esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Resolution Hologram Calculation Method Based on Light Field Image Rendering
###### Abstract  
A fast calculation method for a full parallax high-resolution hologram is proposed based on elemental light field image (EI) rendering. A 3D object located near the holographic plane is firstly rendered as multiple EIs with a pinhole array. Each EI is interpolated and multiplied by a divergent sphere wave and interfered with a reference wave to form a hogel. Parallel acceleration is used to calculate the high-resolution hologram because the calculation of each hogel is independent. A high-resolution hologram with the resolution of 200,000 \(\times\) 200,000 pixels is calculated within only eight minutes. Full parallax high-resolution 3D displays are realized by optical reconstructions.  
holographic 3D display; computer generated holography; light field image rendering; pinhole array +
Footnote †: journal: Applied Physics Letters  
14 December 201921 January 202021 January 202023 January 202021 January  
frequency fusing methods similar to holographic stereograms for full-parallax image hologram [11] and color rainbow hologram [10] calculations with high efficiency, but the sizes of the calculated holograms are limited by the RAM (Random Access Memory) of the computer. Rectangular tiling algorithms have been proposed for high-resolution hologram calculations free of limitations from computer RAM [19; 20]. Taking Reference [19] as an example, each rectangular tile is calculated by using the 3D object data within the field of view (FOV) of this tile using the point cloud-based method. A full-parallax hologram with a resolution of 20,000 \(\times\) 20,000 can take up to 32.9 h. Another rectangular tiling-based algorithm is integral holography, which is the combination of integral imaging 3D displays and holography [21; 22; 23; 24]. In this method, each element of the light field image (EI) rendered from a micro-lens array or pinhole array is firstly fast Fourier transformed (FFT) as the complex amplitude of each elemental hologram, also called a hogel. Then the hogel is calculated by the interference of the complex amplitude with a reference wave. However, the calculation of complex amplitude using FFT is time consuming, especially for large-scale high-resolution hologram calculation.  
In order to reduce the computational time of the high-resolution hologram, and inspired by integral holography, a simple and fast calculation method without using FFT is proposed. A pinhole array is set behind the holographic plane for rendering EIs. Each EI located on the holographic plane is multiplied with a divergent spherical wave, which is regarded as the complex amplitude of this hogel. Additionally, a reference wave is interfered with complex amplitude for this hogel calculation. Parallel acceleration is used to speed up the calculation because the calculation of each hogel is independent. A full parallax high-resolution hologram with a resolution of 200,000 \(\times\) 200,000 is calculated only within 8 min. The validity of this proposed method is approved by optical reconstructions.
## 2 Theory of Tunnel Deformation under Surcharge Loading  
Ground surcharge will cause a change in the ground stress field, and, as a result, the existing tunnel will undergo settlement deformation. Figure 3 shows the longitudinal deformation diagram of the existing tunnel under the action of ground surcharge loading. In the method proposed in this paper, Euler-Bernoulli beams are used to simulate the bending deformation of existing tunnels. To simplify the calculations, the Euler-Bernoulli beam model assumes that the tunnel section undergoing bending deformation is always perpendicular to the neutral axis, as shown in Figure 4. The shear deformation at the segment joints is ignored, so only the longitudinal bending deformation of the tunnel is considered. The proper application of conservative Euler-Bernoulli beam theory can ensure the safety and stability of the tunnel to a large extent.  
Figure 4: Deformation characteristics of a Euler–Bernoulli beam.  
Figure 3: Schematic diagram of the existing tunnel under surface surcharge.  
Figure 2: Pasternak foundation model.  
Then, the existing tunnel is simplified and incorporated into the Pasternak foundation model. As shown in Figure 2, the Pasternak foundation model adds a shear layer to the Winkler foundation model, accounting for the continuous characteristics between soils.  
The analytic solution for longitudinal tunnel deformation under the action of ground surcharge is derived with the following assumptions: (a) to simplify calculations, the tunnel is equivalent to an infinitely long Euler-Bernoulli beam; (b) the shear deformation of the layer occurs and does not produce compression deformation; (c) close contact between the soil and tunnel, tunnel deformation, and the contact deformation of soil is consistent; (d) the deformation is independent of the tunnel and the lateral friction force between soil; (e) the deformation is independent of the effects of tunnel and soil compression, such as consolidation settlement and creep.  
The theory of longitudinal deformation of an existing tunnel under surface overloading adopts a two-stage analysis method. It is also a common analytical method in studying the response of adjacent construction to an existing tunnel. First, the additional load at the existing tunnel position caused by ground overloading is calculated; then, the calculated external load is applied to the existing tunnel, and the differential equation of longitudinal deformation of the existing tunnel is established. In this method, the longitudinal displacement solution matrix of the tunnel is obtained by using the finite difference method.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 4 Results and Discussion
### Recommendation Techniques  
Different recommendation techniques have been used in implementing various RSs. Table 3 presents all the papers based on the technique used and their corresponding years of publication to classify the research papers based on multiple recommendation techniques. To measure the annual frequencies of each RS technique, Figure 3 summarizes the number of paper publications under each category every year. Despite some of their weaknesses, it is apparent from this finding that the most used recommendation techniques are collaborative filtering and hybrid. Nevertheless, context-aware recommendations, a popular nontraditional technique that integrates usual recommendations with specific conditions under which recommendations should be made, have also been explored in building many RSs for learning. Previous sections of this paper have highlighted the significant advantages of using the multi-criteria recommendation technique, and its apparent benefits have been outlined in various works in the literature, such as in [27].  
Based on our findings in terms of recommendation algorithms used, it was observed that recommendation approaches are becoming more evenly distributed as nontraditional techniques are still being explored for research in RSs to make learning more impactful. In this era of Big Data, implementing new techniques can significantly influence user satisfaction and provide a promising solution. In recent years, deep learning and other artificial intelligence techniques such as evolutionary computing, genetic algorithms, fuzzy logic, and Bayesian techniques are some of the \"Other techniques\" increasingly attracting considerable attention in RSs for learning.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Collaborative Filtering** & **Content-Based** & **Knowledge-Based** & **Rule-Based** & **Hybrid** & **Context-Aware** & **Deep Learning** & **Othes** \\ \hline
2021 & [61,38,141,146,149] & [61] & [69] & [138,148] & [139,144,145] & [140] & [56,142] & [137,143,147,151] \\
2020 & [48,126,127,129,131,133,150] & [67] & [130] & [114,123,128,135,136] & [55] & [54,125,132] & [124,134] \\
2019 & [106,108,111,112,120,121] & [113] & [118] & [119] & [60,110] & [53,106,115,116,122] & [109,117,118] \\
2018 & [43,646,105-105] & [22,96,101] & [58] & [52,65,979,102] & [43] & [42,66] & [100] \\
2017 & [41,487,92,95] & [39] & [94] & [40,59,85,99,90,93] & [88] & [91] \\
2016 & [19,73,75-77,79] & [79] & [80,81] & [51,63,82,83] & & & [74,75] \\
2015 & [37,38,47,71,72] & [44] & [71] & [45,46,50] & [57,58] & & [49,62,70] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recommendation techniques.  
Figure 2: Recommendation Elements.
## 2 Materials and Methods
### Metrics for the Evaluation of PROforma Models
#### 2.2.4 Connector Interplay Metrics  
This section presents the metrics related to connectors and their interplay, in particular, connector mismatch and control flow complexity metrics. As the behavior of some connectors in PROforma depends on the result of the decisions and on the preconditions of the tasks, we have included two new metrics in this category: number of decisions and number of preconditions.  
* _Connector mismatch_, _MM_: this metric relates to the structuredness of the model, as this property implies that each split connector matches a corresponding join connector. The metric counts the number of mismatches of connector tasks, i.e., number of split connector tasks that do not have a corresponding join connector task. Since we do not have different split/join connectors, it is calculated as the difference between the sizes of both sets: \[MM=|| SCT|-|JCT||\]
* _Control flow complexity_, _CFC_: it tries to measure how difficult is to consider all potential states after a split connector. It is computed as the sum of all split connectors tasks (SCT) weighted by the potential combinations of states after the split, i.e., \(2^{d_{out}(c)}-1\) where \(d_{out}(c)\) is the number of outgoing scheduling constraints of the connector task \(c\). Notice that in our models all connectors are considered _or_-connectors, the worst case scenario for a split connector. \[CFC(G)=\sum_{c\in\mathbb{S}CT}\left(2^{d_{out}(c)}-1\right)\]
* _Number of decisions_\(S_{D}\): In some cases, the behavior of some connectors depends on the result of decisions. This metric calculates the number of nodes of the graph that correspond to PROforma decision tasks.
* _Number of preconditions_, \(S_{Precond}\): Related with the previous metric, the complexity of control flows is increased if they have preconditions to be evaluated. This metric counts the number of preconditions in the graph.
## 2 Results and Discussion
### Quantifying Compound \(\times\) Individual Matrix Effects in a Tomato RIL Population  
We trialed our approach with a tomato RIL population spiked with nine internal standards. This tomato population was selected because it had previously undergone volatile profiling using a single, non-native internal standard (2-octanone) [2]. The pairwise matrix error ranged from 8% to 56% among the 36 standard pairs evaluated (Figure 4). As with the grape population, compound pairs that showed the greatest differences in response across individuals (high values for \(\sigma_{std\ 1 std\ 2}\)) were not readily predictable from their chemistry. For example, it has been previously reported that volatiles with aromatic rings could participate in \(\pi\)-\(\pi\) interactions, decreasing their SPME response [33]. We therefore expected that internal standards with aromatic rings (e.g., [2H4]-4-ethyl phenol) should exhibit more correlated responses across individuals (lower \(\sigma_{std\ 1 std\ 2}\) values) than with nonaromatic standards. However, we observed that [2H4]-4-ethyl phenol showed similar (and relatively high) pairwise variation with respect to both straight-chain compounds (2-octanone, nonyl acetate; \(\sigma_{std\ 1 std\ 2}\) = 37-48%) and other aromatic compounds ([2H8]naphthalene, [2H10]benzophenone, [2H3]IPMP; \(\sigma_{std\ 1 std\ 2}\) = 42-49%). Conversely, [2H3]IPMP showed similar matrix effects with both a mid-chain branched alcohol (4-methyl-2-pentanol; \(\sigma_{std\ 1 std\ 2}\) = 15%) and the polyaromatic [2H8]naphthalene (\(\sigma_{std\ 1 std\ 2}\) = 8%). Overall, Compound \(\times\) Individual matrix effects in the tomato population were considerably less than those observed in the grape population, with \(\sigma_{std\ 1 std\ 2}\) values ranging from 8% to 56%. Even at the extreme case of mismatched standards ([2H4]-4-ethylphenol vs [2H4]furfurral or [2H2]-(E)-2-hexenal), the extent of the error (\(\sigma_{std\ 1 std\ 2}\) = 56%) may still be tolerable for many studies. The reason why Compound \(\times\) Individual matrix effects occur to a lesser extent in the tomato population as compared to the grape population in our study is unclear, and further research is needed to determine if this is a general phenomenon or specific to this population.  
Figure 4: Compound \(\times\) Individual matrix effects (%RSD) for pairs of nine non-native internal standards in a tomato population.  
Figure 3: Compound \(\times\) Individual matrix effects (%RSD) for pairs of six isotopically labeled internal standards in a grape population.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
## 2 Materials and Methods  
### System Architecture  
The new profitability module, namely, ProFit, is an independent module of the ifarma FMIS in terms of interface and algorithms, although it cooperates with the PreFer module of ifarma for exchanging map data. More specifically, ProFit takes spatial data from the PreFer database as input into its algorithms and returns output maps for display in the map viewer of PreFer. In this way, the original PreFer (say, v.1) is upgraded into a new version (say, v.2) after integrating with ProFit (Figure 1).  
ProFit alone comprises two components (Figure 2). A site-specific cost component, which is fed by the database of PreFer, where the precision agriculture applications are stored and displayed. A shared cost component for other (non-precision agriculture) practices, where the required records are manually entered as lump sum amounts. The site-specific cost component takes input from two kinds of spatial data: (a) the fertilizer application maps and (b) the yield maps. The former is used to calculate fertilization cost at every surface unit (of 25 m\({}^{2}\)) after the multiplication of the fertilizer's rates with its corresponding unit cost; the latter, meanwhile, is used to calculate earnings at every surface unit after the multiplication of the yield with the price of the corresponding cultivar in the market.  
The shared cost component holds the lump sum amounts per expenditure category. The distribution of these lump sum costs is then based on an empirical classification of the fields of the farm according to a degree of difficulty (or weighting factor) on a categorical scale of 1-5, with \"1\" corresponding to the easiest field and \"5\" to the most  
Figure 1: The architecture of ProFit, based on its conjunction with the PreFer module of ifarma.  
Figure 2: The structure of the profitability module in ifarma (partially functioning within the PreFer module).  
difficult for each of the agriculture practices applied (e.g., soil tillage, irrigation, weed management, etc.).  
The output data will be in two forms: (a) descriptive statistics of cost, earnings, and profit per field; and (b) profitability maps (cost, earnings, and profit maps) at a 25 m\({}^{2}\) surface unit. The calculations will be done automatically according to embedded formulas.
## 5 Conclusions  
The study investigates the spatial and temporal trends of NPP and the effects of meteorological factors on the NPP change on the Tibetan Plateau. The annual average NPP is 256 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)) in the TP. NPP increased at the rate of 2.38 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)) on the Tibetan Plateau during the period 1979-2018. The variation of the NPP primarily results from precipitation increase, followed by temperature increase and the radiation decrease. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively.  
Moreover, our study demonstrates the meteorological drivers of NPP change in the grasslands with different vegetation covers. The annual average NPP of high-coverage grasslands and low-coverage grasslands were 306 and 243 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)), respectively, and they increased at the rate of 1.97 and 2.61 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)), respectively. The NPP change of the high-coverage grasslands was mainly affected by precipitation increase (2.0 g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)), and then the temperature increase (0.36 g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)) and radiation decrease (0.45 g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)). Comparatively, the precipitation increase is the driving factor of the increased NPP of low-coverage grasslands (2.82 g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)), but the temperature increase is the negative factor (\(-0.33\) g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)).  
**Author Contributions:** M.L.: conceptualization, analysis, methodology, data resources and curation, visualization, and writing--original draft. Q.L.: funding acquisition, project administration, supervision, and writing--review and editing. M.X.: software and validation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Qinghai Provincial Science and Technology Department Major Scientific And Technological Specialties (2021-SF-A6).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis
## 2 Related Work  
With the continuous progress of DRL theory, DRL methods are increasingly used in stock prediction. Research [31] applied the combination of DL and RL to stock market trading for the first time. In the paper, it is proposed to introduce a multilayer perceptron to enhance the extraction capability of the feature recurrent neural networks. Many engineering tips for DL are given in the paper. Research [32] proposed a DRL transaction framework centered on ensemble of identical independent evaluators (EIIE). EIIE treats some kind of neural network as an integration unit, processing the historical information of each asset with a neural network of the same parameters, and ultimately integrating the results of all assets to generate the asset assignment weights. This paper also proposes efficient vector storage methods and training approaches suitable for both offline and online. Research [33] proposed a multimodal DRL method that combines CNN and LSTM as a feature extraction module and uses DQN as a decision model for trading. In the paper, stock timing information is used to generate three types of images and a module combining CNN and LSTM is used to process the images. Research [34] proposed the DeepTraderframework, adding techniques such as TCN, GCN, and spatial attention mechanisms to research [35]. Research [34] improved the feature extraction of the stock price component from temporal and spatial features, respectively, in the paper. In addition, market factors are added to the paper to calculate market sector sentiment. After people gradually realized the advantages of DRL for solving stock prediction problems, it has been discussed in many review articles. Research [36] discusses a number of issues from the perspective of DRL modelling of stock prediction problems, such as problem classification, risk assessment, environment modelling, and model selection. Research [37] summarizes some of the more effective methods. Combining these reviews, DRL-based stock prediction methods can be broadly classified into three categories: Critic-Only, Actor-Only, and Actor-Critic.  
Most previous studies have used the US stock market as the dataset, and few methods have used the Chinese stock market as the dataset. China has a large number of stockholders and huge stock data. Chinese retail investors need some stock prediction methods to help them make stable and high returns. Therefore, it is necessary to establish a method to use the Chinese stock market as the dataset. This research of ours is to use the Chinese stock market as the dataset. More and more scholars are proposing excellent stock prediction methods. However, few methods are specific to newly listed stocks. Newly listed stocks do not have past datasets and cannot be fitted or trained with past data to get stock direction. This paper is aimed at newly listed stocks for which no past data are available. The relevant parameters in this paper are shown in Table 11.
## 2 Results  
### QTL Mapping of Yield and Lodging Related Traits on Chromosome 4 in Different Environments  
The two parents SN265 and LTH showed significant differences in number of grains per panicle (GPP), number of primary branches (PBN), number of secondary branches (SBN), breaking strength of the basal internode (BS), basal culm diameter (CD), and basal culm thickness of the basal culm (CT) under different environments (Table 1). We focused on breaking strength of the basal internode (BS) in the three lodging related traits. A total of four QTLs controlling BS were identified in chromosome 1, chromosome 4, chromosome 7, and chromosome 9 and named \(qSCM1\), \(qSCM4\), \(qSCM7\), and \(qSCM9\), respectively (Figure 1). Among these four QTLs, the alleles of \(qSCM1\), \(qSCM7\), and \(qSCM9\), all had a positive effect inherited from SN265. Only \(qSCM4\) had a positive effect inherited from LTH. Furthermore, the QTLs controlling number of grains per panicle (GPP), number of primary branches (PBN), number of secondary branches (SBN), basal culm diameter (CD), and basal culm thickness of the basal culm (CT) were all detected in the region under different environments (Table 2). Further analysis revealed that no QTLs related to plant height and internode length were detected in this interval (Table 2), avoiding the effect of plant height on the analysis of lodging resistance at this locus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Shenyang**} & \multicolumn{2}{c}{**Harbin**} \\ \cline{2-5}
**Trait** & **SN265** & **LTH** & **SN265** & **LTH** \\ \hline Number of grains per panicle (GPP) & 162.07 & 126.43 & 161.07 & 128.32 \\ Number of primary branches (PBN) & 12.20 & 9.67 & 12.10 & 9.41 \\ Number of secondary branches (SBN) & 33.03 & 24.24 & 31.98 & 24.13 \\ Breaking strength of the basal internode (BS)/g & 883.09 & 366.50 & 879.25 & 367.22 \\ Basal culm diameter (CD)/cm & 4.54 & 4.10 & 4.38 & 4.06 \\ Basal culm thickness of the basal culm (CT)/cm & 1.40 & 0.58 & 1.38 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of several panicle characters and basal culm characters of SN265 and LTH in different environments.  
Figure 1: Genomic locations of four QTLs with strong effects for lodging related traits identified in the RILs population. Green indicates positive effect inherited from LTH. Orange indicates positive effect inherited from SN265. 1–12 indicates chromosome 1 to chromosome 12.