esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### Vital Sign Detection Using a Mono-Static UWB Life-Detection Radar  
#### 2.2.1 Background  
For one UWB radar detection, the received signal \(S(t)\) with the respiration of a human target can be expressed as:  
\[S(t)=\sum_{n=0}^{N-1}u(t-nT-\tau_{r})*h_{r}(t)\ +\sum_{n=0}^{N-1}\sum_{p=1,p\
eq r}^{p}u \big{(}t-nT-\tau_{p}\big{)}*h_{p}(t)+\gamma(t)+\omega(t) \tag{1}\]  
where \(*\) means convolution, \(u(t)\) means the radar transmitted signal, \(t\) means fast-time, slow-time is discrete with \(nT\), on which the reflected signal is received, and \(T\) is the adequate pulse repetition time (PRT). \(h_{r}(t)\) means the respiration response of the human target and \(h_{p}(t)\) means the joint impulse response of transmitting antenna, receiving antenna and \(P-1\) static objects. \(\tau_{r}\) and \(\tau_{p}\) mean the propagation time-delay of the human target and the \(p\)th object, respectively. \(\gamma(t)\) is the non-stationary inference and \(\omega(t)\) is the additive white Gaussian noise.  
To avoid frequency aliasing and range ambiguity, T should satisfy the Nyquist sampling theorem and ensure that all the reflected signals of targets are received during a PRT. All discrete signals of the reflected signals can be expressed as a two-dimensional (2D) (fast-time and slow-time) \(M\times N\) matrix \(S(m,\ n)\):  
\[S(m,n)=h(m,n)+c(m,n)+\gamma(m,n)+\omega(m,n) \tag{2}\]  
where \(M\) means the number of fast-time sampling points and \(m=0,1,\ldots,M-1\), \(N\) means the number of slow-time sampling points and \(n=0,1,\ \ldots,N-1\). Every term in (2) corresponds to that in (1). Matrix \(S(m,\ n)\) is named slow-time range matrix, where the column represents the range dimension and the row represents the slow-time dimension, respectively.
## 3 Materials and Methods
### Context-Aware Technical Information Manager  
Technical information is mapped onto a database of technical assets using an ontology for technical information. The context is then dynamically mapped onto a context vector also using a context ontology to describe the application domain. This vector contains data about the activity, the operator, and the environment. It can also contain other elements as tools and key performance indicators that can influence the choice of technical assets. A reasoner, using recommender system technology, selects the best technical assets according to the context vector and presents them dynamically using an active AR interface to the user.  
Our system implementation uses a Java http back-end and a front-end mobile application implemented with Unity 3D to manage the active AR output on the AR device (see Figure 2).  
Figure 2: System implementation consisting of a Java http back-end and a front-end mobile application to manage the active AR output.  
The Java http back-end receives context information and reasons using the ontology on the currently loaded domain. Both the technical information ontology and the context ontology are handled by Protege, a broadly used software for the assisted generation/customization of OWL ontologies [23]. The reasoner uses two non-standard reasoning algorithms. For the technical information recommendation (i.e., what information to present), a concept contraction/abduction reasoning algorithm is used. For the technical asset type selection (i.e., how to present the information), we use the concept covering algorithm implemented in Mini-ME [24], which tries to compute the best consistent coverage for the current context. The various asset type descriptions are associated to 'rules', predefined by the application designer, to try to cover (without contradictions) all the concepts that describe the current context. The concept covering rules are used by the reasoner to compute the distance between the assets and the context vector. The back-end replies to front-end requests with a list of compatible technical assets, ordered according the concept covering distance. This ranking suggests the technical assets more appropriate to the current context. There could be some context scenarios where CATIM is not able to display instructions. This is mainly due to safety issues. For example, if the operator must have free hands for safety reasons and only smartphone is available as device, CATIM will not provide instructions suggesting using other displaying output (e.g., a monitor or a paper-based support). In this way, CATIM is also able to prevent specific risks in industrial environments.
## 3 Constructing an AT Representation Model Containing Tolerance-Zone DOF Layers
### Tolerance-Zone Layer  
The tolerance-zone layer specifies the direction of the tolerance zone and the mapping relationship between tolerance types and assembly element feature. They provide the reasoning basis for determining the representation of DOF vectors in tolerance zones. In the case of a single benchmark, the tolerance has nine basic shapes, as shown in Table 1. When applying different tolerance types to different assembly features, the shape of the tolerance zones may vary. The tolerance zone type relationship matrix between the tolerance type (single datum) and the assembly feature type is shown in Table 2. The relationship between them can be represented by the adjacency matrix \(M_{T,21\times 6}\), as indicated in Equation (1).  
\[M_{T,21\times 6}=\begin{bmatrix}C_{1,1}&C_{1,1}&\dots&C_{1,6}\\ C_{2,1}&C_{2,1}&\dots&C_{2,6}\\ \dots&\dots&\dots&\dots\\ C_{21,1}&C_{21,2}&\dots&C_{21,6}\\ \end{bmatrix} \tag{1}\]  
where the rows of the matrix \(M_{T,21\times 6}\) represent the tolerance types and the columns correspond to the AFE types, and the values of the elements \(C_{i,j}\) are related to the correspondence between the tolerance types and the AFEs, and if the tolerance type of the ith row has a mapping relationship with the type of AFE of the jth row, the value will be the tolerance zone type designation in Table 1, otherwise it will be \"0\".  
Figure 1: Assembly tolerance information representation model. A denotes the assembly, P denotes the part, PS denotes the part assembly feature surface (AFS), SPL denotes the assembly feature element (AFE) spatial relationship, TYP denotes the assembly tolerance type, TZ denotes the tolerance domain and TZDF denotes the tolerance-zone DOFs.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 2 Proposed Methods  
In this section, DDCE is presented in detail. First, consistent edges of infrared and visible images are extracted to capture the global structures of images. Second, common feature points are detected based on consistent edges. Third, the edge length weighted edge orientation histogram is calculated to build feature descriptions. Lastly, feature correspondences are established according to the obtained feature descriptions.
## 6 System Development
### Crdt
#### 6.3.6 Message Broadcasting  
Kafka offers a highly efficient and distributed mechanism for disseminating messages among consumers. When consumers are organized within the same consumer group, [PERSON] ensures that only one instance within that group receives a particular stream of data. This approach has advantages in microservices architectures, as [PERSON]'s cluster can manage message delivery, ensuring fault tolerance. In cases where a message is not consumed by one instance due to, for instance, a failure, [PERSON] seamlessly redirects the message to the next available consumer, maintaining the continuity of processing.  
However, in certain scenarios, such as when broadcasting messages across multiple consumers, this default behavior may not be suitable. In such cases, all consumers must receive the same message simultaneously. To achieve this objective, a solution entails assigning each consumer to a distinct consumer group, thereby ensuring uniqueness at the Kafka cluster level.  
Consequently, each running instance subscribed to a topic, albeit with different group IDs, will be capable of receiving the stream of data independently, facilitating the desired behavior of simultaneous message consumption across all consumers. Providing a unique group ID to all consumers can be achieved by subscribing in each running instance of the RTC microservice to the same topic but with a randomly generated consumer group ID.  
Randomly generated UUIDs as consumer group identifiers will solve the broadcasting issue, but they have some drawbacks. This method ensures unique consumer groups, but it also creates more groups, requiring more resources from the Kafka cluster to manage message delivery. More consumer groups can strain cluster resources and affect performance. However, this strategy allows for the better distribution of messages among microservices, improving fault tolerance and scalability. By aligning the number of microservices with the Kafka partitions and experimenting with different setups, organizations can find the best balance between resource use and message distribution.
## 5 Implementation and Evaluation
### Complexity Analysis for the Water Cycle Detection Approach
#### 5.1.2 Evaluation of the Water Cycle Approach
It gave accuracy results of approximately 100% when using the DT and DL classifier algorithms. Moreover, if we take into account the average accuracy performance for each feature selection technique, WC continued to have the best accuracy performance, followed by POS, HS, GA, and SA in that order. With regard to the classifier algorithms used with the WC feature selection technique, we found that the DT and DL algorithms were equally the best in terms of performance accuracy and number of identified WSN-DS features. The accuracy performance metric was 100% for both and the number of WSN-DS features identified was 1. The WSN-DS feature sequence was 17 for both classifiers. The accuracy performance of the remaining classifier algorithms was distributed as SVM 99.04%, KNN 98.9%, and NB 81.98%. The number of selected WSN-DS features was also distributed between 12 and 15. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier Algorithms** & **Accuracy** & **\#Features** \\ \hline DT & 99.5922 & 18 \\ KNN & 98.512259 & 18 \\ NB & 56.87 & 18 \\ SVM & 98.817526 & 18 \\ DL & 97.6987 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy results of five classifier algorithms for WSN-DS dataset. Due to the similarity of performance accuracy and number of selected features between the WC + DT and WC + DL, we resorted to using [PERSON] and Iman-Davenport statistical tests [69] in order to compare the two types. The [PERSON] and Iman-Davenport tests are designed to demonstrate whether there is a statistical difference between classes (crossover operators) [70]. Table 8 shows the average ranking of the WC with machine learning algorithms according to [PERSON]'s test (the lower the value, the higher the rank). The last two rows in Table 8 refer to the p-value of the [PERSON] and Iman-Davenport statistical tests [69]. The results tabulated in Table 8 show that the WC+DT had the lowest value, so it was rated first. We chose the WC + DT technique to implement in a WSN simulation. We selected it based on the highest performance in accuracy and [PERSON]'s test ranking. To implement this technique in WSN simulations, we needed to determine the WC+DT output model from the training and testing processes. Therefore, the best value for maximum depth was set as 10 based on the accuracy performance metric. The dependent variable (Label feature) of the WSN-DS dataset had two values (0 \"Normal\" or 1 \"Attack\"), and thus the portion of the WC + DT output model from the WSN-DS dataset represented the relationship between the independent variables and dependent variables as illustrated in Figure 5.
## 2 Methods
### Data Assimilation System  
We use the four-dimensional (4D) LETKF, as a method of the EDA system at KIAPS, whose analysis is obtained by assimilating the available observations within a local region [29; 30; 31]. The 50 ensemble members analyze the zonal wind, meridional wind, potential temperature, humidity mixing ratio, and surface pressure at a 50 km horizontal resolution. The EDA system includes the tropical cyclone initialization [32] as well as the real observations (e.g., sonde, surface, aircraft, Global Positioning System-Radio Occultation, Infrared Atmospheric Sounding Interferometer, Advanced Microwave Sounding Unit-A, Cross-track Infrared Sounder, Microwave Humidity Sounder, Advanced Technology Microwave Sounder, and Atmospheric Motion Vector) [33; 34]. The initial conditions of the LETKF cycle are produced by modifying the analysis with the lagged forecast differences that are used to generate the static BEC for the hybrid 4D ensemble-variational data assimilation system of KIAPS [35].  
Several inflation methods have been implemented to prevent filter divergence [31], such as the following: (1) additive inflation that adds perturbations randomly sampled from the bias-corrected lagged forecast differences used to generate the static BEC to each ensemble member after the analysis step [36]; (2) adaptive multiplicative inflation that multiplies perturbations using an adaptively estimated inflation factor [6]; and (3) relaxation to prior spread (RTPS) that relaxes the ensemble standard deviation of analysis (posterior) back to the background (prior) by defining a relaxation parameter with a range from 0 to 1 [12]. In the current system, the RTPS with the relaxation parameter of 0.95 and the additive inflation showed better performance than any other configurations that we have tried (not shown).  
Regarding the covariance localization, the horizontal localization is given by a Gaussian-like piecewise fifth-order rational function [6; 37] and varies from 660 to 1800 km in radius of influence (ROI) depending on vertical levels [38]. The vertical localization differs as to the observation type. For the conventional data, it is defined by a Gaussian-like rational function as \(2\sqrt{\frac{10}{3}}\sigma_{v}\) where \(\sigma_{v}\) is dependent on pressure \(p\): \(\sigma_{v}\) is \(0.2\ln(p)\) for wind and surface pressure and \(0.1\ln(p)\) for mass variables. For the radiance data, the vertical weighting function is defined by the gradient of transmittance of the measured radiance [39].
## 2 Materials and Methods
### Finite Element Models  
The models consisted of tetrahedral solid elements with a side of 1.0 mm. The numbers of nodes and elements for each model are listed in Table 1. The mechanical properties utilized in the models, including Young's modulus and Poisson's ratio (Table 2), were applied according to previous studies [25]. The interface between each material was fully bonded. The elements on the lateral and bottom surfaces of the cortical and trabecular bones were fully constrained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **Number of the Nodes** & **Number of the Elements** \\ \hline Tooth & 21,427 & 13,192 \\ Periodantal ligament & 9702 & 4829 \\ Cortical bone & 19,603 & 12,972 \\ Trabecular bone & 9323 & 4980 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of the nodes and elements for each model.  
Figure 2: Geometries of the three-dimensional FE models and 3D XYZ coordinates utilized in this study. (**A**) FE model of maxillary right first molar, (**B**) periodontal ligament, cortical bone, and trabecular bone. (**C**) Three different vertical positions of the hook are defined on the YZ plane (coronal; \(Co\), middle; \(Md\), and apical; \(Ap\)). Palatal mucosal contour is shown with pink dotted line.  
When performing molar intrusion with MMEA, a position of the hook is considered to affect the behavior of the molar. To evaluate such influence, we set three vertical positions of the hook (coronal; \(Co\), middle; \(Md\), and apical; \(Ap\)) at 6.0 mm perpendicular intervals in the occluso-apical direction along the palatal mucosal contour (Figure 2C). Regarding an intrusive load, a point of application for the load was 3.5 mm above the cervix along the medio-distal midline on the palatal surface of the crown. A unit load of 1.0 N was applied from the point to each hook position. In addition, a load to the buccal direction was also applied to simulate expansion by the buccal arch wire. The load was defined as a \"counter load\" to avoid palatal tipping of the molar. A point of application for the counter load was on the buccal surface of the molar, and it was symmetrical to the application point of the intrusive load with the XZ plane. The counter load was applied perpendicularly to the buccal surface, and its magnitude was varied from 0 N to 1.5 N in 0.1 N steps (Figure 3A).
## 3 Results and Discussion  
### Morphology, Elementary Composition  
Pure CNFs were observed by high-resolution transmission electron microscopy (HR-TEM) and showed a morphology typical of these carbon materials [27] composed of irregular micrometer-length hollow fibers with a wide range of nanometric diameters (see Figure 1a). The EDS of the CNFs showed C/O ratios of 31.3. The TEM micrographs (Figure 1b) of the calcium alginate/CNFs ultrathin composite samples showed that the CNFs (dark phase) are embedded and heterogeneously distributed in the polar alginate polymer cross-sectional matrix (clear phase) because these carbon materials are highly hydrophobic and non-polar in nature [28].  
Figure 1: High-resolution (HR)-TEM of pure carbon nanofibers **(a)**, TEM ultrathin sections of calcium alginate films with 0.1% \(w/w\) of carbon nanofibers **(b)**.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
###### Abstract  
Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCONet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCONet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.  
MIMO; uplink precoding; limited feedback precoding; MMSE receivers; convolutional neural networks (CNNs); joint transceiver design Article  
* 2022
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 8 Conclusions  
This paper proposes a modified MPPT algorithm and realizes the maximum power capture under different working conditions based on the gyroscope WEC. The main work of this paper is as follows:  
1. The paper introduces the working principle of the gyroscope wave energy converter, and the energy conversion process. The influence of the natural frequency, damping ratio, and input parameters of the wave condition on the power output are analyzed. The complete expression of the power output under wave energy is obtained;  
2. We conclude from the dynamic model of the system that the linear model can be used to replace the nonlinear model when the precession angle is small. The influence of each input parameter on power, conversion efficiency, and the experimental platform is analyzed, and the single-peak function of power output is verified. The linear equation of the output power is obtained through multiple linear regression, and the correctness of the regression analysis is verified by the prediction model;  
3. This paper proposes a modified MPPT algorithm based on the traditional P&O method which focuses on improving the stability of power output and ensuring certain dynamic characteristics. The basic idea of the algorithm is to dynamically adjust the perturbation step length with an inverse proportional function to ensure the stability of the system while achieving rapidity. The sign of the power difference is used to determine whether the system is in a steady state, and smaller step size is applied to reduce the steady-state error. The adopted mean filtering function provides the system with strong robustness and an excellent ability to adapt to changes in external conditions;  
4. The proposed algorithm is compared with the fixed size algorithm under the rated condition. The simulation results show that the method has a faster dynamic response, better stability, and smoother output power curve. Then, two variable step size algorithms are introduced to compare with the proposed algorithm under different sea states. The results illustrate that the proposed algorithm has excellent stability and rapidity. Eventually, experimental results verify that the designed MPPT control algorithm can effectively maximize power generation under different conditions.
## 3 Results and Discussion
### High-Performance Liquid Chromatography (HPLC) Result  
The reference values for anthocyanins in this study were evaluated from thirty-two rice samples planted in different years (2019 and 2020), and the result is presented in Table 2.  
Sixteen rice varieties harvested in 2019 contain anthocyanins at levels between 0.07 and 1.18 mg/g, while 2020 samples contain anthocyanins at levels ranging from 0.32 to 2.07 mg/g. Cyanidin-3-glucoside (C3G) is the most abundant anthocyanins in the examined samples and can be evaluated in all rice varieties. Among 32 samples of black rice, twenty-five samples contain peonidin-3-glucoside (Pn3G) in a shallow concentration, ranging from 0.05 to 0.18 mg/g. A previous study by [PERSON] et al. (2008) [38] reported that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Component** & **Number of Rice Varieties** & **Mean \(\pm\) SD** & **Max** & **Min** & **Range** \\ \hline \multirow{4}{*}{2019} & Total anthocyanins & 16 & 0.41 \(\pm\) 0.32 & 1.18 & 0.07 & 1.11 \\  & Cyanidin-3-glucose & 16 & 0.38 \(\pm\) 0.29 & 1.07 & 0.07 & 1.00 \\  & Peonidin-3-glucose & 9 & 0.07 \(\pm\) 0.02 & 0.11 & 0.05 & 0.06 \\  & Total anthocyanins & 16 & 0.91 \(\pm\) 0.59 & 2.07 & 0.32 & 1.75 \\
2020 & Cyanidin-3-glucose & 16 & 0.81 \(\pm\) 0.56 & 1.89 & 0.26 & 1.63 \\  & Peonidin-3-glucose & 16 & 0.10 \(\pm\) 0.03 & 0.18 & 0.06 & 0.12 \\
2019–2020 & Total anthocyanins & 32 & 0.66 \(\pm\) 0.53 & 2.07 & 0.07 & 0.12 \\  & Cyanidin-3-glucose & 32 & 0.59 \(\pm\) 0.49 & 1.89 & 0.07 & 1.82 \\  & Peonidin-3-glucose & 25 & 0.09 \(\pm\) 0.03 & 0.18 & 0.05 & 0.13 \\ \hline \end{tabular} SD: Standard deviation.  
\end{table}
Table 2: The reference value of anthocyanins (mg/g) obtained through HPLC analysis.  
Figure 3: Mean of near-infrared pre-processed spectra using SNV of three rice group based on the anthocyanins content for seed sample (**A**) and powder sample (**B**), and beta coefficients curve of PLSR model for seed (**C**) and powder (**D**).  
C3G and Pn3G were the major anthocyanins identified in the Korean black rice, while [PERSON] (2010) [11] showed C3G as the highest anthocyanins type in black rice. C3G and Pn3G were also reported as the most common anthocyanin types in Chinese black rice [12]. In this research, the C3G concentration was similar to the total anthocyanins content. Thus, the prediction model for anthocyanins in this study was only based on the total anthocyanins content in rice.