esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Elemental Mapping  
The elemental mapping and EDS analysis were performed on the GO/Zn (OH)x nanocomposite to investigate the elemental composition/homogeneity of the samples. Figures 2-5 show the EDS mappings of the non-coated GO and GO/Zn (OH)x composite materials. As can be seen in Figure 2, the C and the oxygen are homogeneously mixed throughout the samples, indicating a good oxidation of the graphite to form the GO. The EDS mappings of the 25% GO/Zn (OH)x composite (Figure 3) show a homogenous distribution of the C, O, and Zn throughout the sample. The EDS results of the 50% and 75% Zn (OH)x composites (Figure 4) again show a homogenous distribution of the C, O, and Zn in both samples. The homogenous mixture of the C, O, and Zn throughout all the composite samples indicates the presence of either a coated sample or a composite material. The results of the EDS analysis are shown in Table 1. The 25% Zn (OH)x composite shows that 14% of the sample was Zn. The 50% Zn (OH)x composite showed that the sample has a 37 wt.% of Zn present, while the 75% Zn (OH)x sample showed a 31 wt.% of Zn. The observed leveling-off from the Zn concentration in the 50 and 75% samples may be due to the surface site available for Zn binding on the GO.  
Figure 1: SEM images of GO/Zn (OH)x composite: (**A**) Non-coated GO. (**B**) GO with 25 wt.% Zn (OH)x. (**C**) GO with 50 wt.% Zn (OH)x and (**D**) GO with 75 wt.% Zn (OH)x coating.  
Figure 3: Elemental composition of GO with 25% Zn (OH)x coating.  
Figure 2: Elemental composition of non-coated GO.  
Figure 4: Elemental composition of GO with 50% Zn (OH)x coating.  
Figure 5: Elemental composition of GO with 75% Zn (OH)x coating.
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 5 Prediction Model  
### The Suggestion of Prediction Model  
In this study, the effective fracture volume was used to predict the blast-resistant performance of the slab-column joint. Based on the trends identified in Figure 7, the correlation between the effective fracture volume per unit weight of TNT and the scaled distance was derived. As shown in Figure 8, regardless of the total amount of TNT applied, the effective fracture volume per unit weight of TNT showed almost similar value at any scaled distance and showed a certain tendency to decrease with increasing scaled distance. The trend line equation is shown in Figure 8, and the coefficient of determination (R\({}^{2}\)) of the equation for the entire data was 0.870.  
As a result, Equation (2) was proposed to predict the damage level of the slab-column joint subjected to blast load through the weight of the explosive material and the standoff distance.  
\[\text{Effective fracture volume}=\text{W}\times(0.2375\times 10^{-5.1374\text{Z}}) \tag{2}\]  
where Effective fracture volume: Effective fracture volume percentage of slab-column joint (%). W: TNT weight (kg). Z: scaled distance (m/kg\({}^{1/3}\)).  
Figure 7: Effective fracture volume.
# Motor Outcome Measures in Pediatric Patients with Congenital Muscular Dystrophies: A Scoping Review
## 1 Introduction  
Congenital muscular dystrophies are a heterogeneous group of rare neuromuscular disorders. The overall prevalence of CMD is estimated at between 0.6-0.9 cases per 100,000 births. By definition, onset is usually at birth or in early childhood [1]. Suspicion of CMD in later life may be due to the presence of mild or previously overlooked symptoms [2].  
Common symptoms of the various forms of CMD are early muscle weakness, hypotonia, and joint contractures. These features may be more or less prominent. The typical floppy infant presentation may be observed in severe cases; in milder cases, antigravity movements of the limbs may be preserved, with greater impairment at the axial level. Joint contractures may sometimes be associated with ligamentous laxity and may be more or less diffuse.  
In the advanced stages of the disease, cardiac and respiratory failure may occur. Brain involvement may also be present, usually manifesting as an intellectual disability [2].  
Differential diagnosis in children with suspected congenital muscular dystrophy or congenital myopathy is determined by clinical features, age of onset, and inheritance pattern [3].  
Congenital muscular dystrophies are characterized by dystrophic components on muscle biopsy [4]. However, heterogeneity in findings at biopsy has blurred the distinctions between disorders once thought to be distinct.  
Today, with improved sequencing technology, genetic testing is the gold standard, as it speeds up the diagnostic process and reduces costs [3]. Thirty-seven genes are currently associated with congenital muscular dystrophy phenotypes. The most common types are collagen VI-related disorders (12-19%), dystoglycanopathies (12-25%), laminin-alpha2-related dystrophies (10-37%), and selenoprotein N (11.65%). Fukuyama CMD is the most prevalent form of CMD in Japan [5].  
Although advances in research have improved the understanding of the disease mechanisms, there is still no commonly accepted approach to the treatment and care of CMD patients. The causes could be attributed to the rarity of the diseases and the great variability of clinical phenotypes.  
In this scoping review, the current knowledge of the natural history and trends of motor function in pediatric patients with congenital muscular dystrophy is collected and summarized. In addition, the assessment tools used to describe motor function according to the form or stage of the disease are also described.  
Collecting and sharing data on disease trajectories using longitudinal studies could add significant information to what is currently available. Therefore, we aim to make the scientific community aware of the lack of data and the future efforts needed to increase evidence, also considering possible innovative drug treatments.
## Appendix A Kriging Interpolation
The comprehensive network that measures temperature is made up of 77 stations and it was used in the study to interpolate temperature data via regression kriging by cross-validation and to compute the reference evapotranspiration at the 18 AWSs. In order to evaluate the sensitivity of the kriging estimator to the network density, the performances of the kriging estimator at the 18 AWSs was evaluated at the 18 AWS sites by using alternatively the whole set of 77 stations and the 18 AWSs. The results in terms of BIAS and RMSE obtained by cross-validation are shown in Figure 11.  
Figure 11 shows boxplots of BIAS and RMSE of the temperature estimates at each of the 18 AWSs obtained with kriging estimates recursively conditioned to the air temperature observed at the residual 76 stations and 17 stations, respectively, according to the cross-validation procedure. In the former case, the mean BIAS is 0.37 \({}^{\circ}\)C, ranging from \(-\)0.80 \({}^{\circ}\)C to 1.64 \({}^{\circ}\)C; the RMSE is on average 1.11 \({}^{\circ}\)C, varying from 0.81 to 1.98. In the latter case, the mean BIAS is very close to zero as expected, ranging from \(-\)1.61 \({}^{\circ}\)C to 1.32 \({}^{\circ}\)C and the RMSE ranges from 0.46 \({}^{\circ}\)C to 1.96 \({}^{\circ}\)C with a mean value of 1.15 \({}^{\circ}\)C. The results show a low sensitivity of the kriging interpolator of temperature to network density.
## 3 Results
### Scoliosis Prediction and Local Interpretation  
Fifth, scoliosis prediction results and feature importance for each participant are indicated in Figure 6. Participant #120 (female of 25 years old) was diagnosed with lumbar scoliosis with a Cobb angle of 11\({}^{\circ}\) on radiographic assessment. For this participant, the predictive probability of scoliosis was 94%, and PHD, wrist height difference, and knee height difference (green bars: positive effect) analyzed with high height differences were examined as important factors that predominantly increased the probability of scoliosis (Figure 6A). Conversely, in a 20-year-old woman with a Cobb angle of 0\({}^{\circ}\) and healthy spine curve, the predictive probability of scoliosis was 9%, and PHD and wrist difference of low height (red bars: negative effect) were analyzed as important factors that contributed to lowering the probability of occurrence of scoliosis (Figure 6B).  
Conversely, the distribution of predictions using detailed numerical values, in which PHD was one of the dominant parameters that affected scoliosis, was analyzed (Figure 7). If the PHD is <2 mm, the prediction probability is small at 24.6%. However, if the PHD is >3 mm, the probability of diagnosing scoliosis sharply increases to 85.3%.  
Finally, mutual information (MI) metrics were evaluated in Table 4. MI is a measure of the similarity between two features in the same dataset. In Table 4, PHD (0.22) and knee height difference (0.09) are the most dependent factors, but wrist height difference (0.00) and ankle height difference (0.00) are independent factors. This means that PHD and knee height difference are related to the lower extremities in body parts. However, age and weight factors are less dependent. They relate to the output of spinal deformities in the same dataset.  
Figure 5: Feature analysis for the parameters using global interpretation. (**A**) Shapley additive explanation values for dominant parameters in the order of importance for all 140 participants. (**B**) Specific case analysis for the parameter of the 15 th participant among 140 participants.  
Figure 6: Scoliosis prediction for specific cases using local interpretation. (**A**) A 25-year-old woman with 94% prediction probability and ground truth of 1 (Cobb angle 11\({}^{\circ}\)). (**B**) A 20-year-old woman with 9% prediction probability and ground truth of 0 (Cobb angle 0\({}^{\circ}\)).  
Figure 7: Scoliosis prediction probability for one of the major parameters, i.e., PHD. A threshold value of 3 mm of PHD results in \(>\)85% probability for scoliosis prediction.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 5 Results and Discussions
### The Outcomes of the DL Scenario
The outcomes are superior to those of the best U2 users in [38], with an improvement rate of \(10^{-2.3}\) in the BER. Figure 6: BER against transmitting power for four users with varying distances and power coefficients for DL NOMA at BW 200 MHz with 64 \(\times\) 64 MIMO for DL NOMA. Figure 7: SE vs. transmit power for four users with varying distances, with power coefficients and BW 80 MHz for DL NOMA. The performance of the DL NOMA SE in terms of transmitted power at 80 MHz BW and 64 \(\times\) 64 MIMO is shown in Figure 9. At the transmitting power of 5 dBm, the SE for all users is relatively close. Figure 10 depicts the DL NOMA SE performance versus transmit power at 200 MHz BW and 64 \(\times\) 64 MIMO. At a transmitter power of 10 dBm, the SE for all users is relatively close. The MIMO improved SE performance. Figure 8: SE against transmitting power for four users with varying distances, with power coefficients and BW 200 MHz for DL NOMA. Figure 9: SE vs. transmitting power for four users with varying distances, with power coefficients, with the 64 \(\times\) 64 MIMO and BW 80 MHz for DL NOMA.
## 6 Experimental Results and Discussion
### 1-D CNN Comparisons of Single-Point and Multi-Point Model  
The test set was used to test the multi-point and single-point models of 1-D CNN under different noise intensities and the comparison results are shown in Table 4. It can be seen from Table 5 that the accuracy of the 1-D CNN multi-point model on the test set was reduced, especially when the SNR was reduced to below 20 dB, and the model accuracy was significantly reduced.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Power Spectrum** & **Power Spectrum** & **Wavelet Packet** & **1D CNN** \\  & **Energy (SVM)** & **Energy (BP)** & **Energy (BP)** & **Single-Point** \\ \hline Accuracy & 69.08\% & 60.53\% & 93.75\% & 100\% \\ Error task & 47 & 60 & 10 & 0 \\ \hline \end{tabular}
\end{table}
Table 4: 2-D CNN multi-measurement point reciprocating compressor gas valve fault diagnosis model structure.  
Figure 12: Key phase pulse signal.  
Figure 13: Acceleration waveform of the vibration signal.  
The test sets were extracted with different SNR at the output of the dense layer 1 and principal component analysis (PCA) was performed on them to obtain Figure 14. It can be seen that when the SNR is 5 dB, the output of the valve rupture and spring damage faults at the fully connected layer 1 are too close. Consequently, they overlap widely after dimensionality reduction. In addition, the failure data of the spring damage also overlaps with the gap data of the valve plate to a small extent, resulting in a decrease in the accuracy of the model. More spring damage fault data were collected to better extract the characteristics of this type of fault, which can improve model accuracy. When the SNR was 10 db, only a small number of samples were close to the output of the dense layer 1, but the model accuracy was still only 55.59%. Thus, it is judged that the output layer may not be well classified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SNR**} & \multicolumn{3}{c}{**Failure Recognition Rate and Error Task**} \\ \cline{2-4}  & **1-D CNN Multi-Point Model** & **1-D CNN Single-Point Model** & **2-D CNN Multi-Point Model** \\ \hline
5 dB & 47.32 \%/1101 & 58.75\%/881 & 49.67\%/1052 \\
10 dB & 55.59\%/928 & 85.58\%/308 & 99.09\%/19 \\
20 dB & 99.43\%/12 & 100.00\%/2 & 99.19\%/17 \\
50 dB & 99.52\%/10 & 100.00\%/1 & 99.19\%/17 \\ No noise & 99.43\%/12 & 100.00\%/2 & 99.19\%/17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the diagnostic effects of different models on the test set.  
Figure 14: PCA of the output of the dense 1 of the 1-D CNN multi-point model of the test set samples under different noise strengths. (**a**) SNR is 5 dB; (**b**) SNR is 10 dB; (**c**) SNR is 20 dB; (**d**) SNR is 50 dB.
## 2 Multi-UAV Cooperative Persistent Monitoring Path Planning Model
### Min-Max Optimization for Multi-UAV Cooperative Monitoring
#### 2.6.3 Multi-UAV Persistent Monitoring Path Planning Model  
The optimization problem of multi-UAV cooperative persistent monitoring path planning is expressed as follows:  
\[\begin{array}{ll}\underset{Y,F}{\text{min}}&J=wJ_{1}+(1-w)J_{2}\\ \text{s.t.}&(1)-(8)\end{array} \tag{16}\]  
where \(w\in(0,1)\) represents the weight coefficient, which will balance between the performance of overdue time and path privacy.
# A 1 \(\times\) 2 Two-Dimensional Slanted Grating Based on Double-Layer Cylindrical Structure
## 3 Characters and Tolerance Analysis
Our grating period has a 53 nm  
Figure 6: Grating diffraction efficiency versus thickness h1 of MgF\({}_{2}\) and thickness h\({}_{2}\) of SiO\({}_{2}\); (**a**) (\(-1\), \(0\)) order diffraction efficiency under TE polarization; (**b**) (\(0\), \(-1\)) order diffraction efficiency under TE polarization; (**c**) (\(-1\), \(0\)) order diffraction efficiency under TM polarization; (**d**) (\(0\), \(-1\)) order diffraction efficiency under TM polarization. fabrication tolerance, and the slanted angle has a 8.8-degree fabrication tolerance. This is a positive result. We have a relatively large range of manipulation in the manufacture process. Due to the characteristics of the existing fabrication technique, it is more difficult to control the duty cycle than the period or the height of the grating in the process of fabricating the grating. Therefore, it is necessary to calculate the production tolerance for the duty cycle and discuss the deviation of the duty cycle from the designed optimal values. In Figure 8, we show a tolerance calculation for the duty cycle, such that Figure 8a is for TE polarization incidence, and Figure 8b is for TM polarization incidence. In the region of interest, the (\(-1\), \(0\)) order diffraction efficiency and the (\(0\), \(-1\)) order diffraction efficiency both exceed 35% when the duty cycle is in the range of 0.46 to 0.53. In this range, the grating ridge width range b is 277.38 nm to 319.59 nm when the grating period d is equal to the optimal value of 603 nm. The tolerance range of the ridge width of the grating is 42.21 nm. With the grating ridge width b in this range, the sum of the grating (\(-1\), \(0\)) order diffraction efficiency and (\(0\), \(-1\)) order diffraction efficiency will be greater than 70%. Moreover, we can see that the diffraction efficiency of the (\(-1\), \(0\)) and (\(0\), \(-1\)) orders decreases rapidly when the duty cycle is greater than 0.53, regardless of whether the incident wave is in the TE or TM polarization state. Figure 7: Grating diffraction efficiency versus grating period d and slanted angle \(\alpha\); (**a**) (\(-1\), \(0\)) order diffraction efficiency under TE polarization; (**b**) (\(0\), \(-1\)) order diffraction efficiency under TE polarization; (**c**) (\(-1\), \(0\)) order diffraction efficiency under TM polarization; (**d**) (\(0\), \(-1\)) order diffraction efficiency under TM polarization. According to our knowledge, this paper is the first to report a two-dimensional grating based on a double-layer cylindrical structure. The high diffraction efficiency and low PDL can improve the SNR. In consideration of the broadband for application and great tolerances for fabrication, the proposed 2D slanted gratings might be highly interesting for fabrication of high-quality 2D grating. Furthermore, the fabricated 2D slanted gratings might provide the highest efficiency with polarization independence, which implied the advanced revolutionary potential for AR/VR technology.
## 3 Result and Discussion
### Performance Evaluation of The PAN Nanofiber Air Filters  
Figure 5 shows a schematic diagram of the PAN nanofiber air filter efficiency and pressure drop performance evaluation apparatus. The filtration efficiency and pressure drop performance evaluation device were configured with the particle generation portion, the tester filter holder section, and detector. In this study, the performances of nanofiber air filter A and B were tested using the TSI-Fractional 3160 equipment (TSI Inc., Shoreview, MN, USA) at the Korean Institute of Industrial Technology (KITECH). The test conditions were controlled at a temperature of 25 \({}^{\circ}\)C, humidity of 40%, flow rate of 32 L/min, face velocity of 5.3 cm/s, and the test particle size range was 0.1-0.6 \(\upmu\)m. With a diameter of 15 cm, PAN nanofiber air filters A and B were placed, respectively, at the test filter holder section. The particle counter and the micro-manometer, which are installed upstream and downstream, respectively, were measured the filtration efficiency and pressure drop. The test method was carried out in accordance with British standards and the European standard (BS EN 1822-3), and performance test conditions are shown in Table 4.  
Figure 6 shows the filtration efficiency and pressure drop of PAN nanofiber air filter A and B. The manufactured PAN nanofiber air filter A had a measured filtration efficiency of 91 \(\pm\) 0.1% and pressure drop of 22 \(\pm\) 0.1 mmHg2O for 0.3 \(\upmu\)m particle diameter size. The manufactured PAN nanofiber air filter B had a measured filtration efficiency of 95 \(\pm\) 0.1% and pressure drop of 25 \(\pm\) 0.1 mmHg2O for 0.3 \(\upmu\)m particle diameter size. The PAN nanofiber air filter B increased the pressure drop, and it had structural stability and a high filtration efficiency. Additionally, it satisfied the criteria of high efficiency air filters EPA E11 (collection efficiency of 95% or more, transmittance less than 5%) based on the performance evaluation of the British standards and European Standard BS EN 1822-3 [30].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Temperature (\({}^{\circ}\)C) & 25 \\ Humidity (\%) & 40 \\ Filter size (cm\({}^{2}\)) & 15 \\ Particle diameter (\(\upmu\)m) & 0.1–0.6 \\ Flow rate (l/min) & 32 \\ Face velocity (cm/s) & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental conditions to evaluate PAN nanofiber air filter A, B performance by TSI-3160.  
Figure 5: Schematic diagram for the performance evaluation of the PAN nanofiber air filters.
###### Abstract  
A large number of studies have described a positive relationship between physical exercise and cognition. Physical exercise can be divided into closed-skill exercise (CSE) and open-skill exercise (OSE) based on the predictability of the performance environment. It remains unknown whether either of these types of exercise is more beneficial for cognitive function. Therefore, the purpose of this meta-analysis was to evaluate the effect of OSE versus CSE on cognition. Eligible studies included cross-sectional studies and intervention studies that had a clear definition of OSE and CSE, and these were used to compare the cognitive performance differences between the two classes of exercise. A total of 15 cross-sectional studies and 4 intervention studies were included in this meta-analysis. Among the cross-sectional studies, the overall effect size for OSE versus CSE was 0.304 (95% confidence interval (CI) (\(-\)0.097, 1.213); \(p<0.05\)). Further subgroup analysis showed that the overall effect size for OSE versus CSE was 0.247 for inhibition and 0.360 for cognitive flexibility (both \(p<0.05\)). In contrast, no significant differences between the two exercise modes were observed in the intervention studies. In particular, there were no significant differences in visuospatial attention or in processing speed between the two exercise modes. Taken together, these results suggest that OSE is superior to CSE, especially for executive function, according to the 15 cross-sectional studies examined. However, data from the intervention studies indicate that OSE is not superior. Therefore, additional well-designed, long-term intervention studies are needed to elucidate the potential efficacy of OSE in all populations.  
exercise modes; open-skill exercise; closed-skill exercise; cognitive function; executive function +
Footnote †: journal: Applied Sciences
## 4 Methods and Materials
### Proposed LoRa Architecture  
A cloud-based agriculture field-monitoring scheme was implemented to precisely monitor the temperature, humidity, and other required parameters to operate end devices in an agriculture field. This scheme facilitates the conservation of water and energy in a field. Here, an open agriculture field was studied. Figure 6 shows the architecture of the LoRa network. The proposed protocol can be adapted to a greenhouse, hydroponic agriculture, and vertical farming. Devices provide numerous data on agriculture parameters from the field, and analysis of the data allows the farmer to work more effectively. A LoRa-enabled sensor network was deployed in the agriculture field. A LoRa-enabled architecture for precise irrigation and monitoring has been demonstrated in which LoRa enabled each sensor node and could establish a communication link with another LoRa-enabled receiver. LoRa is a low-power operating technique that enables transmission and reception with a wide range of communication. Although many other protocols exist, they have significant limitations compared to LoRa.  
Figure 5: Circuit diagram of the sensor node.