esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
###### Abstract  
Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 \(\times\) 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfred ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.  
irrigated and rainfed areas; growth characteristics; phenology; support vector machines 1
## 4 System Architecture
### Scope and Context
#### 4.1.3 Definition of System Requirements  
Table 2 shows a summary of the set of requirements to be addressed to satisfy the needs of the proposed scenarios. It does not attempt to provide a complete list of requirements.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 4 Experiments
### Experimental Results
#### 4.3.1 Comparison between the Edge and Cloud Servers  
The first experiment compared a cloud server and an edge server with a traditional AR service. The AWS cloud servers located in Singapore, Virginia, and Seoul were used in this experiment. The experiment measured the AR content loading times of the cloud and edge servers. The experiment was performed ten times, and the mean and standard deviation were calculated.  
Figure 8 shows the load time of the AR service for each server. The number of attempts denotes the number times each experiment was performed. Table 3 lists the average latency and standard deviation of the data in Figure 8. The AR content load time was 4329.50 ms when the edge server was used; it exhibited faster load times than that of cloud servers. Further, the standard deviation was stable at 170.31 ms. The edge server reduced delays and was 14.49% faster than the cloud server. Further, stability increased by 4.54% when using the edge server, and this showed that latency and stability were excellent because of the reduction in nodes when the physical distance between the server and client was reduced.  
Figure 8: AR content load on the cloud server.
# Depth Estimation of a Deformable Object via a Monocular Camera
## 2 3D Labeled Reconstruction
### 3D Reconstruction with Multiple Images  
Given a static object, we can use a single camera (whose intrinsic parameter is \(f\)) to circle around the object, reconstructing a point cloud. In order to reconstruct the object, the total number of the images captured by the camera is \(N\). Additionally, the total number of points on the object surface is \(M\). The orientation and position of the camera with regard to the world frame at the \(i\)th instant can be respectively represented by a matrix \(R_{i}\) and a vector \(t_{i}\). Denote \(P_{j}=\left(\begin{array}{ccc}X_{j}&Y_{j}&Z_{j}\end{array}\right)^{T}\) as the \(j\)th point on the object surface with regard to the world frame, \(P_{j}^{i}=\left(\begin{array}{ccc}X_{j}^{i}&Y_{j}^{i}&Z_{j}^{i}\end{array} \right)^{T}\) as the same \(j\)th point with regard to the camera frame at the \(i\)th instant, and \(m_{j}^{i}=\left(\begin{array}{ccc}x_{j}^{i}&y_{j}^{i}\end{array}\right)^{T}\) as the image coordinate of \(j\)th point at the \(i\)th instant (for denotation simplification, we define \(m_{j}^{i}\in\mathcal{O}\) if \(P_{j}\) is occluded under the observation by the camera at \(i\)th instant). The following can be given [20]:  
\[\left[\begin{array}{c}P_{j}^{i}\\ 1\end{array}\right]=\left[\begin{array}{ccc}R_{i}&t_{i}\\ 0&1\end{array}\right]\left[\begin{array}{c}P_{j}\\ 1\end{array}\right], \tag{1}\]  
\[Z_{j}^{i}\left[\begin{array}{c}m_{j}^{i}\\ 1\end{array}\right]=\left[\begin{array}{ccc}f^{i}I_{2\times 2}&O_{2\times 1}\\ O_{1\times 2}&1\end{array}\right]P_{j}^{i}. \tag{2}\]  
Let  
\[v_{j}^{i}=Z_{j}^{i}m_{j}^{i}-\left[\begin{array}{cc}f^{i}I_{2\times 2}&O_{2 \times 1}\end{array}\right]\left[\begin{array}{cc}R_{i}&t_{i}\end{array} \right]\left[\begin{array}{cc}P_{j}^{T}&1\end{array}\right]^{T}. \tag{3}\]Subsequently, the desired result in this step is the 3D reconstruction \(\left\{P_{j}^{*}\right\}_{j=1...M}\) in the following form:  
\[\left\{P_{j}^{*}\right\}_{j=1...M}=\arg\min_{\left\{p_{j}\right\}_{j=1...M}}\sum _{i}^{N}\sum_{j}^{M}\left\|v_{j}^{i}\right\|. \tag{4}\]
## 3 Conceptual Frameworks
### KETs  
Key enabling technologies have been defined as knowledge-intensive technologies associated with high-intensity research and development, rapid innovation cycles, high capital expenditure, and high-skilled employment [114]. They can also be classified as advanced manufacturing technologies, advanced materials and nanotechnologies, life-science technologies, artificial intelligence, micro/nano electronics and photonics, and security and connectivity tools.  
Examples where KETS have a place include those of an organisational type (fractal, holonic, or bionic organisation), digital (cloud, big data, AR&VR, wearables, and mobile devices) and technological type (collaborative robots, additive manufacturing, etc.) [115] Data generation, data analytics and decision-making technologies are examples of KET-artificial intelligence. Human-machine interfaces and cyber-physical systems provide examples of KET-security and connectivity [116; 117].  
Information and communication technology (ICT) and KETs challenge traditional production structures and require the search for alternative and innovative solutions to those proposed so far, through the use of disruptive activities and competencies by Operator 4.0. This requires engineers with the ability to solve problems based on different outlooks rather than those already raised by cultural heritage. At this point, the third generation of activity theory becomes useful by allowing the transcending of established frameworks and by generating creative and innovative solutions that break with the mental structures of established systems, thereby providing solutions to the new challenges that arise.  
KETs are used as variety filters and amplifiers to support DfHFinI4.0. Connectivism, together with the law of requisite variety, activity theory and KETs, help SCMS modelling, and enable real-time support and assistance for the engineer, through the design of dynamic interfaces that co-evolve according to needs considered in engineering environments 4.0, which are characterised by the digitisation and virtualisation of products and processes. This involves an impact on improvement in the acquisition and training of competencies, as well as on the affective connectivity established between human and technological factors.
## 4 Results and Analysis
### Average Weight
Then, any parameter in the table with a \(p\)-value lower than 0.1 is an important parameter that affects the average weight, because the level of significance is set to 0.1. Table 7 shows the effective parameters with a shaded background, and the results are exactly in accordance with those of the effects plots shown in Figures 6-8. The column 'Effect' denotes the vertical distance between two points in Figures 6 and 7. The column 'Coefficient' is the same as half of the column 'Effect', and the values are the coefficients in the regression equation. The column '\(t\)' is obtained by dividing the 'Coefficient' with the 'SE Coefficient'. The values of column '\(t\)' are shown in the Pareto chart in Figure 8. A high magnitude of '\(t\)' indicates that the parameter displays a high effect. Thus, it is concluded that the most effective method to minimize the weight of a single jetted dot involves reducing the needle stroke and the open time, and this significantly affects the average weight with a high \(t\)-value. In Table 6, a run order of 1, 2, 5, and 6, with low needle stroke and low open time displays a lower average value when compared with other run orders. The average values for the run orders 9, 10, 13, and 14 slightly increased because of the higher needle stroke when compared to the run orders 1, 2, 5, and 6. However, the average values for the run orders 3, 4, 7, and 8 sharply increased because of the higher open time when compared to the run orders 1, 2, 5, and 6. The effects and estimated coefficients are reinterpreted with only the meaningful parameters, and the result is depicted in Table 8. The coefficients in the third column in Table 8 are used to express the regression model to estimate the average weight of a single dot as follows:  
\[Y_{average\_weight}=4.01+0.26\times\mathrm{A(Stroke)}+0.03\times \mathrm{B(Rising\ Time)}+0.40\times\mathrm{C(Open\ Time)} \tag{1}\] \[+0.07\times\mathrm{A(Stroke)}\cdot\mathrm{C(Open\ Time)}+0.04 \times\mathrm{A(Stroke)}\cdot\mathrm{D(Cooling\ Pressure)}\] \[-0.03\times\mathrm{C(Open\ Time)}\cdot\mathrm{D(Cooling\ Pressure)}\]  
Figure 5:0 Pareto chart for the average weight.
# A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem
###### Abstract  
Firefly algorithm is among the nature-inspired optimization algorithms. The standard firefly algorithm has been successfully applied to many engineering problems. However, this algorithm might be stuck in stagnation (the solutions do not enhance anymore) or possibly fall in premature convergence (fall into the local optimum) in searching space. It seems that both issues could be connected to exploitation and exploration. Excessive exploitation leads to premature convergence, while excessive exploration slows down the convergence. In this study, the classical firefly algorithm is modified such that make a balance between exploitation and exploration. The purposed modified algorithm ranks and sorts the initial solutions. Next, the operators named insertion, swap and reversion are utilized to search the neighbourhood of solutions in the second group, in which all these operators are chosen randomly. After that, the acquired solutions combined with the first group and the firefly algorithm finds the new potential solutions. A multi-echelon supply chain network problem is chosen to investigate the decisions associated with the distribution of multiple products that are delivered through multiple distribution centres and retailers and demonstrate the efficiency of the proposed algorithm.  
Keywords:firefly algorithm; premature convergence; stagnation; supply chain optimization +
Footnote †: journal: Applied Mathematics and Applications
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
#### 4.1.10 Ensemble Methods  
A set of classification models that are trained separately, and the predictions of those models that are then integrated in a way to give the overall prediction, is known as a classifier ensemble [63]. The following ensemble learning-based classification methods are the most widely used: bagging, blending, gradient boosting machines, random forest, and boosting. Within the application sector of smart homes, ref. [79] developed a cluster-based ensemble approach solution for activity recognition. This method models activities as cluster collections constructed from various feature subsets. By combining the prediction output of various classifiers that make up the ensemble using genetic algorithm optimization, ref. [78] suggested an ensemble classifier approach for activity recognition in smart homes. For activity recognition, they used the ANN, HMM, conditional random field, and SVM [80] as basis classifiers. A deep LSTM ensemble method was put out by [78] for activity recognition utilizing wearables: Specifically, the authors created improved LSTM network training processes and suggested combining sets of LSTM collectives comprised of various learners.
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 4 Conclusions  
In order to achieve autonomous driving and path-tracking of vehicles in complex standardized orchards that involve high noise and interference between rows of fruit trees, an automatic vehicle navigation method based on 2D lasers was developed for standardized orchards under complex disturbance environments. This study presents a filtering algorithm based on Euclidean clustering algorithms by analyzing different types of orchard interference points. The algorithm can effectively eliminate noise generated by small disturbances in the orchard by setting different thresholds, thus greatly improving positioning accuracy. Considering prior knowledge of plant spacing, row spacing and trunk diameter, a topographic map of orchard trees' center was extracted and a target navigation path with higher precision was fit, based on least square methods and geometric linear fitting. It reduces the computation of data processing and complex mathematical models. At the same time, in the pure tracking model, fuzzy control was used to realize the adaptive adjustment of the forward-looking distance. Additionally, the orchard 2D laser automatic navigation system based on agricultural electric vehicle was designed, and its robustness and control accuracy were verified by experiments. This study satisfies the precision requirements of orchard vehicle automatic navigation and provides guidance  
Figure 18: Variation of average deviation of lateral deviation and walking distance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Maximum Deviation**} & \multirow{2}{*}{**AVG Deviation (m)**} & \multirow{2}{*}{**SD Deviation (m)**} \\  & **(m)** & & \\ \hline
1 & 0.09 & 0.05 & 0.05 \\
2 & 0.13 & 0.08 & 0.04 \\
3 & \(-\)0.07 & \(-\)0.04 & 0.03 \\
4 & \(-\)0.10 & 0.04 & 0.03 \\
5 & 0.09 & \(-\)0.03 & 0.02 \\ MAD (m) & 0.096 & 0.048 & 0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Navigation field test results.  
and suggestions for the industrial application of laser navigation with high precision and low cost in the standardized orchard planting industry.  
Methodology, S.Z. and C.G.; validation, Z.G. and A.S.; formal analysis, S.Z. and J.C., writing--original draft preparation, S.Z.; writing--review and editing, J.C. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (2017 YFD0700400-2017 YFD0700402-2) and the Shaanxi Key Research and Development Project (2018 NY-160).  
The authors declare that there was no commercial conflict of interest in this study.
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
## 3 Results and Discussion  
### Nanoparticle Production: Photothermal Effect and Laser Decomposition  
In this work, the laser served as a focused, potent energy source to accelerate the breakdown of ZnO in a DI water medium. The precursor solution began quickly evaporating within seconds of the exposure to the laser beam at 60 watts. Furthermore, fumes were formed from the reaction zone within the Perspex box, and a white condensed circle was visible on the substrate's surface after irradiation, as shown in Figure 1b.c. Note that liquid water can absorb infrared radiation from a CO\({}_{2}\) laser. The complex and rapid heating event is due to the laser's Gaussian intensity distribution, which induces a Gaussian spatial temperature distribution. Two related phenomena occur when a laser beam is applied to a thin layer of liquid owing to the local increase in temperature [20]: (i) the heat will alter the liquid's refractive index, resulting in thermal defocusing that enlarges and modifies the shape of the beam, (ii) this optical effect is accompanied by a change in the surface tension concerning the liquid layer, inducing thermocapillary. The local increase in temperature produces two effects: a rapid liquid flow away from the laser beam's center and the formation of a depression surrounded by a ridge in the liquid layer. These effects are accompanied by the evaporation of the solvent, which induces a concentration gradient. When the precursor crystals in the solution react, convection eventually changes from thermocapillary to concentration convection, resulting in nucleation of ZnO (or Ce/ZnO) [21]. In laser decomposition, NPs are believed to be formed by the rapid condensation of reactant species in the liquid phase. Laser light's penetration depth is proportional to its wavelength, with longer wavelengths penetrating deeper into the medium [22]. This results from the laser light's thermal effect, which increases with increasing wavelength. The photon energy must be compatible with the energy gap between the excited and ground states to allow for electronic transition. Here, the photon energy of infrared light is insufficient for electrons to achieve higher excited states. Consequently, only transitions between vibrational levels are observed, and the energy loss from these levels occurs as heat due to the photothermal effect. Laser irradiation induces a non-equilibrium state that enhances the formation of nanostructures from precursor molecules. This process cannot be accomplished with the same oven temperature. Hence, locally heating the sample with a CO\({}_{2}\) laser results in the potential hemolysis or dissociation of multiple bonds [23].
## 3 Results and Discussion
### Pyrolysis of Sugarcane Bagsase
Based on the information obtained from XRD patters and the Rietveld analysis (Figure 9), it can be inferred that during pyrolysis, 87% of the CuO was reduced to Cu\({}_{2}\)O and Cu. This reduction can be attributed to the presence of CO which is part of the non-condensable products during the pyrolysis [36; 37; 38; 39; 40; 41]. Therefore, Cu\({}_{2}\)O and Cu have a lower capacity to transform microwaves into heat energy than the CuO compound; this explains why the reaction progress is reduced after 4 min of irradiation (Figure 6). The morphology of CuO was analyzed before and after the pyrolysis. The scanning electron microscopy of CuO MW (Figure 10a-c), CuO US (Figure 10d-f), and the carbon-CuO US mixture (Figure 10g-i) were performed. The carbon-CuO US mixture was obtained after the pyrolysis which promoted the higher liquid fraction. A nanoleaves morphology with different sizes is observed in Figure 10a-c [42]. Details of the morphology are appreciated in micrograph b, where the zone marked with a circle shows the growth of this morphology through the formation of dendrites. At 50000\(\times\) (c), it can be seen that these structures have porosity and the existence of a monolithic phase in a smaller proportion. The synthesized sample by ultrasound irradiation (Figure 10d-f) has a different morphology when compared with the one obtained by microwaves. In this case, the faceted and agglomerated monoliths of different sizes and shapes (d) are presented. Micrograph (e) depicts a growth based on dendrites (circled area) similar to that found by microwaves. It should be mentioned that in general,  
Figure 9: XRD patterns of cane bagasse + CuO and the carbonaceous residue obtained after pyrolysis. Percentage of the crystalline phases: CuO, Cu\({}_{2}\)O, and Cu found in the carbonaceous residue. the particle size is larger than the one obtained by microwaves. In micrograph f some porous areas and details of this morphology are identified. The difference in morphology of the CuO synthesized with ultrasound (facetted monoliths) or microwave (nanoleaves) treatment is due to the topotactic effect. This indicates that the synthesis method modifies the shape and size of Cu\({}_{\text{x}}\)O\({}_{\text{y}}\)H\({}_{\text{z}}\) in the nucleation and growth during the crystal formation. This morphology is preserved in the resulting oxides after the calcination [43]. The carbon-CuO US mixture obtained after performing the pyrolysis (Figure 10g-i), shows some Cu species agglomerates on the surface of the char (f). At 20,000\(\times\) (h) there is a homogeneous dispersion of Cu species on the char. It should be noted that there was a change from monolithic faceted morphology to quasispherical particles and a size reduction in CuO ((e), 20,000\(\times\)) when reduced to Cu\({}_{2}\)O and Cu ((h), 20,000\(\times\)). The micrograph (i), which has the same magnification but is using a backscattered electron detector, corroborates that the dispersed and agglomerated material corresponds to the Cu species.