esa-sceva/satcom-chunk-collection huggingface dataset:
# Western Mediterranean Precipitation Extremes, the Result of Quasi-Resonant Sea-Atmosphere Feedbacks
## 2 Materials and Methods  
### Data  
Daily (1/4\({}^{\circ}\)\(\times\) 1/4\({}^{\circ}\)) SST data are provided by the NOAA: [[https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/](https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/)]([https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/](https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/)) accessed on 1 May 2023 [22; 23; 24; 25]. The daily optimum interpolation SST is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High-Resolution Radiometer (AVHRR). The data are averaged on grids (1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)) to obtain a resolution adapted to the needs of the present study. Indeed, here the representativeness and the precision of the SST take precedence over the spatial resolution.  
Version 06 of daily (0.2\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\)) data of precipitation (2000 to present) combines information from the GPM satellite constellation using microwave sensors to estimate precipitation over the majority of the Earth's surface: [[https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/](https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/)]([https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/](https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/)) accessed on 1 May 2023 [26].
## 3 Related Work
### SDN Assisted Streaming Using Multiple Connections  
In some works, MPTCP in SDN is considered. References [25,26] focus on determining the appropriate number of SVC layers for transferring across selected streaming paths. The approachof [25] is based on priorities that first determines the shortest route N and then sends the video layer using one of the selected routes. The approach in References [26], on the other hand, uses the optimal paths rather than the shortest. In addition, since they focus on the UDP protocol, References [25; 26] do not target DASH video streaming. [PERSON] et al. [27] propose an architecture based on SDN to improve scalable video streaming efficiency using DASH. They measure their proposed system against QoE parameters and conclude that for each QoE parameter, traditional shortest-path routing provides better performance. Determining routing and providing quality adaptation is considered by few works. [PERSON] et al. [28] develop an optimization model for maximizing video quality. In order to select optimal paths for different SVC layers over SDN, they consider the current segment bit rate, available network bandwidth, path length and competing flows. [PERSON] et al. [29], and [PERSON] et al. [30], propose to use routing strategy in an SDN based networks for the routing of the MPTCP sub-flows to provide QoS/QoE-guaranteed services to DASH clients. [PERSON] et al. [14], propose a Dynamic Multi Path Finder (DMPF) algorithm that leverages the properties of multiple paths available in the network to transfer each client request SVC layers over different paths. They accommodates maximum requests by DASH clients while utilizing the network bandwidth efficiently by dynamically adapting to the varying connection characteristics.
# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
###### Abstract
The CCFont is trained to separate all the components constituting Chinese characters from the 2003 traditional Chinese (TC) characters currently used in China and combines them in a new style. The CCFont model automatically generates not only almost all TC characters (max 17 components) but also all 7445 simplified Chinese (SC) characters of GB/T 2312-1980, which includes 99.99% of the Chinese characters currently used in China, in real time with a high-quality new style. Because our proposed method obtains style information from the components and not from the glyphs, the model reduces the number of failure cases. It operates in an end-to-end manner that converts styles using component images of Chinese characters and can generate high-quality Chinese characters without additional training steps for unseen font styles. The CCFont model is executed step by step as follows:  
1. Data generation (Character-to-Radical-to-Components Module, CRC-Module);
2. Model training (CCFont Training Module);
3. Generating new character/style Chinese characters (CCFont Generating Chinese Characters, GC-Module). The data generation module breaks Chinese characters into components to generate data for the CCFont Training Module, which receives the data and regenerates the images through training. For generating a new character, the GC-Module uses the CCFont training module to generate Chinese characters. Figure 2 depicts an example of Chinese characters generated (out) by the CCFont model in the style of the target character (tgt) based on character input (src). Figure 1: A sample case of failures to generate Chinese characters. The CCFont model proposed in this study has the following advantages and contributions:  
* Decomposing Chinese characters into components decreases the rendering difficulties significantly. * Component image conversion, which is much simpler than character shapes, significantly reduces the number of failure cases and enables high-quality character generation. * Up to 17 components can be generated, making it possible to generate almost all Chinese characters (99.99% in use). * The development of TC and SC Chinese character automatic generation/con-version model (CCFont) using Chinese character components. * Experiments demonstrated zero-shot font generation and generated Korean (Hangul) and Thai fonts using the CCFont model. There are two types of Chinese characters, TC and SC. In this paper, we trained our model with only TC characters and demonstrated that the model is generalized to generate the unseen SC characters. Additionally, we conducted experiments to demonstrate that the proposed method can also generate multi-lingual characters such as Korean Hangul and Thai characters.
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.7 Lipids  
Lipids are essential precursors of the aromatic substances in tea [32]. After stuck fermentation, an overall of 60 lipids were elevated, whereas just seven were down-regulated (Figure 3G). Free fatty acids (FFAs), lyso-phosphatidylcholine (LPC), lyso-phosphatidylcholine (LPE), and glycerol ester were the four primary lipids that increased significantly in the fermented tea. [PERSON] et al. also reported an increase in FFAs, LPE, and LPC in tea soup after fermentation, and proposed that FFAs are likely formed during lipolytic reactions driven by microbial hydrolases [32]. Glycerophospholipids, which are crucial elements of the plasma membrane, degrade into LPE and LPC [20]. According to one research, microbes cover the surface of tea leaves after fermentation and drastically change their cell structure [33]. Therefore, we hypothesized that the elevated LPE and LPC in FHYH may be the result of the degradation of the microbial plasma membrane. Linoleic acid (CAS:60-33-3), a polyunsaturated fatty acid that was significantly up-regulated after stuck fermentation, is a precursor of an aliphatic aromatic substance that contributes to specific aromas in microbially fermented teas [32]. This could explain the shift in aroma to fungal type after stuck fermentation. The formation of aroma precursor compounds from polyunsaturated fatty acids is the result of assisted oxidation by lipoxygenase (LOX) [20]. In fact, [PERSON] et al. have isolated an _Aspergillus_ strain that secretes highly active LOX [34]. In conclusion, microbial lipid metabolism during stuck fermentation may generate precursors that transform the aroma profile of FHYH.
## 3 Integrated Model
### Computation Algorithm for the Model  
The calculation algorithm for the model is shown in Algorithm 1. First, determine the approximate range of \(N\) and \(L_{p}\), set the initial values of degradation magnitude, time, cost, and maintenance times, and obtain the production time of each product through the actual demand and productivity; then, start production from \(t=0\) (according to the system specification (detailed description)), in each inspection window and the degradation of the system can be obtained. According to the relationship between the degradation and \(L_{p}\), \(L_{f}\), the type of maintenance the system needs to perform is determined (according to the detailed description of maintenance situations). Calculate and record information, such as the magnitude of degradation after maintenance (or without maintenance), maintenance times, and the maintenance cost and proceed sequentially until the end of production. Finally, it is necessary to traverse all combinations of \(N\) and \(L_{p}\) to find the combination that maximizes the profit per unit time.  
```
1. Give the value range of \(N\) and \(L_{p}\).
2. Assign \(X(t_{0})=0,t=0,TC=0,r=0,f=0\)
3. Obtain \(\Delta t_{s}\) by \(Q_{s}/(u\cdot N)\), generate the production time of one cycle.
4. for \(j=0:N\)
5. for \(s=0:m\)
6. Generate \(t_{t}^{\prime}\) based on the required set-up time for each product. Determine which product should be produced at time \(t_{mt}^{i}\).
7. Determine the maintenance status at time \(t_{t}^{\prime}\) based on the degradation quantity \(X(t_{t}^{\prime})\).
8. Determine the relationship of \(X(t_{t}^{\prime})\) with \(L_{p}\) and \(L_{f}\), if \(X(t_{t}^{\prime})<L_{p}\), doing nothing, means no repair time. Generate \(X(t_{i+1})\) from \(X(t_{t}^{\prime})\) and \(\Delta X\). Obtain the cost \(C(t_{i+1})\), times of PM \(r(t_{i+1})\) and times of CM \(f(t_{i+1})\) at \(t_{i+1}\).
9. else if \(L_{p}\leq X(t_{t}^{\prime})<L_{f}\), carry out PM, need to spend the corresponding PM time, then \(X(t_{t}^{\prime})\) into \(X(t_{mt}^{i})\), where \(X(t_{mt}^{\prime})=\gamma\). Generate \(X(t_{i+1})\) from \(X(t_{mt}^{i})\) and \(\Delta X\). Obtain the cost \(C(t_{i+1})\), times of PM \(r(t_{i+1})\) and times of CM \(f(t_{i+1})\) at \(t_{i+1}\).
10. else \(X(t_{t}^{\prime})\geq L_{f}\), carry out CM, need to spend the corresponding CM time, \(X(t_{t}^{\prime})\) into \(X(t_{mt}^{i})\), where \(X(t_{mt}^{i})=0\). Generate \(X(t_{i+1})\) from \(\Delta X\). Obtain the cost \(C(t_{i+1})\), times of PM \(r(t_{i+1})\) and times of CM \(f(t_{i+1})\) at \(t_{i+1}\).
11. end
12. end
13. Obtain the total cost \(TC\), total number of PM \(r\), total number of CM \(f\).
14. Calculate profit per unit of time.
```  
**Algorithm 1** Computation algorithm for the model.
## 1 Introduction
### Related Works
These features were fused and fed into the multi-class support vector machine (SVM) for the classification of different nodule types. Their scheme is evaluated using two datasets, namely LIDC-IDRI and ELCAP. They achieved the classification rate of 93.1% on LIDC-IDRI and 93.9% on ELCAP, respectively. However, their nodule voxels selection was based on the radii threshold; thus, it is challenging to make it a scalable scheme for the detection of invariant and very tiny nodules. The primary objective of this paper is to present a fully automated scheme that can detect various types of nodules providing a high accuracy. Unlike the previous works, we present three significant contributions. For the first contribution, we propose an automated seeded region growing algorithm for the segmentation of lung regions. It is an advance of the original region-growing method and can overcome the weakness in the conventional one, in particular, the needs of the proper initial region and homogeneity criteria. For the second contribution, we present a three-dimensional chain coding algorithm to detect the boundary-attached nodules. This algorithm traces the voxelized edge of the lungs to find the juxta-pleural nodules. As it is based on three-dimensional codeword systems, it can provide more specific information about the nodules, as compared to the two-dimensional methods. Moreover, it does not need an extra algorithm to find the start and endpoints of the boundary gap. As an advantage, we can eliminate the training related problems as well as saving on computational effort. For the third contribution, we develop an optimized random forest (RF) for false reduction. The original RF is a successful ensemble method for nodule detection, and we further improve its performance by combining the attribute selection algorithms in order to get more accurate diagnosis results. The rest of this paper is organized as follows. Section 2 describes the details of the materials and methods used in this work. Section 3 designs the proposed nodule detection and evaluates the experimental results. Lastly, Section 4 summarizes the paper by discussing the scope of future works.
## 5 Conclusions  
This paper presents a high-accuracy SFIR filter design based on an adaptive scaling algorithm. The relationship between representation noise and represented values of a stochastic bit stream is theoretically analyzed, providing a potential way to improve the accuracy under the same stochastic bit stream length. Afterward, an adaptive scaling algorithm (ASA) is proposed for the SFIR to scale the input signals into low-noise regions adaptively.According to the simulation results on a 64-tap FIR filter, the ASA and AV methods gained \(17\) dB and \(6\) dB in terms of signal-to-noise ratio (SNR), respectively. Finally, the hardware architecture of the proposed ASA-based SFIR (ASA-SFIR) is designed and implemented, which demonstrates \(4.6\) times hardware efficiency improvement with respect to the existing SFIR schemes.  
Conceptualization, J.H. and K.H.; methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and K.H.; investigation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); simulation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); hardware, K.H.; writing, [PERSON] ([PERSON]), K.H., J.H. and J.W. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China under Grant No. 62001277 and 62001276. Guangdong Basic and Applied Basic Research Fund under Grant No. 2019A1515110560. National key research and development plan No. 2018 YFB1801500.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 2 Array Signal Processing Model Based on Compressive Sensing
### Array Received Signal and Its Sparsity
#### 2.1.2 Wideband Array Received Signal  
The wideband underwater sound sources share the same received model for all narrowbands of different frequencies, so the wideband observation model can be treated as separated narrowband problems. We assume that they are independent and uncorrelated between different frequency bands in this paper. Therefore, the frequency domain form of the ULA received signal \(\mathbf{X}(f)\) as the superposition of \(G\) narrowband signals, Equation (1) can be written as:  
\[x_{n}(f)=\sum_{k=1}^{K}s_{k}(f)e^{-|2\pi f\frac{d(n-1)\cos\left(\theta_{k} \right)}{c}}+v_{n}(f),\ n=1,2,\cdots,N, \tag{4}\]  
We divide the frequency domain array element observation signal into G narrowband at different frequencies in the bandwidth B, therefore, the wideband observation model of the ULA received signal for the \(g\)th narrowband in the frequency domain can be expressed by Equation (2) as [29]:  
\[\mathbf{X}(f_{g})=\mathbf{A}\big{(}f_{g},\mathbf{\theta}\big{)}\cdot\mathbf{S}\big{(}f_{g} \big{)}+\mathbf{V}(f_{g}),(g=1,2,\cdots,G), \tag{5}\]  
where \(f_{g}\) represents the center frequency of the \(g\)th narrowband signal, \(\mathbf{V}(f_{g})=\big{[}V_{1}(f_{g}),V_{2}(f_{g}),\cdots,V_{N}(f_{g})\big{]}^{T}\) is the \(j\)th narrowband component of the noise transformed by DFT, \(\mathbf{X}(f_{g})=\big{[}X_{1}\big{(}f_{g}),X_{2}(f_{g}),\cdots,X_{N}(f_{g})\big{]} ^{T}\) is the \(g\)th narrowband component of the ULA received signal transformed by DFT, \(\mathbf{S}(f_{g})=\big{[}S_{1}\big{(}f_{g}),S_{2}\big{(}f_{g}\big{)},\cdots,S_{P} \big{(}f_{g}\big{)}\big{]}^{T}\) is the amplitude information of targets at the \(g\)th narrowband component, \(\mathbf{A}\big{(}f_{g},\mathbf{\theta}\big{)}=\big{[}\mathbf{a}\big{(}f_{g},\theta_{1} \big{)},\mathbf{a}\big{(}f_{g},\theta_{2}\big{)},\cdots,\mathbf{a}\big{(}f_{g},\theta _{p}\big{)}\big{]}\) is the array manifold matrix at frequency \(f_{g}\), \(\mathbf{\theta}\) is the set of the all potential bearing of the \(K\) targets, \(\mathbf{a}\big{(}f_{g},\theta_{p}\big{)}\) is the array response vector or bearing vector of the target which is from bearing \(\theta_{p}\) as incident frequency \(f_{g}\), and it can be shown as:  
\[\mathbf{a}\big{(}f_{g},\theta_{p}\big{)}=\bigg{[}1,e^{-j(\frac{2\pi f_{g}}{c} \times d\times cos\theta_{p})},\cdots,e^{-j(\frac{2\pi f_{g}}{c}\times(n-1) \times d\times cos\theta_{p})}\bigg{]}^{T},(p=1,2,\cdots,P). \tag{6}\]  
It can be seen from the above that the target is sparse under the divided grid, and when \(M\geq Klog(N)\) is satisfied [21], the array data after compressive sampling can contain the essential information of the target signals and complete the detection estimation of \(K\) targets with high probability.  
Figure 2: P grid points for narrowband sound sources.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 5 Assessment of Combined GPS/LEO RTK for Different Length Baselines
### RTK for Medium-to-Long Baselines  
In order to better investigate the augmentation performances of different LEO constellations for medium- and long-baseline RTK positioning, the medium baselines of 33.0, 44.2, 57.0, 68.7, and 88.0 km, as well as the long baselines of 138.2, 390.5, 514.7, 604.7, and 739.2 km, are selected for analysis from the perspectives of convergence time and TTFF. Meanwhile, it is noted that the DD ionospheric and tropospheric delays need to be considered as estimated parameters in medium-to-long baseline schemes.  
Figure 8 presents the average convergence time and TTFF of different-baseline schemes with the augmentations of different LEO constellations. It is obvious to see that the convergence time and TTFF are gradually getting longer with the increase of baseline length. For one specific baseline, the LEO constellation can make a significant contribution to the convergence and ambiguity-fixed performances. Especially for the 192- and 288-satellite constellations, the average convergence time and TTFF for all baselines can be shortened by about more than 80% and even reach to about 1 min. At meanwhile, it also can be found that the long-range improvement is a little better than medium-range one.  
Further statistics about LEO constellation-augmentation performances in above-mentioned medium- and long-baseline schemes are demonstrated in Figure 9. Through introducing LEO constellations consisting of 60, 96, 192, and 288 satellites, the correspondingly average convergence time for medium baselines can be shortened from 4.19 to 2.7, 1.54, 0.77, and 0.48 min, and that for long baseline are dramatically decreased from 8.55 to 4.56, 2.55, 0.96, and 0.80 min, compared with the  
Figure 7: Average convergence time (**left panel**) and TTFF (**right panel**) of different LEO constellations augmented GPS RTK positioning for five short baselines.  
GPS-only scheme. As for the average TTFF, the statistical time for medium baselines can be shortened from 7.11 to 4.03, 2.56, 1.22, and 0.90 min, respectively. More obvious improvement is observed for the long baselines, which can reduce the TTFF from 12.34 to 7.44, 3.62, 1.62, and 1.31 min, respectively. As a result, the convergence and ambiguity-fixed speeds of RTK positioning can indeed be significantly improved with the addition of LEO satellites. The larger LEO constellations, the better improvement.
# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model  
The overall framework of the NRH recommendation model (Node2 vec-side and RippleNet Hybrid Model) is shown in Figure 1, which makes the following innovations compared with the traditional RippleNet model: (1) Improving user portrait construction. Node2 vec-side knowledge representation is introduced by embedding the Ripple set, which combines the static history portrait and the dynamic preference portrait to form a new user portrait. (2) Adopting a hybrid prediction approach. The probability values are calculated separately for the two parts of the user profile, and, finally, the two parts are combined to predict the user's preference for an item.  
The following essential elements make up the basic framework of NRH:  
Input: The knowledge network of suggested items and past user behavior data is input to the NRH model. The user's previous interactions, including purchases, browsing, ratings, etc., can be included in the historical behavior data. The items' properties, tags, and other features are included in the recommended item knowledge graph.  
Node and relationship embedding generation: The NRH model uses the Node2 vec-side technique to learn the embedding representation of the knowledge graph's nodes and relationships. In the Node2 vec algorithm, random wandering on the knowledge graph produces wandering sequences. There are correlations and similarities between the objects in these node sequences. The embedded representations of nodes and edges are then learned after the nodes and edges are processed using relational aggregation, a variety of wandering algorithms, etc.  
User interest representation: The NRH model builds the user interest representation using the RippleNet algorithm. For feature and representation learning of entities and association information in the knowledge graph, RippleNet employs attention methods. The association relationship between users and items is captured by creating a comprehensive view of user-item interactions. The static history portrait and dynamic preference  
Figure 1: NRH recommendation model framework.  
portrait of users and the association between things are combined. In contrast, the interest representation of users is fused with the node-embedding representation.  
Preference prediction part: The user's preference for a particular item is forecast by combining the user's static history portrait and item portrait. The prediction probability is calculated, and the score is adjusted to do this. By combining this data, the NRH model may produce recommendation results that are more precise and individualized.