esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 9 Discussion  
There are many deep learning methods which are applied to the analysis of medical videos. For echocardiography videos, deep learning is mainly used for LV segmentation, because it provides prognostic information and diagnostic clues. LV size helps cardiologists in diagnosing heart failure and LV hypertrophy. UNet is considered the top choice for segmentation in the medical field because CNNs impose some limitations. These limitations include the unavailability of a large number of samples and other problems, such as gradient exploding or gradient vanishing. UNet was introduced to cater to these drawbacks. UNet can be trained on a limited number of samples. Furthermore, UNet realizes image features with multiscale recognition and fusion. However, UNet does not consider the contribution of all semantic strengths during the segmentation process. To tackle this, combining UNet with FPN can improve segmentation accuracy. Deep learning is also applied to echocardiography videos to classify different views, as every standard view of the heart during echocardiography is crucial for other measurements; for example, the parasternal long-axis view is considered the most appropriate view to measure LV size. Most researchers have explored CNNs for view classification and reported greater than 90% accuracy overall. However, the performance of CNN models was not uniform for all personal views. To avoid this issue, segmentation can be used as a preprocessing step, although this additional step can prove to be resource- and time-consuming. For endoscopy videos, deep learning is usually applied to detect or classify polyps and lesions in different body organs. Some researchers have interpreted this as classifying informative (with polyps) and noninformative (without polyps) frames from videos. Researchers have widely explored transfer and machine learning models for endoscopy videos [83]. GoogleNet has achieved up to 100% accuracy in detecting ulcers from endoscopy videos. However, the runtime cost for GoogleNet can be as high as 37 min, which is very high compared with a CNN trained from scratch. Another approach that can improve the accuracy of the pretrained model is the fusion of handcrafted and CNN-extracted features. This approach is beneficial when data are limited and domain knowledge is available. However, this approach is not suitable for real-time analysis of the videos. For ultrasound videos, deep learning is usually applied for classifying or detecting vital fetal organs. To capture the temporal features, LSTM is widely used for all kinds of medical diagnostic videos, including ultrasound [59]. LSTM can be fused with a pretrained CNN model or a CNN model which is trained from scratch. The combination of ResNet and LSTM has produced promising results for detection and classification tasks. Researchers also explore 3D CNNs to capture the temporal features of all three kinds of videos. However, 3D CNNs require more extensive training time than 2D CNN models.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 4 Evaluation
### Effective Data Rate  
The data rate depends on the baud rate and the used coding. The effective rate \(R_{eff}\) is the ratio of the number of information bits to the total number of bits that need to be transmitted. Table 1 lists the implemented codes from Section 3.3 and their rates (\(R_{code}\)), the rate by the UART (\(R_{uart}\)) and the resulting effective rate. The effective rate is calculated by multiplying the following two rates: \(R_{eff}=R_{code}\cdot R_{uart}\). The rate from the UART is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coding** & **Coding Rate \(R_{code}\)** & **UART Rate \(R_{uart}\)** & **Effective Rate \(R_{eff}\)** \\ \hline No Coding & \(1\) & \(\frac{8}{10}\) & \(\frac{8}{10}=0.8\) \\ Reed-Solomon & \(\frac{223-1}{255+1}=\frac{222}{256}\) & \(\frac{8}{10}\) & \(\frac{222}{256}\cdot\frac{8}{10}\approx 0.694\) \\ Hamming & \(\frac{8}{3\cdot 7}=\frac{8}{21}\) & \(\frac{8}{9}\) & \(\frac{8}{21}\cdot\frac{8}{9}\approx 0.296\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Used codes and their rates.  
Figure 5: Setup of the evaluation.  
\(R_{uart}=\frac{8}{10}\) in the cases of _no coding_ and _Reed-Solomon coding_. For _Hamming coding_, the UART is configured to transmit only 7 data bits, which is why the rate for \(R_{uart}\) is \(\frac{7}{9}\) in this case.  
The data rate is finally calculated by multiplying the effective rate and the baud rate. In the following, only the data rate is considered.
## 1 Introduction
The principal idea behind the adjustment method proposed in this paper involves the transformation of a classical geodetic network (distances and horizontal and vertical angles) into a spatial linear (distance) network independent of the local reference system. The objective is to adjust a classical network and a GNSS vector network together in a common, geocentric \(XYZ\) coordinate system (referenced to the GRS'80 ellipsoid).
## 3 Indoor Experiment  
In this study, an accuracy comparison experiment was conducted on the three location estimation methods in an indoor laboratory. The indoor experiment was conducted five times repeatedly at an indoor laboratory simulating an underground mining environment. The raw data measured from the IMU, LiDAR and encoder sensors, and all \(x\), \(y\), \(z\) coordinates and directional angles calculated in real time while the robot was driving through the indoor laboratory, were recorded. The location estimation accuracy was analyzed by comparing the recorded actual location with the estimated location.
## 4 Conclusions  
The studies performed have shown that for the correct determination of the effective permittivity \(\epsilon_{\text{m}}\) of a metal surface via the proposed method of terahertz (THz) plasmon interferometric refractometry or by other methods based on an analysis of the characteristics of surface plasmon polaritons (SPPs), the measurements must be performed in the presence of a dielectric layer with a uniform thickness on the metal. The thickness of the dielectric coating should be large enough for the radiative losses because of the SPP scattering on the surface roughness and inhomogeneities to be significantly less than the Joule losses of the SPPs in the metal, although not large enough to make it impossible to measure the SPP decay with an acceptable accuracy. The choice of the optimal thickness of the dielectric layer depends on many factors [62]: the type of the metal, its state (surface of bulk or deposited metal), substrate roughness, optical homogeneity of the metal film, and the optical density of the dielectric. The proposed method of the plasmon refractometry of a conducting surface can be applied not only to metals but also to other materials used in THz plasmonics (doped semiconductors [31], graphene [63], dielectrics on absorption lines, etc.), which provide the macroscopic propagation length of the surface wave. Using the \(\epsilon_{\text{m}}\) value found via the method proposed will enable more adequate modeling of the operation of the elements and devices of THz plasmonics, plasmonic sensors, and communication lines.  
Conceptualization, A.K.N. and V.V.G.; methodology, V.V.G. and A.K.N.; software, V.V.G., A.K.N., and I.A.K.; formal analysis, V.V.G. and A.K.N.; investigation, V.V.G.; resources, A.G.L. and I.A.A.; data curation, V.V.G.; writing--original draft preparation, V.V.G. and A.K.N.; writing--review and editing, V.V.G. and A.K.N.; visualization, V.V.G.; supervision, V.V.G. and A.K.N.; project administration, V.V.G.; funding acquisition, V.V.G. and A.K.N. All authors have read and agreed to the published version of the manuscript.  
The work was done using equipment from the shared research center SSTRC on the basis of the Novosibirsk FEL at BINP SB RAS.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding authors.  
The authors thank the Novosibirsk free electron laser team for assistance in the experiments. The authors acknowledge the core facilities of \"VTM\" (Novosibirsk State University) for the access to its experimental equipment.  
The authors declare no conflict of interest.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space  
In light of the global power dynamics that were previously analyzed, it is possible to determine the current power structure of the space system. This structure is manifested as a result of the interaction between actors, which ends up shaping a power system or global order that characterizes the system as a whole and normally determines dynamic domination and subordination relations among actors. In this regard, it is critical to understand how this process of power rebalancing unfolds among the major actors in space and the global structures they establish as a consequence.
## 2 Methodology  
### Weighting Functions  
Given the size of the LEDs was only 7% of the imaging aperture diameter, they were treated as point sources in the present framework [9]. The source-aperture geometry permits 3 different scenarios: (i) sensing by a pair of apertures where the sensing paths cross each other somewhere along the path, (ii) sensing by a pair of apertures such that the sensing paths tend to cross behind the apertures, and (iii) sensing by a single aperture where the sensing paths converge at the aperture. These three geometries are shown in Figure 1. Consider the crossed-path geometry of Figure 1a. The two apertures, each of diameter \(D\) are located at position coordinates \(\mathbf{r}_{1}\) and \(\mathbf{r}_{2}\), respectively, with their center-to-center separation being \(\mathbf{s}=\mathbf{r}_{2}-\mathbf{r}_{1}\). The z-tilt over the 1 st aperture when viewing the 2 nd source in the direction \(\theta_{2}\) can be expressed as [10]. \[\alpha_{1}(\theta_{2})=\frac{32\lambda}{\pi^{2}D^{4}}\int d\mathbf{r}(\mathbf{ r}-\mathbf{r}_{1})W(\mathbf{r}-\mathbf{r}_{1})\phi(\mathbf{r},\theta_{2}), \tag{1}\]  
where \(\lambda\) is the wavelength, \(\phi(\mathbf{r},\theta_{2})\) is the turbulence induced wavefront distortion at aperture coordinate \(\mathbf{r}\) due to the 2 nd source and,  
\[W(\mathbf{r}-\mathbf{r}_{1})= \begin{cases}1&|\mathbf{r}-\mathbf{r}_{1}|\leq 0.5D \\ 0&|\mathbf{r}-\mathbf{r}_{1}|>0.5D.\end{cases}  \tag{2}\]Similarly, the z-tilt over the 2 nd aperture when viewing the 1 st source in the direction \(\theta_{1}\) can be expressed as:  
\[\alpha_{2}(\theta_{1})=\frac{32\lambda}{\pi^{2}D^{4}}\int d\mathbf{r}(\mathbf{r }-\mathbf{r}_{2})W(\mathbf{r}-\mathbf{r}_{2})\phi(\mathbf{r},\theta_{1}), \tag{3}\]  
where \(\phi(\mathbf{r},\theta_{1})\) is the turbulence induced wavefront distortion at aperture coordinate \(\mathbf{r}\) due to the 1 st source. Since the z-tilts are zero mean random processes, the differential tilt variance can be expressed as. \[ \begin{array}{ll}\left\langle[\alpha_{1}(\theta_{2})-\alpha_{2}(\theta_{1})] ^{2}\right\rangle&=\left\langle[\alpha_{1}(\theta_{2})-\alpha_{2}(\theta_{1}) ]\cdot[\alpha_{1}(\theta_{2})-\alpha_{2}(\theta_{1})]\right\rangle\\ &=\left(\frac{32\lambda}{\pi^{2}D^{4}}\right)^{2}\left\langle\int\int d \mathbf{r}d\mathbf{r}^{{}^{\prime}}[(\mathbf{r}-\mathbf{r}_{1})W(\mathbf{r}- \mathbf{r}_{1})\phi(\mathbf{r},\theta_{2})-(\mathbf{r}-\mathbf{r}_{2})W( \mathbf{r}-\mathbf{r}_{2})\phi(\mathbf{r},\theta_{1})]\right.\\ &\left.\cdot\left[\left(\mathbf{r}^{{}^{\prime}}-\mathbf{r}_{1}\right)W( \mathbf{r}^{{}^{\prime}}-\mathbf{r}_{1})\phi(\mathbf{r}^{{}^{\prime}},\theta_ {2})-\left(\mathbf{r}^{{}^{\prime}}-\mathbf{r}_{2}\right)W(\mathbf{r}^{{}^{ \prime}}-\mathbf{r}_{2})\phi(\mathbf{r}^{{}^{\prime}},\theta_{1})\right] \right\rangle,\end{array}  \tag{4}\]  
where the angled brackets indicate ensemble averaging. By interchanging the order of integration and ensemble averaging, Equation (4) can be rewritten as,  
\[
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 5 Review on ECS Methods on WBAN
### Harq
#### 5.2.2 Harq-Ii
However, the result does not take code rate into account, as the result is presented against \(Es/N_{0}\) instead of \(E_{b}/N_{0}\). Additionally, complex decoding algorithms are used in the proposed system without considering the decoding cost. Therefore, the claim of better performance is not fair and straightforward as mentioned in the paper. In [55], the authors extend their work [50; 56] in two aspects. Previous works are only based on simulation under AWGN channel. In [55], the authors presented a theoretical analysis based on bit error probability and event error probability of the proposed schemes in [50; 56] under AWGN and Rayleigh fading channels. The theoretical results indicated that both error probabilities for both channels as a function of code rate and \(E_{b}/N_{0}\) that is exactly we discussed in Equations (2) and (3). However, the simulation results of bit error probability and throughput is presented against Es/Nthat is against their theoretical findings where error probabilities for both channels are presented as a function of \(E_{b}/N_{0}\). Secondly, the decomposable code used in previous work is slightly modified to perform theoretical analysis. The concern of using \(Es/N_{0}\) instead of \(E_{b}/N_{0}\) in the simulation result is raised again and the use of complex decoder without considering the decoding cost in [51; 56] also applies in this paper. Therefore, the simulation result that is presented in [55] did not consider the code rate and decoding complexity, while comparing the results with the IEEE 802.15.6 approach.
## 5 Conclusions  
An in-situ method of measuring the viscosity of unstable and stable emulsions on a continuous basis under agitation conditions was developed. The rheology of oil-in-water emulsions stabilized with surfactant (Triton X-100) and starch nanoparticles was investigated using the in-situ technique. The stability characteristics of emulsions in terms of droplet size changes and phase separation with aging were also determined. Surfactant-stabilized emulsions were found to be more stable with respect to coalescence and phase separation. Pickering emulsions tended to separate readily into oil and water phases if left unagitated for a certain period of time. The droplet size of Pickering emulsions increased rapidly with aging. The droplet sizes of surfactant-stabilized emulsions hardly changed with aging up to seven days and no phase separation was observed in these emulsions. However, Pickering emulsions were found to be more viscous than the surfactant-stabilized emulsions despite having a much larger average droplet size compared with the surfactant-stabilized emulsions. The Sauter mean diameter of freshly prepared Pickering emulsions was about six to seven times that of the surfactant-stabilized emulsions. This observation is in contradiction with the existing literature, as fine small-sized emulsions are generally known to be more viscous than the coarse large-sized emulsions. The high viscosity of Pickering emulsions is explained in terms of the presence of a layer of nanoparticles at the surface of the droplets. The presence of nanoparticles at the surface of the droplets not only increased the rigidity of the droplets, but also increased the effective dispersed-phase concentration of the emulsion. Both surfactant-stabilized and Pickering emulsions were found to be generally Newtonian only at low values of the dispersed-phase concentration. They became non-Newtonian shear-thinning emulsions at high concentrations of the dispersed phase.  
Figure 11: Comparison of the relative viscosities of Pickering emulsions and surfactant-stabilized emulsions at a fixed rpm of 30 (shear rate of 36.78 s\({}^{-1}\)).  
**Author Contributions:** This project of in-situ measurement of the rheology and phase inversion of emulsions, with and without additives such as surface-active nanoparticles and surfactants, was conceived by [PERSON] The design and construction of the setup was done by both U.B. and R.P. The experimental work was carried out by U.B. The manuscript was written by [PERSON].  
**Funding:** The APC was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Rotating Cylinder on Mixing Performance in a Cylindrical Double-Ribbon Mixer
## 1 Introduction  
Uniform mixing is essential in various industries, including the chemical [1], pharmaceutical [2], cement [3], and food processing [4] industries. Homogeneity is the main requirement for the final products. To meet this requirement, the mixing performance in the mixing equipment must be investigated and improved. The mixing quality can be generally distinguished into three types--segregated, randomly mixed, and perfectly mixed conditions [5]. Use of a specific mixer is dependent on the type of materials being mixed. In the process industry, a wide range of mixers, including ribbon, tumbler, screw, polyshare, and convective mixers, are used in particle mixing [6; 7; 8; 9; 10; 11; 12]. Among them, cylindrical ribbon mixers are extensively used in various mixing industries and hence have attracted research attention to enhance their mixing performances. Recent studies also show the importance of using cylindrical helical ribbons for mixing viscous fluids [11]. Cylindrical ribbon mixers are further classified by their structures into horizontal [13] and vertical [14], and both types have been studied to determine the mixing times [15], mixing performances [16; 17], power requirements [18], and power consumption [15] during operation.  
Generally, whey protein is extracted from cow's milk, and the benefits of using it as a supplement were well-explained by [PERSON] et al. [19] and [PERSON] et al. [19; 20]. Whey protein contains all nine essential amino acids, known as branched-chain amino acids (BCAA) [21], which are capable of improving the immune system of asthma patients. Athletes across the globe consume whey protein as a supplement to improve their athletic stamina. Motivated by these widespread advantages, weexamined whey protein concentrates and numerically analyzed the mixing performance of a vertical cylindrical ribbon mixer during the mixing of commercially produced whey protein concentrates.  
Several parameters, such as the rotational speeds of the ribbon, cylinder (with and without a ribbon), and a combination (simultaneous cylinder and ribbon rotation), impact the mixing performance in a vertical cylindrical ribbon VCR mixer. These effects are difficult to analyze experimentally. Discrete element method (DEM) simulations [10; 22; 23; 24; 25] have been widely used to examine the effects of the operational parameters prior to actual experimentation. These simulations can be used to analyze the operating conditions in industrial mixing. Furthermore, simulations can be used to increase the manufacturing rate if the conditions are modeled appropriately. Hence, a novel approach has been proposed to optimize the mixing performance of VCR mixers by adopting an appropriate non-sampling mixing index and simulating the rotational movement of the cylinder to attain a uniform mixture in the smallest possible amount of time.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## Appendix A
### Proof of Theorem 2  
By plugging the GMC regularization (7) into \(F_{B}^{GMC}(\beta)\), we can rewrite \(F_{B}^{GMC}(\beta)\) as  
\[F_{B}^{GMC}(\beta) =\frac{1}{2n}\|y-X\beta\|_{2}^{2}+\lambda\|\beta\|_{1}-\lambda \min_{v}\bigg{\{}\|v\|_{1}+\frac{1}{2}\|B(\beta-v)\|_{2}^{2}\bigg{\}}\] \[=\max_{v}\bigg{\{}\frac{1}{2n}\|y-X\beta\|_{2}^{2}+\lambda\|\beta \|_{1}-\lambda\|v\|_{1}-\frac{\lambda}{2}\|B(\beta-v)\|_{2}^{2}\bigg{\}}\] \[=\max_{v}\bigg{\{}\frac{1}{2}\beta^{\top}\big{(}\frac{1}{n}X^{ \top}X-\lambda B^{\top}B\big{)}\beta+\lambda\|\beta\|_{1}+\frac{1}{2n}\|y\|_{ 2}^{2}-\frac{1}{n}y^{\top}X\beta\] \[-\|v\|_{1}-\frac{\lambda}{2}\|Bv\|_{2}^{2}+\lambda v^{\top}B^{\top }B\beta\bigg{\}}\] \[=\frac{1}{2}\beta^{\top}(\frac{1}{n}X^{\top}X-\lambda B^{\top}B) \beta+\lambda\|\beta\|_{1}+\frac{1}{2n}\|y\|_{2}^{2}-\frac{1}{n}y^{\top}X\beta\] \[+\lambda\max_{v}\bigg{\{}-\|v\|_{1}-\frac{\lambda}{2}\|Bv\|_{2}^{ 2}+\lambda v^{\top}B^{\top}B\beta\bigg{\}}. \tag{116}\]  
The expression enclosed in the curly braces in (116) is affine with respect to \(\beta\) (hence convex). Consequently, the final term in the equation is also convex, as it represents the pointwise maximum of a collection of convex functions. Therefore, \(F(\beta)\) is convex if \(\frac{1}{n}X^{\top}X-\lambda B^{\top}B\succeq 0\), and if \(\frac{1}{n}X^{\top}X-\lambda B^{\top}B\succ 0\), then \(F_{B}^{GMC}(\beta)\) demonstrates strict convexity.
## 6 Picking Test and Result Analysis  
### Identification and Positioning  
The YOLOv5 algorithm, improved by incorporating CBAM and BiFPN structures, was adopted, which utilizes the Realsense D415 depth camera for the identification, localization, and detection of crabapples from different angles, fruit quantities, and cluster quantities on trees. The three-dimensional spatial coordinates of crabapple picking points were outputted and saved, as illustrated in Figure 14b for the random placement of five clustered crabapples on a tree, and Figure 14d for the identification and localization of three clustered crabapples at random positions on a tree. The corresponding output coordinates are listed in Table 5. Furthermore, the actual distance from the camera to the fruit was measured using a measuring tool for error analysis.  
Comparing the spatial coordinates of crabapples obtained by the D415 camera with the actual measured positions, it was found that there was a certain error between the coordinates obtained by the D415 camera and the actual measurement results, as shown in Table 7. Specifically, the average error in the \(X\)-axis direction is 3.25 mm, 3.375 mm in the \(Y\)-axis direction, and 2.875 mm in the \(Z\)-axis direction. By calculating the relative error for each picking point, it was found that the relative error for each group did not exceed 2%. This indicated that using the D415 camera for crabapples positioning was relatively accurate and met the positioning requirements, thus proving the effectiveness and feasibility of the proposed method of using the Realsense D415 depth camera for clustered crabapples recognition and localization.  
Figure 14: Identification and localization of crabapples. (**a**) Location of 5 clustered crabapples. (**a** to E represent the five points for picking crabapples) (**b**) Identification and positioning of 5 clustered crabapples. (**c**) Location of 3 clustered crabapples. (**a** to C represent the three points for picking crabapples) (**d**) Identification and positioning of 3 clustered crabapples.  
The error of the camera has a potential influence on practical application and should be paid attention to. For example, when applied to robot navigation, if the error is large, the robot may misjudge the distance of obstacles, resulting in collision or inaccurate path planning; real-time positioning and mapping will affect the quality of 3D point clouds, causing the map to distort or drift. When applied to 3D scanning and modeling, the error will cause noise on the surface of the scanned object, resulting in distortion of the 3D reconstructed model.