esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Agro-Climatic Mapping  
As it was mentioned in Section 4.1, the problem of discriminating several different types of crops directly from spaceborne data requires the analysis of spectral responses and phenological cycles of multi-temporal datasets with very high time-frequency and resolution. This can be solved in some way when mapping small areas, but it can be very complex, expensive and demanding if we combine large geographical scale, high per sq. km cost of data, and constraints on time and resources.  
Figure 3: Sample results on Costa Rica. Each distinct color depicts one region reputed to be homogeneous according to the clustering method described.  
Figure 2: Map of the concerned area in Central and Southern America, North up. Countries included in our analysis are represented in light blue, whereas other solid land is represented in gray and oceans in dark, bluish gray.  
The methodology described in the Section 4.2 is able to classify the territory into spectrally homogeneous areas allowing to separate areas with very different spectral responses (e.g., urban areas and forest areas) and group the ones with very similar response (e.g., the croplands with similar spectral response). However, the spaceborne mapping technique, as previously illustrated, is not able to attribute to each spectral class a specific crop type (coffee, banana, rice, etc.). Therefore, a second independent technique of classification has been implemented. The agro-climatic mapping identifies for each unit of the territory the crops that potentially could grow according to their aptitude and potential agricultural production with respect to the actual environmental conditions [32]. The approach used in this chapter focuses on the evaluation of an area of interest and according to the agro-climatic conditions required for the selected crop, in order to estimate potential areas for its cultivation. Finally, these potential areas are confined into the areas classified as croplands through public products of land use and vegetation derived from spatial sensors. A schematic illustration of conceptual agro-climatic mapping is shown in Figure 4.  
The final aim of the procedure described in this chapter is to obtain maps, for each country, where the areas classified as croplands are assigned potentials of production of certain types of crops. To carry out this mapping technique, two processes are basically performed:  
1. Definition of the agro-climatic conditions required for each crop to achieve its potential production through the regression analysis of reference data from public sources. The parameters that define the agro-climatic conditions are: (1) annual precipitation, (2) monthly temperature (minimum and maximum), (3) elevation over sea level, and (4) deaphology.
2. Estimation of crop potential areas that are those cropland areas where all the ranges of agro-climatic conditions are fulfilled. In this step, the agro-climatic parameters defined previously and the cropland classification are contained in different layers that are spatially crossed to unify all in a single element [33].
### The Importance of Spatial Audio  
[PERSON] et al. [18] suggest that the spatial properties of virtual auditory environments have been of significant importance since the introduction of stereo sound in the 1930s. Stating that spatial sound is used to simulate an auditory reality that gives the user the impression they are surrounded by a 3D virtual environment. In terms of precision, spatial acuity is somewhat inferior to vision and somatic sensory system when perceiving the environment [19]. However, it is not unimportant. Spatial hearing is a critical way in which the user perceives space, providing information about events and objects that are far beyond the field of view [20].  
Two studies conducted by [PERSON] and [PERSON] [21] investigated how three-dimensional sound influences the user experience within a stereoscopic virtual environment (VE). The first compared a silent VE to environments with spatial sound. The second was a comparison between two environments with spatialized and non-spatialized sound sources. The results indicated that spatialized audio significantly increased the feeling of presence, with interactive sounds to be originating from their visual cues perceived as more realistic.  
To perceive the real world, the human brain must decode a constant stream of multi-modal information from the various sensory channels [22]. An example of which is most cases of face to face communication. The brain must interpret visual information such as moving lips and facialexpressions, while also listening to localized speech sound from the mouth [23]. This cross modality synchronization can also be echoed in VR by using 3D audio, by simulating the shared spatial qualities of both audio and visual stimuli that occupy the same time and space.
## 3 Results
### Development of the SQI\({}_{u}\)
It is interesting to note that, when adding the lowest dose of MPs (0.5% w w\({}^{-1}\)), there was a decrease in the soil quality (\(SQI_{u}\) = 0.21) compared to the control treatment (\(SQI_{u}\) = 0.23) (Supplementary Table S1), although this was not significant (\(p>0.05\)). On the other hand, treatments T3 and T4 presented higher values with respect to the control, having an \(SQI_{u}\) of 0.26 and 0.32, respectively (Supplementary Table S1). This indicates that, at low concentrations of MPs (0.5% w w\({}^{-1}\)), there may be a negative effect on the quality  
Figure 10: Correlation bi-graph between PC1 and PC2. N-NH\({}_{4}\)\({}^{+}\), ammonium; N\({}_{\rm min}\), mineralizable N; and C-CO\({}_{2}\), carbon dioxide. of soil, whereas at high concentrations (\(>1\%\) w w\({}^{-1}\)), certain processes that increase the quality of soil may be favored. Finally, it is important to mention that there is little or no information about _SQIs_ developed for soils with MPs and monitored through C and N dynamics over time. The results obtained in the present study may be a turning point for future research related to soils with MPs, since the proposed indicators are not only extremely important for soils, but are also rarely used or have not been used previously in this type of study. Furthermore, the resulting \(SQI_{u}\) proved to be a useful tool to determine soil quality by reducing the number of indicators used. Therefore, it would be worth considering the use of this tool for other types of soils and MPs. It is known that the effect of MPs can vary with different soil textures, as well as with different MPs due to their type, size, shape, and concentration. Having an \(SQI\) for different conditions could help to generalize appropriate strategies for dealing with these MPs. On the other hand, since the present study was 90 days long at the microcosm level, it is important to transfer this type of study to the field level, so as to consider a longer period and obtain results closer to those found in agricultural soils.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 2 Materials and Methods  
### Data  
The assimilation-prediction experiment was conducted using MLDAS at the AmeriFlux Mead (US-Ne1) site, which is located at the University of Nebraska Agricultural Research and Development Center. There are 3 sites (US-Ne1, US-Ne2, and US-Ne3) over Mead which are within 1.6 km of each other. Site 1 (US-Ne1), used in our study, is an irrigated site covered by corn [19].  
Meteorological forcing data of the Mead site, including air temperature, pressure, humidity, precipitation, wind speed, incoming shortwave and longwave radiation used to drive Noah-MP, were downloaded from AmeriFlux ([[http://ameriflux.lbl.gov/](http://ameriflux.lbl.gov/)]([http://ameriflux.lbl.gov/](http://ameriflux.lbl.gov/)), accessed on 7 October 2021). LAI and SM were also acquired from the AmeriFlux database. SM was measured at depths of 0.02, 0.05, and 0.1 m, which was averaged to be consistent with the topsoil layer of Noah-MP (0.1m) [17]. The remotely-sensed solar-induced chlorophyll fluorescence (SIF) products with the spatiotemporal resolution of 5 km and 4 days were available via [[https://osf.io/8](https://osf.io/8) xqy6/?view_only=4d14a6 db7 be644d-39564c82a371e1d20]([https://osf.io/8](https://osf.io/8) xqy6/?view_only=4d14a6 db7 be644d-39564c82a371e1d20) (accessed on 1 November 2021) [20].
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 5 Discussions  
The strategy of establishing a communications network without any voids by deploying drones in disaster affected areas at optimal heights with a view to minimizing the number of drones and maximizing the average leftover energy is a unique approach. In this section, we compare our approach with other methods available in the literature which are closely related.  
[PERSON] et al. [8] presented an idea for a bush fire mitigation system using drones, specifically, for the 2019-2020 bush fires in New South Wales, Australia. Using a swarm of drones to monitor the bush fire, they developed a method based on PSO to obtain the shortest path a drone has to travel to the target area, thereby ensuring that it uses the maximum hovering time and the minimum travel time. The drone travels back to the sink station after the monitoring is over. Here, our objective is different, as we want to continuously provide communication facilities until the rescue operation is over. Furthermore, we have to ensure that every single point in the affected region is under network coverage, which cannot be guaranteed by the above approach.  
[PERSON] et al. [45] proposed the idea of establishing in-network connectivity between drones used for intelligent traffic management and damage assessment in post-disaster scenarios. They use a connectivity maintenance heuristic that causes changes in the formation of drones in response to the service requests. It focuses on a planar surface, whereas in our case the algorithm considers terrain types with different obstacles at different heights. Furthermore, the above paper does not pay very much attention to energy conservation, while our method focuses on maximizing the average leftover energy by placing the drones at different heights. Moreover, the above method concentrates on connectivity maintenance between drones, while our technique relies on quick establishment of communications between the base stations and end users, with the drones in between, which ensures that there are no network voids in the disaster-struck terrain.
## 5 Application: IoT Temperature Time Series  
We apply the TP-NARX and TP-RLARX models to perform impact analysis on temperature time series. First, Section 5.1 describes the IoT sensor data and the objective of the intervention impact analysis. Section 5.2 shows the performance for each model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposed Contributions** & **Summary** & **Findings** \\ \hline \multirow{4}{*}{TP-NARX} & Extends the GP-NARX model & 1. Gain robustness of a \\  & to the Student-\(t\) likelihood & heavy-tailed likelihood \\  & in order to accommodate & without increasing computational \\  & heavy-tailed noise and outliers & speed relative to GP-NARX \\ \hline \multirow{8}{*}{TP-RLARX} & Extends the GP-RLARX by & \\  & substituting the & \\  & latent GP prior with a & 1. Gain robustness of a heavy-tailed \\  & Student-\(t\) process prior. & latent state with minor increase \\  & Proposed method is now & to computational speed relative to \\  & robust to heavy-tailed noise & GP-RLARX \\  & at the observational and & 2. TP-RLARX has performance at \\  & latent levels. & least as good as GP-RLARX on \\  & Derived the ELBO for & intervention analysis task. \\  & this proposed model & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of contributions and findings.  
on a number of different forecasting metrics and shows some example forecasts. Finally, Section 5.3 presents detailed results from the impact analysis and a thorough interpretation of the findings. We compare the TP-NARX and RLARX approaches with their Gaussian process counterparts.
# Effects of Table Grape Cultivars on Fruit Quality and Aroma Components
## 3 Results and Discussion
### Aroma Profiles of Different Table Grape Varieties
The contents of terpenes in _Zaoheibao_ and _Manicure Finger_ were more than 1000 \(\upmu\)g/L. In addition, the terpene contents in _Heibaladuo_, _Aishenmeigui_, _Zhengyanwueh_, _Ruby Seedless_, _Rudiuvuheyi_, _Jumeigui_, and _Shine-Muscat_ were between 800 and 1000 \(\upmu\)g/L. The difference in aroma between different grape varieties can be attributed to genetic variation in aroma biosynthesis genes. For example, an allelic variant of 1-deoxy-d-xylose-5 phosphate synthase and a terpenoid biosynthesis gene can cause the accumulation of terpen in the Muscat grape [32]. Cis-(trans-) rose oxide, eugenol, linalool, and citronello had a low threshold of odor inactivation, which contributed to the overall aroma characteristics of the grape. The main terpene compounds were citronello!, neral, linalool, and \(\upalpha\)-terpineol, followed by Geranial cis-Linalool oxide, neral, and (Z)-Limonene. The content of cis-(trans-) rose oxide, 4-terpinene, \(\upbeta\)-terpinene, and hotrienol were lower; because of their very low odor thresholds, linalool and geraniol are generally considered to be the two major flavor contributors to Muscat [15]. Recent studies have shown that monoterenes play a role in the prevention and treatment of various diseases, including cancer [5]. In particular, linalool is an antioxidant, anti-inflammatory, and cardiovascular stimulant [5; 33], and geraniol can inhibit the growth of HEPG2 human hepatoma cells [17; 34]. C13-isoprene is a carotenoid aromatic substance. Due to the distinct carotenoid species, regional characteristics, and extremely low threshold of odor inactivation, it plays an important role in the characteristic aroma of a wine. The C13 isoprene detected in this study included \(\upbeta\)-damascenone, \(\upbeta\)-Ionone, and geranylacetone. The content of C13 isoprene was low in the total content of all aroma components (lower than 1 \(\upmu\)g/L), and its distribution was more uniform in all varieties. C13 isoprene compounds were detected in all varieties except _Rudiuvuheyi_. The \(\upbeta\)-damascenone and \(\upbeta\)-Ionone had distinct sweet fruit and floral aromas, and their deactivation thresholds were less than 0.005 \(\upmu\)g/L. Therefore, \(\upbeta\)-damascenone and \(\upbeta\)-Ionone contributed significantly to the overall aroma characteristics of the grape.
## 3 Results
### Land-Use Change and Spatial Characteristics
#### 3.1.2 Characteristics of Land-Use Transfer Analysis  
The land-use transfer matrix method was used to analyze changes in land-use types prior to and after the implementation of the project, yielding 2 land-use transfer matrix tables, representing 1990-2000 and 2000-2020, respectively (Tables 3 and 4). With 2000 as the dividing point, the transfer-in and transfer-out of land-use types changed considerably.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Land-Use Types**} & \multicolumn{4}{c}{**Area (ha)**} & \multicolumn{4}{c}{**Change Rates (\%)**} \\ \cline{2-9}  & **1990** & **2000** & **2010** & **2020** & **1990–2000** & **2000–2010** & **2010–2020** & **1990–2020** \\ \hline Farmland & 7,182,895 & 7,196,119 & 6,826,601 & 6,666,814 & 0.02 \(\uparrow\) & \(-0.51\downarrow\) & \(-0.23\downarrow\) & \(-0.18\downarrow\) \\ Forestland & 4,616,388 & 4,635,906 & 4,803,465 & 4,833,057 & 0.04 \(\uparrow\) & 0.36 \(\uparrow\) & 0.06 \(\uparrow\) & 0.12 \(\uparrow\) \\ Grassland & 7,715,601 & 7,777,948 & 7,889,430 & 7,836,053 & 0.08 \(\uparrow\) & 0.14 \(\uparrow\) & \(-0.07\downarrow\) & 0.04 \(\uparrow\) \\ Water areas & 186,686 & 181,408 & 180,463 & 178,801 & \(-0.28\downarrow\) & \(-0.05\downarrow\) & \(-0.09\downarrow\) & \(-0.11\downarrow\) \\ Built-up land & 276,517 & 315,211 & 430,337 & 527,762 & 1.40 \(\uparrow\) & 3.65 \(\uparrow\) & 2.26 \(\uparrow\) & 2.27 \(\uparrow\) \\ Barren land & 615,548 & 487,051 & 463,445 & 447,936 & \(-2.09\downarrow\) & \(-0.48\downarrow\) & \(-0.33\downarrow\) & \(-0.68\downarrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Land-use change in Shaanxi Province from 1990 to 2020.  
Figure 2: Proportions of land-use types in Shaanxi Province from 1990 to 2020.  
From 1990 to 2000, the area of farmland converted to grassland in Shaanxi Province was the largest, at 55,325.07 ha, followed by built-up land that was converted to farmland and forestland that was converted to grassland, at 35,707.32 ha and 25,334.28 ha, respectively. Between 2000 and 2020, the reciprocal transfer of farmland, forestland, and grassland was visible, and the shift was considerable due to the GGP and the Natural Forest Protection Project. Water areas, barren land, and built-up land converted to farmland also increased, indicating that Shaanxi Province has developed some other types of land to make up for the decrease in farmland caused by the project's implementation. The increase in conversion to built-up land in all regions indicates economic development and accelerated urbanization.
# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
## 5 Conclusions  
Our adaptive LF contrast enhancement method uses an efficient hierarchical training structure based on MLP, and the hierarchical structure improves the parameter acquisition accuracy to enhance the contrast of LF images. The experimental results show that it is superior to existing methods for facilitating image processing and appropriately enhancing the global contrast without losing details. Meanwhile, the MLP network achieves adaptive LF enhancement, which is user friendly and real time. Our architecture can be used to implement real-time enhanced portal devices with limited hardware resources.  
Light field images have wide application prospects, such as Google's development of LF immersive video calls [53], Samsung's development of a slim-panel holographic video display [54], and Light Field Lab's SolidLight that offers a true holographic video display [55]. In the future, we would like to enhance the scalability of our approach. For example, the FS formed by our method currently has a low resolution (624 \(\times\) 432), and we want to enhance the image contrast while increasing the resolution. Because low-light LF images lack information expression and produce more noise due to the dark shooting light, we hope to enhance the information expression by improving the contrast of the low-light field image and denoising. In the future, we will implement light field composite enhancement suitable for various scenes.  
**Author Contributions:** Conceptualization, X.G. and Z.Y.; methodology, X.G.; software, X.G.; investigation, X.G. and J.G.; resources, Z.Y.; data curation, X.G.; writing--original draft preparation, X.G. and J.G.; writing--review and editing, Z.Y. and Y.C.; supervision, Z.Y. and Y.C.; project administration, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Shanxi Province, grant number 202102150401011, and the National Natural Science Foundation of China, grant number 52275568.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author would also like to thank [PERSON] for his proofreading comments during the review process of the paper and for his help with the related calculations in the machine learning part.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Spatial Adaptive Feature Modulation Mechanism (SAFMM)
#### 2.3.1 Space Adaptive Feature Modulation Unit (SAFM)  
The SAFM module utilizes non-local information to adaptively modulate features, thus allowing the model to select the most appropriate modulation for each pixel location, enhancing adaptation to different regions. To introduce the ability to interact over long distances and model dynamics in convolution, a multi-head paradigm is followed using parallel and independent computations that allow each head to process different scales of information about the input, and then these features are aggregated to generate an attention map for spatially adaptive feature modulation.  
The detailed structure of SAFM is shown in Figure 6. The normalized input features are first divided into four groups and then fed into the Multi-Scale Feature Generation Unit (MFGU), which performs a \(3\times 3\) deep convolution for the first group and merged sampling for the rest. Given the input feature \(X\), the formula for this process is shown in Equation (10):  
\[\begin{array}{l}[X_{0},X_{1},X_{2},X_{3}]=Split(X),\\ \hat{X}_{0}=DW-Conv_{3\times 3}(X_{0}),\\ \hat{X}_{i}=\uparrow_{p}(DW-Conv_{3\times 3}(\downarrow_{\frac{p}{2^{s}}}(X_{ i}))),1\leq i\leq 3.\end{array} \tag{10}\]  
where \(Split(\cdot)\) corresponds to the channel segmentation operation, \(DW-Conv_{3\times 3}(\cdot)\) is a \(3\times 3\) deep convolution, \(\uparrow_{p}(\cdot)\) denotes the fast implementation of up-sampling a specific level of features to the original resolution \(p\) via nearest neighbor interpolation, and \(\downarrow_{\frac{p}{2^{s}}}\) denotes the pooling of input features to the size of \(\frac{p}{2^{s}}\).  
Figure 5: SAFM structure diagram.  
These extracted short- or long-range features are connected via channel dimension, and \(1\times 1\) convolution is performed to aggregate them. This is shown in Equation (11) below:  
\[\hat{X}=Conv_{1\times 1}\big{(}Concat\big{(}[\hat{X}_{0},\hat{X}_{1},\hat{X}_{2}, \hat{X}_{3}]\big{)}\big{)} \tag{11}\]  
where \(Concat(\cdot)\) denotes a cascade operation and \(Conv_{1\times 1}(\cdot)\) is a \(1\times 1\) convolutional.
## 4 Experiments and Results
### Comparison to Other Models  
Python libraries enable the deployment of many traditional models. Scikit-learn is an open-source machine learning library that supports supervised and unsupervised learning. It offers several tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.  
Figure 14: MAE vs. different network topologies.  
We compared our proposal with three well-known models provided by the scikit-learn library, which support multiclass classification. These classifiers are decision tree, k-nearest neighbor (kNN), and random forest.  
We also employed 10-fold cross validation for the evaluation and comparison of these machine learning algorithms. Table 10 shows the results achieved by our deep neural network model and these traditional models.  
The analysis shows that our proposed DNN model achieves the highest accuracy, precision, recall, and F1-score of 92.7%, 92.9%, 92.7%, and 92.7%, respectively, compared to traditional machine learning models. We also observe that, of the traditional models, decision tree achieves the highest accuracy, precision, recall, and F1-score of 89.2%, 87%, 88%, and 87.5%, respectively, followed by k-nearest neighbor, with an accuracy, precision, recall, and F1-score of 59.3%, 65%, 68%, and 66.4%, respectively. We can conclude that our proposed algorithm outperforms other traditional machine learning algorithms and is able to perform beach attendance predictions adequately.