esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Challenges
#### 4.2.5 Legal Challenges  
Out of 58 studies, 9 scrutinized legitimacy challenges of digital payment technologies. There are challenges in legitimacy concerns, mostly cryptocurrencies, such as Bitcoin, that run on blockchain technology. Several governments all over the world view future development of cryptocurrencies, such as Bitcoin, as likely to be limited [82]. Studies provide suggestions for investors to consider the price trend, security risks and legal policies concerning Bitcoin when investing [82]. It was further confirmed that Bitcoin investment can be improved by legislation and the value of Bitcoin can be recognized and can become a legal investment only if it is supported by legislation.  
Likewise, studies have also explored the phenomenon of cryptocurrencies, such as Bitcoin and Digicash, to examine their potential vulnerabilities [49]. They focused particularly on the in-depth analysis of what challenges these cryptocurrencies may pose to the global anti-money-laundering and financing of terrorism. Their research shows that anonymity is the most criticized characteristic of cryptocurrencies, which is likely to make it unpopular among consumers. Despite 'anonymity' being an important feature for privacy, it brings numerous risks to the customer, for instance, if the user is unable to keep the right of property in case of loss or theft. The study highlights the challenges of understanding the nature of cryptocurrencies to assess the potential risks of digital payment systems being abused by criminals [49].
# Chemical Migration from Wine Contact Materials
## 7 Discussion on the Findings of Wine Migration and Wine Analysis Studies  
The migration studies that have been implemented to determine industrial chemical contaminants migrating from wine contact materials are limited to the investigation of cork stoppers and other types of closures. The identified migrating compounds include (A) intentionally added substances that are authorized for use in the manufacture of plastics according to Reg EU 10/2011, (B) intentionally added substances that are not authorized according to Reg EU 10/2011, and (C) non-intentionally added substances as presented in Figure 2. The corresponding concentration levels are reported in Table 2.  
The studies that involved wine analysis aiming at the determination of known migrating compounds investigated samples packaged in PET bottles with and without aluminum cups for the determination of the monomer BPA [35], as well as wine samples in polycoupled carton packaging for the determination of ink photoinitiators [80]. The determination of phthalate plasticizers was investigated in a big sample of market-ready French products [78] and in table wines processed in stainless steel tanks and matured in oak casks [79]. Finally, another study was conducted for the determination of phthalate, DEHA, alkylphenols, and BPA in wine samples in aseptic plastic laminate paperboard cartons (tetrapacks) [77]. Among the determined analytes, DBP (2.212 mg/kg), DEHP (1.1317 mg/kg), BPA (30 ng/mL), and benzophenone (217 mg/L) presented the maximum concentrations, while for DBP, the maximum quantified concentration (2.212 mg/kg) exceeded the SML of the regulation (0.3 mg/kg).
## 3 Methodology
### Stereo Reconstruction  
Once we have the system calibrated, the next step is to acquire images and reconstruct a scene using the following procedure (see Figure 4):  
1. Capture one image of the environment from each camera of the calibrated system.
2. Transform the catadioptric images to panoramic images using Algorithm 1.
3. Extract the features and descriptors from the panoramic images using a feature point detector such as SIFT, SURF, KAZE, a corner detector such as Harris, or more advanced feature detectors such as DeepMatching [40], CPM [45], or SuperGlue [39]. Match the points between the upper and lower camera features as described in Section 3.3.1.
4. Filter the wrong matches using epipolar constraints as described in Section 3.2.
5. Map the matching points coordinates from panoramic back to catadioptric images coordinates, using Algorithm 2.
6. Transform the catadioptric image points to the corresponding mirror PMU and PML.
7. Obtain the 3D reconstruction by triangulating the mirror points points using Equation (15) describe in Section 3.3.2.  
```
Input :\(img\_cat\), catadioptric image \(x_{c},y_{c}\), optical center Output :\(img\_pan\), panoramic image \(LUT_{x}\), Look up table X-coordinates \(LUT_{y}\), Look up table Y-coordinates Procedure ; \(h,w\gets size(img\_cat)\); radius \(\gets h/2\); angle \(\gets w/2\); \(r\gets 0\)to radius; \(a\gets 0\)to \(2\pi-\frac{2\pi}{angle}\); \(Y\leftarrow sin(a)\); \(X\leftarrow cos(a)\); \(LUT_{x}\gets round(kron(r,X)+x_{c})\); \(LUT_{y}\gets round(kron(r,Y)+y_{c})\); img\_pan \(\leftarrow interpolate(img\_cat,LUT_{x},LUT_{y})\);
```  
**Algorithm 1** Transforming catadioptric images to panoramic images  
```
Input :\(pan\_points\), points in the panoramic image \(LUT_{x}\), Look up table X-coordinates \(LUT_{y}\), Look up table Y-coordinates Output :\(cat\_points\), points in the catadioptric image Procedure ; \(cat\_points\leftarrow[\quad]\); \(num\_points\leftarrow size(pan\_points)\); for\(i\gets 1\)to\(num\_points\)do \(Pt_{xy}\gets round(pan\_points(i))\); \(x\leftarrow LUT_{x}[Pt_{xy}]\); \(y\gets LUT_{y}[Pt_{xy}]\); end for \(cat\_points\gets concatenate(cat\_points,[x,y])\);
```  
**Algorithm 2**Mapping between catadioptric to panoramic images  
Figure 4: Panoramic 3D reconstruction (best seen in color). The procedure is the following: (1) Capture images from the catadioptric camera system, (2) unwrap the omnidirectional images to obtain panoramic images (3) to search for feature matches and (4) filter the matches using epipolar constraints. (5) Convert features back to catadioptric image coordinates and (6) to mirror coordinates. (7) Perform 3D reconstruction.
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
## 1 Introduction
However, for this to happen, waveguides should be fabricated perfectly rectangular (without any sidewall angle). All the fabrication processes introduce some sidewall angles causing waveguides to be inherently asymmetric even with top SiO\({}_{2}\) cladding. As reported in Ref. [29], the sidewall angle should be less than 2\({}^{0}\) to suppress mode hybridization and avoid undesired mode conversion. However, it is very challenging to eliminate the sidewall angle, and a typical value in most of the fabrication processes is around 8\({}^{0}\)[29; 30; 31]. The other popular waveguide, particularly for silicon-based integrated optoelectronics is SOI ridge waveguides [32; 33; 34; 35]. The ridge waveguide structure is inherently asymmetric in the vertical direction, even when considering SiO\({}_{2}\) upper cladding (without angled sidewalls). Consequently, strong mode conversion is observed between TM\({}_{0}\) mode and higher-order TE polarization in regular adiabatic and bi-level tapers [9]. However, in previous reports, no emphasis was given to various process parameters since strong mode hybridization was easily achieved in an SOI platform with relatively thin silicon thickness. A taper length of \(<\)200 microns was sufficient for mode conversion [9; 11; 12; 17; 19; 24]. Note that, due to strong mode hybridization in ridge waveguides, it is also difficult to depress mode conversion in ultra-short low loss tapers in TM-type PICs. Figure 1a,b show the cross-section of the ideal rectangular waveguide and waveguide with angled sidewalls, respectively. Even though the SOI platform with 220 nm silicon thickness is very popular, the SOI platform with thicker silicon (400-500 nm) is attractive to achieve efficient coupling between the III-V laser and silicon waveguide [36]. Additionally, various high-performance passive components, such as optical spiral delay lines, ring resonators, and Bragg grating, were also reported with 500 nm thick silicon [37]. For the first time, this paper studies the mode conversion in submicron 500 nm SOI for different waveguide ridge heights and upper claddings. The study paves the way for realizing compact PSRs and robust TM-type circuits. Additionally, it shows that, without optimization, adiabatic tapers for mode conversion would be at least ten times longer (1000s of microns) compared to relatively thinner SOI platforms. To reduce this taper length, we extend the work performed on angled sidewallsin strip waveguides [29] to the ridge waveguides. By selecting an optimum etch-depth and considering asymmetries caused by angled sidewalls and cladding, we demonstrated an 84.7% reduction in the overall taper length needed for polarization mode converters. It is also shown that ultra-short adiabatic tapers with 97% TM preserving efficiency can be achieved. These results illustrate the importance of accounting for the process parameters while designing TM preserving and mode evolution tapers.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 6 Conclusions
Also, the combined orbit determination using the space geodesy technologies of GNSS, SLR, as well as VLBI and DORIS, are bound to be the development trends of space technology in the future. **Funding:** This study is financially supported by the National Natural Science Foundation of China (Grant No. 41874032, 41731069 and 41574013) and the National Key Research & Development Program of China (2016 YFB0501701). **Acknowledgments:** Thanks are due to ILRS, CDDIS, EDC, IGS ACs, and WUM for providing the SLR data and orbits of BeiDou Navigation Satellite System. The authors are thankful to Dr [PERSON], from group of Astronomy and Geomatics (gAGE), Universitat Politecnica de Catalunya (UPC) at Barcelona, Spain, who revised the manuscript. Finally, the authors are also grateful for the comments and remarks of three reviewers, which helped improve the manuscript significantly. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Response Fusion Types and Loss Functions  
The next experiment aims to determine the optimal combinations of the original image (I) with the maximum (\(R_{\text{Max}}\)) and mean (\(R_{\text{Mean}}\)) responses. The Gabor scale (\(\lambda\)) of 4 pixels was specified. Figure 4A presents the comparison of their accuracies, using the modified ResNet-50, at different fusion weights (\(\alpha\)). It was found that the combination with \(R_{\text{Mean}}\) consistently outperformed its counterpart. Therefore, it was used in the subsequent experiments. Similarly, the appropriate loss function was determined. Figure 4B compares the accuracies using Categorical Cross-Entropy (CCE) and Binary Cross-Entropy (BCE) at varying weights (\(\alpha\)).  
Figure 3: The impact of the learning rate (LR) on model performance, showing the accuracy at varying RL (**A**) and the convergence behaviors with LR = 1.0 \(\times\) 10\({}^{-4}\) (**B**) and 5.0 \(\times\) 10\({}^{-6}\) (**C**).
## 3 Results
### Accuracy Evaluation of Modified Model Based on PWSI  
The performance of the modified LUE model was evaluated using data from June-September 2020 and May-June 2021. On sunny days, after using the PWSI to modify the water stress scalar in the original model, the performance of the seven LUE models was significantly improved, with RMSE and IA ranging from 1.1084 to 1.7488 g C m\({}^{-2}\) day\({}^{-1}\) and 0.7510 to 0.8816, respectively, and there was less scatter in the figure. All models explained 49-65% of the daily GPP variation in the cork oak plantation. This shows that on sunny days, the PWSI can better indicate the limiting effect of water stress on plant photosynthesis than traditional drought indicators. Except for the MOD17, EC-LUE, and Wang models, the fitting curve of the simulated values and measured values for the other models after modification by the PWSI was very close to the 1:1 line (Figures 7 and A2). For the MOD17 and EC-LUE model structures, because only one stress factor was considered to affect the actual LUE, the simulated values of the two models were still higher than the measured values even when PWSI was modified (Figures 7a,e and A2). However, because of many limiting factors in the model structure, the simulated value of the modified Wang model was lower than the measured value (Figures 7m and A2). In addition, the modified ECM-LUE model performed better than the modified EC-LUE model Figures 7e,g and A2). Among the seven models, the model structures with the best performance were the modified ECM-LUE, SM-LUE, and GLO-PEM models based on PWSI (the three models had the same structure after modification), and the R\({}^{2}\), IA, and RMSE were 0.6511, 0.8816, 1.1084 g C m\({}^{-2}\) day\({}^{-1}\), respectively. This was also the optimal LUE model structure suitable for cork oak plantations on sunny days.  
On cloudy days, except for the SM-LUE and Wang models, the performance of the models decreased after replacing the water stress scalar in the original model with PWSI, and the explanatory powers of all models for daily GPP variations was only 38-40% (Figure 7). Therefore, the unmodified ECM-LUE model has an optimal structure suitable for cork oak plantations on cloudy days.  
In conclusion, the optimal LUE model structure suitable for estimating the \(GPP\) of cork oak plantations in northern China can be defined as follows:  
\[GPP=\left\{\begin{array}{ll}PAR\times FPAR_{NDVI}\times LUE_{max}\times W _{S\_PWSI}\times T_{\_EC-LUE},&(CI>0.5,LUE_{max}=1.2367)\\ PAR\times FPAR_{NDVI}\times LUE_{max}\times W_{S\_EF}\times T_{\_EC-LUE},&(CI<0.5,LUE_{max}=1.5056)\end{array}\right. \tag{16}\]
## 6 Discussion  
This study aimed to effectively classify normal, COVID-19, lung opacity, and viral pneumonia using CXR imaging. The COVID-19 CXR dataset has an imbalanced number of images for each disease, and the number of normal CXR images is greater than the number of disease CXR images. In order to improve the performance of the model, it is necessary to match each dataset ratio using data augmentation. The existing data augmentation such as image rotation does not reflect the shape of the actually photographed CXR images. In general, because CXR images are taken around the center of the body, 3D rotational augmentation is more effective than 2D image rotation. Therefore, the 3D rotational augmentation method using 3 DPI was proposed. Then, the two-step learning model was proposed to improve the model performance by first separating the normal cases, which constitute the highest amount of data in the X-ray images, from other disease cases. Figure 13 shows the confusion matrix of each method. Figure 13a,b shows the results of VGG16 (Imbalanced) and YOLOv4 (Imbalanced). The accuracy of VGG16 (Imbalanced) for normal, COVID-19, lung opacity, and viral pneumonia was 94.13, 75.69, 81.06, and 78.05%, and YOLOv4 was 98.81, 84.44, 82.31, and 82.00%. This result indicates that the performance of YOLOv4 is better than that of VGG16. In Figure 13c, the normal accuracy of the two-step learning model (Imbalanced) is 93.44%, which is lower than that of YOLOv4, but the accuracy of COVID-19, lung opacity, and viral pneumonia is 96.38, 85.63, 94.50%, which is higher than that of YOLOv4. This confirms that the three-class model classifies diseases well. The lowered accuracy of normal is predicted to occur due to data imbalance during training of the two-class model. Figure 13d represents the two-step learning model with 3D rotational augmentation (Balanced). The accuracy of the model for normal, COVID-19, lung opacity, and viral pneumonia is 97.38, 96.50, 91.75, and 100.00%, which is the best. This confirms that the proposed 3D rotational augmentation effectively improved the performance of the model. However, the two-step learning model has a problem that requires a lot of training time because two models need to be trained. In the final result compared to the imbalanced YOLOv4 model, additional research is needed on the fact that the accuracy of normal is lowered from 98.81% to 97.38%.  
Figure 13: Confusion matrices for each method. (**a**) VGG16 (Imbalanced); (**b**) YOLOv4 (Imbalanced); (**c**) Two-step learning model (Imbalanced); (**d**) Two-step learning model with 3D rotational augmentation (Balanced).  
The proposed method has a higher WR score than those of existing methods. This indicates that the proposed model has fewer FN cases. An FN case is a failure to identify an infected patient. A highly contagious disease such as COVID-19 can easily spread through a community due to misdiagnosis. Therefore, there is a need for a method to reduce FN cases. The proposed method will be helpful in clinical decisions because the proposed method has better performance than the existing method.
## 3 The Speed Test
### Test Specimens  
Four conical friction specimens with different configuration parameters are developed. As shown in Figure 5, the outer radius is \(R_{o}\); the inner radius \(R_{i}\); the thickness is b; the cone angle is \(\alpha\); and the cone height is h. Cone angle \(\alpha\) and cone height h are the main conical configuration parameters. The cone angle is mainly decided by the design torque. The distribution of temperature of the friction disk affected the friction coefficient, and a bigger value of the angle tends to reduce the effect of the temperature on the friction period.  
Radial grooves (Figure 6a) are intended to facilitate lubrication and help lower the temperature of the contact area. The cone height is mainly decided by the whole thickness of the friction disk. The height of the cone (\(h\)) is typically between a third and a half of the thickness of specimens (\(b\)).  
The configuration parameters of test specimens are listed in Table 2. The outer diameter and inner diameter are designed as constant. The cone angle \(\alpha\) and the cone height h of the conical structure are the main variables.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Name** & **Model/Type** & **Parameters** \\ \hline
1 & Motor & Y100L-2 three-phase asynchronous motor & Rated power: 3 kW \\  & & Rated speed: 2800 r/min \\ \hline
2 & Torque sensor & HK-710-3 dual-flange static torque sensor & Range: 0–300 Nm \\ \hline
3 & Speed sensor & CY12-02 PK speed Holzer sensor & Corresponding frequency: 20 kHz \\ \hline
4 & Temperature sensor & Pt100 temperature sensor & Temperature range: 0–400 \({}^{\circ}\)C \\ \hline
5 & Acquisition card & MCC 1608 GX-2 AO & 16-bit resolution, 500 kS/s sampling rate \\ \hline  & Loading device & Hydraulic Loading device & Range: 0–1000 N \\  & & Accuracy : \(\pm\)5 N \\ \hline
6 & oil & Gear Oil, ISO 220 & Lubricating oil density, \(\rho\) = 870 kg m\({}^{-3}\) \\  & & Lubricating oil viscosity, \(\mu\) = 0.0962 Pa\(\cdot\)s \\ \hline \end{tabular}
\end{table}
Table 1: Components of the experimental rig.  
Figure 5: Conical configuration parameters.  
Figure 6: Conical groove friction specimens (T3).  
Test specimens with cone angle of 45\({}^{\circ}\) and cone height of 3 mm can be seen in Figure 6. The material of friction disk is Cu15 Ni8 Sn-copper alloy. The separator disk is made of steel 30 Cr. The mean surface roughness Sa of friction disk and separator disk are 0.3 \(\pm\) 0.1 \(\mu\)m.  
The basic physical properties of the disks are shown in Table 3. Cu15 Ni8 Sn is widely used in ships to achieve excellent abrasion resistance capability. Its main element proportions and mechanical properties are shown in Table 4.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 3 State-of-the-Art
### Clustering Strategies
#### 3.2.2 Clustering Using Representation (CURE)  
CURE is a clustering algorithm that combines hierarchical clustering with sampling to deal with bigger datasets at smaller computation costs. [PERSON] et al. [63] presented the _CURE_ (clustering using representation) algorithm that can scale hierarchical clustering up to large datasets. The CURE algorithm can broadly be divided into two phases: initialization and completion. Initialization begins with taking a random sample without replacement from the original population and applying hierarchical clustering. Both the number of representative points and the shrinkage factor can be optimized as part of hyperparameter tuning. In the completion step, for each resulting cluster, CURE chooses a small set of representative points which are well scattered inside the cluster (so representing the shape of the cluster itself) and after shrinking toward the cluster centroid, a _KNN_ scheme is used to assign the cluster to all remaining objects by computing distances toward all those cluster representatives. Due to its robust approach, the CURE algorithm can even recognize arbitrary shaped geometries while being robust against outliers. Another important advantage of the algorithm is that it is linear in space complexity, of order \(O(n)\); however, the time complexity is still of order \(O(n^{2}log(n))\)[63]. The algorithm also does not need any strict assumption about the distribution of the data inside the clusters. However, in the context of big data [64], this method is still not sufficiently scalable. In this paper, a modification of CURE is proposed to increase the model capacity based on bootstrap strategies.
###### Abstract  
The purpose of this study was to determine whether a support vector machine (SVM) model based on quantitative susceptibility mapping (QSM) can be used to differentiate iron accumulation in the deep grey matter of early Parkinson's disease (PD) patients from healthy controls (HC) and Non-Motor Symptoms Scale (NMSS) scores in early PD patients. QSM values on magnetic resonance imaging (MRI) were obtained for 24 early PD patients and 27 age-matched HCs. The mean QSM values in deep grey matter areas were used to construct SVM and logistic regression (LR) models to differentiate between early PD patients and HCs. Additional SVM and LR models were constructed to differentiate between low and high NMSS scores groups. A paired t-test was used to assess the classification results. For the differentiation between early PD patients and HCs, SVM had an accuracy of 0.79 \(\pm\) 0.07, and LR had an accuracy of 0.73 \(\pm\) 0.03 (\(p\) = 0.027). SVM for NMSS classification had a fairly high accuracy of 0.79 \(\pm\) 0.03, while LR had 0.76 \(\pm\) 0.04. An SVM model based on QSM offers competitive accuracy for screening early PD patients and evaluates non-motor symptoms, which may offer clinicians the ability to assess the progression of motor symptoms in the patient population.  
Parkinson's disease; machine learning; support vector machine; quantitative susceptibility mapping; nonmotor symptom 202