chi^{i}}^{*}(k|k-1)-\widehat{\mathbf{x}}(k| k-1)\right]^{T}+\mathbf{Q}(k-1),\end{split} \tag{14}\]  
where the corresponding weights of the state and covariance matrix are defined as  
\[\begin{split} w_{m}^{0}&=\frac{\lambda}{(n+\lambda )},\\ w_{c}^{i}&=\lambda/(n+\lambda)+(1-\alpha^{2}+\beta), \\ w_{m}^{i}&=w_{c}^{i}=\frac{1}{2(n+\lambda)},i=1, \ldots,2n.\end{split} \tag{15}\]  
with \(\beta\) being related to prior knowledge of the distribution of \(\mathbf{x}(k)\) (for Gaussian noises, \(\beta=2\) is optimal).
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Influence of Excitation Parameters on Nonlinear Vibration Characteristics of Transmission System
#### 3.2.5 Influence of Input Speed on Vibration  
In accordance with the meshing angular frequency formula, the input speed of the transmission system directly defines its meshing angular frequency, while the meshing angular frequency specifies the period of error excitation and the vibration response will peak at this frequency.  
Excitation \(T_{at}=10\) N - m, transmission error \(E=20\) m, torque fluctuation is \(T_{f}=0\) N - m, bearing backlash \(b_{b}=0\) m, tooth backlash 2 \(b=10\) m, and input speed n = 900, 1000, 1100 r/min respectively, to obtain the time domain curve comparison diagram of external gear vibration, as shown in Figures 10 and 11.  
Figures 10 and 11 indicate that for n = 900, 1000, 1100 r/min, the radial vibration amplitude is 5.578 m, 8.059 m, and 10.729 m, whereas the axial vibration amplitude is 0.8007 m, 0.9603 m, and 1.2034 m.  
Further examination of the radial vibration amplitude of the gear, when the speed is 400 r/min-1200 r/min, reveals that when the input speed is low, the amplitude of the radial and axial vibration curves of the gear pair varies noticeably in the same manner as the speed. When the input speed is between 3000 r/min and 4000 r/min, the vibration amplitude tends to remain steady.
# The Presence of Marine Filamentous Fungi on a Copper-Based Antifouling Paint
## 3 Results and Discussion
### Copper Resistance of Fungal Isolates  
In order to prove that fungal isolates are able to grow on antifouling paints, their sensitivity to different copper concentrations is tested in laboratory experiments (Table 2). Due to low solubility of CuO, CuSO\({}_{4}\) was used in this experiment. Previous studies suggest that CuSO\({}_{4}\) is more toxic compare to CuO [53]. Thus, the isolates are more resistant to CuO than is reported in Table 2. Generally, isolates from antifouling paint can tolerate higher concentrations of copper. Five out of six isolates from the antifouling paint can tolerate an average daily release rate of copper 3.8 \(\mu\)g cm\({}^{-2}\) day\({}^{-1}\)[30] from the tested paint (Table 2). The highest copper resistance was observed for _Aspergillus terreus_. This fungus can tolerate 2% of CuCl\({}_{2}\) in a polyvinyl chloride coating in a laboratory experiment [54] and can be used to remove heavy metals from water [54]. In opposite, fungal isolates from unprotected substrata had low tolerance to copper (Table 2). This suggests that isolatesfrom the copper-based antifouling paint are adapted to high copper concentrations and could grow and play an important role in biofilms.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Species** & **Substrate** & **No** & **GenBank Accession Number** & **Picture** \\ \hline _Aspergillus tubingensis_ & Antifouling paint & H1 & MN947598 \\ _Aspergillus terreus_ & Antifouling paint & H2 & MN947599 \\ _Alternaria sp._ & Antifouling paint & H3 & MN947600 \\ _Aspergillus niger_ & Antifouling paint & H4 & MN947601 \\ _Cladosporium haltolerans_ & Antifouling paint & H6 & MN947602 \\ _Cladosporium omanense_ & Antifouling paint & H7 & MN947603 \\ _Aspergillus pseudodeflectus_ & Not painted panel & H90 & MN947605 \\ _Cladosporium omanense_ & Not painted panel & H89 & MN947604 \\ _Cladosporium omanense_ & Ropes & H91 & MN947606 \\ _Parengyodontium album_ & Ropes & H92 & MN947607 \\ \hline \end{tabular}
\end{table}
Table 1: The list of fungal isolates from panels painted with the antifouling paint and not painted (control) panels and ropes.
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 4 Results and Discussion
### Bubble Sizes from \(R=0.01\) m to \(0.05\) m
#### 4.2.3 Bubble Shape Deformation and Rising Velocity  
The vapor bubbles can be classified into three different shapes: sphere, spheroid, and spherical cap. The bubble is spherical when its inertial force is much lower than surface tension or viscous force. As bubble size increases, velocity rises since the larger bubble  
Figure 4: Schematic representation of centroid positions of bubble along the \(x\)-axis with different bubble radii of (**a**) \(R\) = 0.01 m, (**b**) \(R\) = 0.015 m, (**c**) \(R\) = 0.025 m, (**d**) \(R\) = 0.035 m and (**e**) \(R\) = 0.05 m.  
will induce larger buoyancy and hence higher oscillation. The shape of the bubble is transforming the drop from a sphere into oblate spheroid shapes. Due to the resistance in the liquid medium, when bubbles are large enough, they tend to have flat and often curved bases, breaking up the curve of the bubble shape. This is known as the spherical cap. Figure 5 shows the rising velocity and bubble shape for different bubble radii. The computational domain of Figure 5 is 0.12 m \(\times\) 0.3 m to perform a better picture of the bubble rising. When the bubble size \(R\) is 0.01 m and 0.015 m, the bubble shape remains spherical. The bubble shows a spheroid shape with a radius \(R\) = 0.025 m. The bubble shape can be found in a spherical cap with a radius \(R\) = 0.035 m and 0.05 m. It is noted that the deformation of the bubble occurs near the capillary length (\(R<L_{c}\approx 3.23\times 10^{-2}\) m). Figure 6 shows both our simulation results (in red lines) and previous experiment work (in black lines) [24]. Since the fluid properties in the two works are different, the magnitudes of bubble size and velocity are different, as expected. However, our numerical work shows a similar trend to experiment one qualitatively.  
Figure 5: Simulation results of the rising velocity and bubble shape for different bubble radii.  
Figure 6: The rising velocity of bubbles compared with the referenced values.
# Chronological Evaluation of Gait Ability and Posture Balance after Adult Spinal Deformity Surgery
###### Abstract  
Adult spinal deformity (ASD) is highly prevalent in persons older than 65 years, affecting more than 30% of the total population. There are many important reports that describe excellent postoperative results for ASD surgery. However, there is no report that describes a chronological evaluation of gait ability and spinal balance after ASD surgery. The present prospective study aimed to determine the recovery time of gait and spinal balance ability after adult spinal deformity (ASD) surgery. Patients with ASD (\(n\) = 25: mean age, 73.0 years) who underwent corrective surgery and preoperative gait and spinal balance ability evaluation. Spinal radiographs and gait analysis data (static balance; single-leg standing, dynamic balance; time-up and go test, gait ability; 10 m walk velocity) were acquired preoperatively, 1 month, 6 months, and 12 months after surgery. The radiographic parameters of the spine and pelvis (lumbard fordosis; LL, pelvic tilt; PT, sagittal vertical axis; SVA, pelvic incidence; PI) were measured. The mean postoperative SVA, LL, PT, and PI-LL became 30.0 mm, 50.1 degrees, 14.1 degrees, and 0.35 degrees, respectively. Preoperative, all spinal balance and gait ability were worse than the normal threshold (one-leg standing; 14.2 s vs. 15.0 s, time-up and go test; 13.9 s vs. 13.5 s, 10-m walk speed; 0.91 m/s vs. 1.00 m/s). At 12 months after surgery, all spinal balance and gait ability became normal or improved (single-leg standing; 14.7 s, time-up and go test; 11.0 s, 10-m walk speed; 1.09 m/s). The ODI at one-year follow-up improved from 46.8 \(\pm\) 12.6% to 27.8 \(\pm\) 18.1%. In conclusion, gait and spinal balance analysis revealed that ASD patients could not recover gait and spinal balance ability one month after surgery. At 12 months after surgery, gait ability and dynamic spinal balance became better than preoperative levels. However, the static spinal balance was at the same level as that of preoperative status.  
adult spinal deformity; surgery; rehabilitation; gait ability; spinal balance +
Footnote †: journal:
## 6 Comparison between Experimental and Predicted Results  
The measured ultimate shear strengths were verified using shear prediction equations presented in this study. For beams of series B1 with a shear span-to-effective depth of 2, the measured shear strengths were greater than the shear strengths predicted by using equations summarized in this study. On the other hand, the measured shear strengths of series B1 with 3 and 4 and a longitudinal reinforcement ratio of 0.75% were less than the predicted shear strengths proposed by [PERSON] (1986), [PERSON] and [PERSON] (1987), and [PERSON] et al. (2002), [PERSON] (2006), and [PERSON] (2014). In contrast, the equation proposed by [PERSON] (2011) exhibited a result that was slightly less than the measured shear strength for specimen B1-1-0.75. In addition, the shear strength equation proposed by [PERSON] (2014) slightly overestimated the shear strength of specimen B1-4-1.5. For specimens in series B1, the comparison between measured and predicted strengths showed that most equations overestimated the shear strength of SFRC beam specimens, while only both equations proposed by [PERSON] et al.  
Figure 7: General strain, idealized stress distribution, and internal force of steel fiber-reinforced concrete (SFRC) beam section [41].  
(1992) gave results smaller the than test results. Among specimens B1, only specimen B1-2-1.5 failed in shear. For series B2, all strengths obtained by using shear strength equations reviewed in this study were less than those obtained from the experimental program.  
It can be expected that the shear strength prediction equations proposed by previous researchers were developed by using their own test results or a beam database dominated by beam specimens failing in shear. Therefore, for a specimen with flexural or flexural-shear failure, it is absolutely normal if the predicted shear strength is greater than the measured shear strength. Meanwhile, as shown in Table 6, only the Equation (11) could give results closer to the test results of series B2. In general, Equation (11) can show more precise and reasonable results compared to the other reviewed equations. The mean and COV values exhibited by using Equation (11) are 1.02 and 0.28, respectively.
## 6 Discussion  
This research studies the implementation of FM-ACSK transceiver prototype in SDR. The purpose of the given research is a validation of the theoretical results obtained in earlier research and assessment of the real-world performance of a given modulation scheme taking into account practical problems, such as timing and frequency synchronization, multi-path propagation and multiple access interference (MAI). In order to make such a study, it was necessary to develop a new symbol synchronization unit that fits the particular scheme of ACSK demodulation. Moreover, compatibility of FM-ACSK with simple FEC schemes were tested. The experimentation and measurement campaign involved four different tests, which allowed us to learn about the real-world performance of FM-ACSK.  
Figure 17: Auto-correlation of used spreading sequence.  
Figure 18: BER versus SNR for data transmission with FM-ACSK and FHSS. SD—strobe-based detector; ED—energy-based detector. FM deviation is 10 kHz.  
The BER versus transmit power measurement results have shown that the communication system is capable to provide acceptable BER \(10^{-2}\) at SNR down to 8-10 dB. The newly proposed ED provides approximately 10 times lower BER compared to the SD one, proposed in earlier publications. The communication system is susceptible to STO due to the triangular shape of symbols at the input of the DM unit.  
The BER versus CFO tests have shown that the transceiver is tolerant to large carrier frequency offsets and can keep BER \(10^{-2}\) with CFO up to at least half of FM deviation. This property is achieved by the employment of non-coherent FM on top of coherent ACSK. Immunity to the CFO comes at the cost of a lower link budget as FM has a pronounced threshold effect and does not work at low SNR.  
Unfortunately, MAI test has shown that orthogonality among FM-ACSK users, which employ similar chaotic oscillators with slightly different parameters, is low and there is considerable interference between users with the same carrier frequency. For providing multiple access in FM-ACSK based communication system, it is necessary to employ dedicated multiple access schemes such as frequency division multiple access (FDMA) or use advanced techniques such as non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC).  
The image transfer test has shown that the developed transceiver prototype can work fully autonomously and can be used for real data communication. Test results demonstrated acceptable performance of the transceiver in an office environment at the significant distance between the transmitter and receiver. Employment of Hamming FEC can provide significant improvement of BER, however, in case of insufficient code rate, FEC can cause the increase of BER.  
From BER plots in Figures 12, 14 and 18 it can be concluded that FM-ACSK systems without FEC have error floor at approximately BER \(\approx 10^{-3}\). This error floor can be caused by inaccuracies of the chaotic synchronization employed for the despreading of the chaotic waveforms.
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.2 Simulation of Interference Detection Performance for Anti-Jamming Algorithm  
The next step involves validating the effectiveness of the proposed algorithm's interference detection performance. Three experiments were designed to compare the interference detection performance under different conditions of jamming center frequency offset from the signal center frequency and bandwidth. For this purpose, we defined the interference detection ratio (IDR), denoted as \(IDR=N_{d}/N_{j}\), to measure the interference detection performance, where \(N_{d}\) is the number of detected interference spectral lines, and \(N_{j}\) is the total number of interference spectral lines. The specific simulation parameters are provided in the Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Scene One** & **Scene Two** & **Scene Three** \\ \hline Jamming center frequency offset signal center frequency difference & 0 kHz & 100 kHz & 200 kHz \\ \hline Jamming signal bandwidth & & 10 kHz, 20 kHz, 40 kHz \\ \hline JSR & & 5 dB–30 dB, the step was 2 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameter settings of interference detection performance.  
Figure 6: Change in power spectrum before and after anti-jamming under strong jamming for the SCCI algorithm and SCCME algorithm (JSR 30 dB). (**a**) Before anti-jamming; (**b**) after anti-jamming (SCCI); (**c**) after anti-jamming (SCCME).  
Figure 7: Change in power spectrum before and after anti-jamming under weak jamming for SCCI algorithm and SCCME algorithm (JSR 15 dB). (**a**) Before anti-jamming; (**b**) after anti-jamming (SCCI); (**c**) after anti-jamming (SCCME).  
Figure 8 shows the interference spectral line detection rate under different JSRs. The horizontal axis represents the JSR, and the vertical axis represents the interference spectral line detection rate.  
Figure 8 demonstrates the following. (1) For the SCCME algorithm, the farther the jamming center frequency is from the carrier, and the wider the jamming signal bandwidth, the lower the interference detection probability and the greater the impact of the model's estimation error on interference detection. (2) In different scenarios, the interference detection rate of the SCCI algorithm was consistently >96%, indicating that the algorithm is insensitive to the center frequency of the jamming signal.
# Spontaneous Dural Carotid-Caverous Fistula Treated with Microcoil Insertion
###### Abstract  
This report includes a case of a 65-year-old woman presenting with a spontaneous dural carotid-cavernous fistula. Biomicroscopic examination of the anterior segment showed significant conjunctival chemosis, dilatation of the episeleral vessels, narrow anterior chamber, and a protoposis of the right eye, whereas the fellow eye was unremarkable. Retinal examination revealed an impaired arteriovenous ratio (A/V) from 1-4 to 1-2 and two extensive cotton exudates. An ultrasound scan (US) demonstrated congestion of the upper ophthalmic vein. Selective brain angiography through right femoral catheterization revealed a dural fistula of the wall of the cavernous right sinus. The patient underwent surgery on the superior ophthalmic vein and insertion of a micro-catheter in the cavernous sinus under CT guidance. Furthermore, a trans-femoral catheter was placed in the carotid artery on the same side as the fistula to allow arteriographic controls after micro coil positioning and embolization. Angiographic follow-up immediately after positioning the coils showed the occluded fistula and a regular flow circulation between the internal and the external carotid arteries. After treatment, the patient presented a complete resolution of symptoms. Conservative management is effective and safe in treating patients with carotid-cavernous fistula and mild clinical features because of a good chance of spontaneous or secondary thrombosis after arteriographic occlusion.  
c 2022 22 July 2022 4 October 2022 2022 July 2022 4 October 2022 2022 2022 July 2022 24 October 2022 24 October 2022 Published: 8 October 2022  
The etiology of the indirect carotid-cavernous fistula is not completely known; however, these could be associated with arterial hypertension, cerebral arteriosclerosis, diabetes, collagen diseases (Ehlers-Danlos syndrome, collagenosis), and post-menopausal age in women [1; 2; 3].  
Although dural carotid-cavernous fistula could be approached with conservatory therapy, untreated patients could present severe ocular and neurological complications, in particular the presence of ocular risk factors, such as increased intraocular pressure (IOP), retinal ischemia, and optic nerve edema [3].  
The advent of endovascular treatments significantly reduced the morbidity related to cavernous carotid fistula.  
In this report, we describe a case of spontaneous dural carotid-cavernous fistula that occurred in a healthy woman, successfully treated with endovascular micro coil insertion.
## 3 Theoretical Model and Computational Analysis of Aberrations in Double Prism Compensation Devices
### Aberration Result Verification
The error between the theoretical and actual values of these two examples is partly due to the generation of induced aberrations and higher-order aberrations during the adjustment process. Additionally, an important source of error stems from the approximation used in the aberration formula. To make the aberration formula more accurate, the field vector and pupil vector should be as small as possible; however, this condition is generally not met, leading to an increase in the error. In a rotating prism, the effect of aberrations and the mechanisms and analysis of errors caused by the increase of field vectors and pupil vectors are complex, which is an area for future research.
## 2 Materials and Methods
### Methods
#### 2.3.4 Analysis of Synergistic Effects  
(a) Gray correlation analysis  
The gray correlation analysis determines whether the relationship between sequences is close or not, mainly through the similarity of their curve geometries [34]. If the curves are more similar, the correlation between the series is greater, and if it is the other way around, the correlation is lower. This method is frequently used in the analysis of influence factors because it is less demanding and less computationally intensive than the mathematical and statistical methods. This method was used in this study to calculate the correlation between the hydrological process factors and EQ. The calculation formula is as follows:  
\[\gamma_{oi}=\frac{1}{n}\sum_{k=1}^{n}\xi_{oi}(k) \tag{9}\]  
\[\xi_{oi}(k)=\frac{\frac{min\;min\;min\;min\;\Delta_{oi}(k)+p\;max\;max\;max\; \Delta_{oi}(k)}{\Delta_{oi}(k)+p\;max\;max\;\Delta_{oi}(k)}}{\Delta_{oi}(k)+p \;max\;max\;\Delta_{oi}(k)} \tag{10}\]  
\[\Delta_{oi}=|x_{o}{}^{\prime}(k)-x_{i}^{\prime}(k)|,\;i=1,2,\ldots,m;\;k=1,2, \ldots,n\]  
where \(\gamma_{oi}\) is the gray correlation degree, \(\xi_{oi}\) is the gray correlation coefficient, and \(\rho\) is the discrimination coefficient.  
(b) Pearson correlation analysis  
The Pearson correlation analysis between the URB's \(EQ\) and hydrological process factors from 2001 to 2019 was conducted at the subbasin scale. Moreover, the correlation coefficients were tested for significance to reflect the degree of spatial and temporal correlation between \(EQ\) changes and hydrological process factors and identify the response of \(EQ\) to changes in the hydrological process factors. The calculation equation [35] is as follows:  
\[r=\frac{\sum_{i=1}^{n}\left(EQ_{i}-\overline{EQ}\right)(y_{i}-\overline{y})}{ \sqrt{\sum_{i=1}^{n}\left(EQ_{i}-\overline{EQ}\right)^{2}}\sqrt{\sum_{i=1}^{ n}(y_{i}-\overline{y})^{2}}} \tag{11}\]  
where \(r\) is the correlation coefficient between \(EQ\) and the hydrological process factors; \(EQ_{i}\) and \(y_{i}\) are the mean values of \(EQ\) and the hydrological process factors in year \(i\), respectively; \(\overline{EQ}\) and \(\overline{y}\) are the mean values of \(EQ\) and the hydrological process factors in 19 years, respectively; and \(i\) represents the number of years. If \(r>0\), the \(EQ\) and the hydrological process factors are positively correlated and vice versa; the larger the \(r\), the stronger the correlation between them.