esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Global climate changes have increased the imbalance of water resources, especially in northern China, which comprises typical arid and semiarid regions. Large-scale afforestation has been implemented over the past three decades in northern China. The ecosystem water use efficiency (WUE) connects the carbon cycle and water cycle of the terrestrial ecosystems and is defined as the ratio of the gross primary productivity (GPP) to the evapotranspiration. However, there are still an insufficient number of studies on the impact of the afforestation on the WUE. In this study, we applied the random forest (RF) model to explore the impacts of climate and nonclimate factors on the WUE in northern China. The results showed that in areas with high precipitation, the forests had the highest WUE, while in the arid areas, the croplands had the highest WUE. Of the total area, 44.34% showed a significant increase, and 5.89% showed a significant decrease in the WUE from 1982-2015 in northern China. The main driving factors for the changes in the WUE were climate factors, including the precipitation, temperature and solar radiation, which contributed to approximately 84% of the WUE trends, while human activities, such as afforestation, contributed to approximately 16% of the WUE trends. Overall, although the climate had a larger impact on the WUE dynamics than the human activities, our results suggested that the impacts of the afforestation programs on forest carbon and water cycles should be considered in the context of climate change.  
WUE; spatiotemporal dynamic; afforestation; random forest; GLASS; MODIS +
Footnote †: journal: Remote Sens. **2019**, _11_, _2766_; doi:10.3390/rs11232766
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 7 Experiment Result and Analysis
### Experiment Settings  
Figure 12 illustrates a fat-tree topology of our SDDC testbed. It consisted of ten HP 2920 OpenFlow-enabled switches, a HP 1820 switch, and six HP workstations (five workstations for setting OpenStack environment and the other for ONOS controller). HP 2920 OpenFlow-enabled switches were used for connecting data network of OpenStack, and HP 1820 switch was used for connecting control and management networks of ONOS and OpenStack. The workstations used for the OpenStack controller node and ONOS SDN controller have an Intel Xeon W3565 3.2 GHz CPU, with 48 GB RAM, and 1 TB SSD. The workstations used for the compute nodes have an Intel Xeon E-5 1680v4 3.4 GHz CPU, with 128 GB RAM, and 1 TB SSD. The OpenStack environment was built with a controller node and four compute nodes.  
Additionally, 30 VMs were created on the compute nodes, and their resources were randomly set from resources in Table 4. The VMs were divided into three dependency groups, in which each group consisted of ten VMs. Data rates between VMs in the same dependency group were assigned randomly according to a Gaussian distribution with a mean of 1024 Mbps and an SD of 100 Mbps; the data rates between VMs in different dependency groups were randomly assigned according to a uniform distribution with minimum of 0 Mbps and maximum of 100 Mbps.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **CPU (Cores)** & **Memory (MB)** & **Storage (GB)** \\ \hline m1.tiny & 1 & 512 & 1 \\ m1.small & 1 & 2048 & 20 \\ m1.med & 2 & 4096 & 40 \\ m1.large & 4 & 8192 & 80 \\ m1.xlarge & 8 & 16,384 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resource types for experiment VMs.  
Figure 12: Experimental setup of SDDC testbed.
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 3 Results and Discussions
### Analysis of Error Sources  
While the results at a dataset level are important on their own, without an attentive analysis of the distribution of errors across the dataset, some information would be missing. This is where the confusion matrix comes in to give a more accurate idea of what classes are the main sources of errors.  
When examining the confusion matrix (Figure 7) for one of the datasets, SAU-LI, a clear pattern emerges. We can see a higher confusion for some pollen types, with the others having the error more uniformly spread out. We can also see that most of the errors in the classification regarded just four classes: Alnus, Corylus, Betula and Quercus.  
Figure 6: Performance of the combined model before (**a**) and after (**b**) hyperparameter search. The black set represents the entire dataset and each of the three smaller sets (red, yellow and green) is a sub-set of the samples that were classified correctly by using each feature type. The green area is the sub-set of the samples classified correctly by the combined model.  
Figure 7: Confusion matrix created on the test set from the SAU-LI dataset. Values are percentages.  
Figure 8 (left) presents microscope slide pictures of the pollen classes that have the highest confusion (a-d), revealing the similarity in size and shape, in comparison with two other pollen species (e,f). All the highly confusable genera have distinctive \"bumps\" on the surface and are very similar in size. This would translate to obtaining a similar scattering image among these species. When analyzing the fluorescence spectra for the same four classes (Figure 8, right), we can see that they are also very similar. This makes distinguishing among these classes exceedingly difficult compared to other species, such as Artemisia and Pinus, that are quite distinct in size, shape and fluorescence spectrum and are quite easy for the model to classify.  
In Figure 9, the confusion matrix was built from the classifications made with a single feature at a time. The patterns observed in Figure 7, for the combined model, were present in the results of all the feature extractors' classifications.
## 4 Discussion  
Based on a monocular vision reconstruction system, this study developed a LIBS visual experimental platform for microscopic scales by integrating industrial CCD cameras with microscopes. Due to physical limitations such as a narrow depth of field, short focal length, and small camera baseline, traditional 3D reconstruction methods struggle to achieve high-precision morphology under microscopic conditions. To overcome these challenges, we propose a microscale-optimized monocular vision modeling and pixel-matching method, supported by a dedicated microscopic calibration board for accurate intrinsic and extrinsic camera parameter calibration, which improves disparity map accuracy and reconstruction stability. The samples used in this study had relatively smooth surfaces to ensure reliable morphology reconstruction and quantitative analysis; the effects of surface roughness and uneven textures were not examined and remain for future investigation to further enhance method robustness.  
The proposed calibration method distinguishes itself from prior approaches by combining depth-of-focus (DOF) 3D imaging with a nonlinear regression model for matrix effect correction. Unlike previous studies that utilized only qualitative or single-dimensional crater features, our approach quantitatively captures the complex morphological characteristics of ablation craters and integrates them into a robust multivariate calibration framework. This integration significantly enhances the robustness and adaptability of LIBS quantitative analysis under complex matrix conditions, providing a promising solution for industrial applications requiring high-precision micro-scale elemental detection.  
Our findings reveal that ablation morphology not only influences plasma formation and evolution but also plays a critical role in the accuracy of LIBS real-time quantitative analysis. By applying a regression algorithm to analyze trends under the combined effects of multiple factors, a nonlinear matrix-effect calibration model was established. This model effectively suppressed matrix interference, improved the accuracy of trace element  
Figure 16: Comparison of prediction stability before and after morphology correction.  
detection to R\({}^{2}\) = 0.987, and reduced RMSE to 0.1, demonstrating high robustness and adaptability under complex matrix conditions.  
In summary, this study achieved system-level optimization from optical modeling and image reconstruction to spectral analysis. It provides technical support and methodological insights for high-precision quantitative spectral analysis at the microscopic scale, offering promising prospects for broader applications.
## 3 Results and Discussion
### Changes in Myofibrillar Protein  
Figure 2 presents the results of the electrophoresis of myofibrillar proteins in sous vide-cooked horse loin at different cooking durations and temperatures. The quality characteristics of muscle foods are significantly influenced by the functionality of myofibrillar proteins. The cooking process can alter the functional attributes of these proteins, leading to changes in texture and microstructure. The intensity of the band represents the degree of protein degradation. Thinner bands mean that the protein is more degraded compared to the thicker bands. Myofibrillar proteins, as described by [PERSON] et al. [9], constitute 55-60% of the total muscle proteins and play a significant role in enhancing meat tenderness during denaturation. A comparison of the myosin heavy chain (MHC) bands between the control group and the treated groups reveals that the MHC in the 70 \({}^{\circ}\)C for 18 h treatment group has undergone significant degradation, resulting in the thinnest band. The intensity of the tropomyosin \(\beta\)-chain and troponin-T decreased after sous-vide cooking at 70 \({}^{\circ}\)C for 18 h, compared to the control or raw meat. This suggests that sous-vide cooking breaks down the peptides into smaller ones. The functionality of myofibrillar proteins significantly influences meat quality attributes, including texture and microstructure, which can be altered during cooking [44].  
Figure 1: Microstructure of raw and horse loin cooked sous vide at different conditions (1000\(\times\) magnification). R, raw meat. C, control (boiled at 72 \({}^{\circ}\)C for 40 min).
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## Abbreviations  
The following abbreviations are used in this manuscript:  
ALOS Advanced Land Observing Satellite ANN Artificial Neural Network ASAR Advanced Synthetic Aperture Radar onboard ENVISAT satellite BNP Biebrza National Park Bw wet biomass CCI Climate Change Initiative CO2 carbonyl dioxide Eddy Covariance atmospheric measurement technique to measure and calculate vertical turbulent fluxes ENL NL Effective Number of Looks ENVISAT ESA's Environmental Satellite ERS-1/2 SAR ESA's two European Remote Sensing satellites ESA European Space Agency GEC Geocoded Elipsoid Corrected GRD Ground Range Detected GPP Gross Primary Production HH Horizontal Transmit/Horizontal Receive--like polarisation HV Horizontal Transmit/Vertical Receive--cross polarisation IEM Integral Equation Model IS2 ASAR swath (look angle 19.2\({}^{\circ}\)-26.7\({}^{\circ}\)) IS6 ASAR swath (look angle 39.1\({}^{\circ}\)-42.8\({}^{\circ}\)) IW Interferometric Wide mode of Sentinel-1 IERS-1 Japanese Earth Resources Satellite-1 LAI Leaf Area Index Landsat 8 American Earth observation satellite, the eighth in the Landsat program Landsat TM Landsat Thematic Mapper satellite Landsat ETM+ Landsat Enhanced Thematic Mapper Plus satellite MBE Mean Biases Error MERIS Medium Spectral Resolution Imaging Spectrometer MODIS Moderate-Resolution Imaging Spectroradiometer onboard Terra satellite NDVI Normalised Difference Vegetation Index NEE Net Ecosystem Exchange OLI Operational Land Imager onboard Landsat 8 satellite PALSAR Phased Array type L-band Synthetic Aperture Radar Photosynthetically Active Radiation PUWG1992 Polish local projection (Partswouw Ukkad WspOrfzednych Geodezyjnych 1992) R correlation coefficient R2 coefficient of determination RESP Ecosystem Respiration RGB Red, Green, Blue spectral wave used for generation of composite imagery RMSE Root Mean Square Error SAR Synthetic Aperture Radar S1 acronym of Sentinel-1 S1 TBX ESA's Sentinel-1 Toolbox ScanSAR SAR imaging mode Sentinel-1 European Radar Observatory, the first in the series of ESA's satellites within the Copernicus Programme SM soil moisture SNAP Sentinel Application Platform StripMap SAR imaging mode TDR Time Domain Reflectometrya radar Earth observation satellite, a joint venture being carried out under a TerraSAR-X (TSX) public-private-partnership between the German Aerospace Center (DLR) and EADS Astrium TOA Top of Atmosphere TSX acronym of TerraSAR-X UTM Universal Transverse Mercator VV Vertical Transmit/Vertical Receive--like polarisation VH Vertical Transmit/Horizontal Receive--cross polarisation \(\sigma^{\circ}\) backscattering coefficient [dB]
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 4 Results  
To verify the acceleration of the proposed algorithm, running times and image quality were compared using a unified hardware development platform. In simulation and experiments, NVIDIA GeForce GTX 3090 is used, which is mounted in a computer equipped with AMD-3800X (Core frequency is 3.80-4.5 GHz), 64-bit CPU serving as the host, 64 GB RAM, and Windows 10 64-bit OS, whereas the C/C++ Visual Studio 2019 development environment is used to implement the algorithms. The specific operation process is implemented in the following environment:  
1. C++ as a reference compiled procedural language using the Faster Fourier Transform in the West (FFTW) library version 3.3.5.
2. CUDA 10 with the cuFFT and NPP included libraries.
## 2 Background  
Autonomous shipping is an important and significant topic for the maritime industry [7]. Sea trials and tests are underway, attracting international attention. Research and development is proliferating at a faster rate than previously expected in areas such as Norway, Sweden, and the United Kingdom, and also in China, Japan, and Singapore [8]. The number of autonomous shipping publications produced per year has also substantially grown in the past decade [9].  
The Danish Maritime Authority (DMA) has defined four levels of automation. These levels have been unofficially adopted by the International Maritime Organization (IMO) for their Maritime Safety Committee's Regulatory Scoping Exercise (RSE). The RSE has been undertaken to identify the gaps and assumptions of existing IMO codes with the conceptual implementation of autonomous shipping. These levels currently have no legal implications or mandatory requirements for use. According to these definitions, traditionally manned merchant ships of today are at autonomous Level 1, or Manual \"M\". Level 2 is Remote Controlled with Crew Onboard \"R\". Level 3 is Remote Controlled without Crew Onboard \"RU\", and level 4 has no crew onboard \"A\" [10].
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 3 Harmony Search Applications in China
### Engineering  
#### 3.1.1 Electrical/Electronic  
In Ref. [8], using a differential approach hybrid HS algorithm (HSDE), a day-ahead model scheduled for a photovoltaic-wind smart microgrid network with battery storage was introduced. In its current state, several enhancements have been implemented, for example better mutants, increased competitiveness and distinct variations to effectively produce optimal outcomes.  
In a study by [PERSON] et al. [9], a reactive power coordinated delivery network management problem was explored. The problem centered on Improved Harmony Search (IHS) having adaptive parameters process, and intermittent distributed energy incorporation. The applicability of the proposed reactive power-coordinated optimization approach was shown by empirical tests with a conventional 10 kV distribution network and a specific distribution system.  
To increase wireless network resource performance, [PERSON] et al. [10] suggested modern energy-effective clustering and routing algorithms, centered on HS. Their analysis was focused on numerous enhancements to the HS and the energy intensive clustering algorithm as follows:  
1. A distinct harmony encoding scheme for clustering;
2. Designing a roulette-wheel type of selection-strategy;
3. Altering dynamically the value of HMCR; and
4. Proposing a local search approach.  
Many famous metaheuristics have contrasted the proposed method. Their experimental findings explicitly demonstrated the supremacy of the method suggested.  
In research by [PERSON] et al. [11], an optimization model and a modified HS were presented for microgrid planning with the Energy Storage System (ESS). The results when optimized showed that the updated HS algorithm with a microgrid planning project ESS will effectively increase stability and economic performance.  
In Ref. [12], for Fuzzy Cerebellar Model Articulation Controller (FCMAC) networks, an algorithm of Self-adaptive Global Best Harmony Search (SGBHS) for the function approximation problem was used. The SGBHS configured both the weight values of the FCMAC network and the middle location of the Gaussian membership function and width parameters. Experimental and statistical findings concluded that in a short training period, the proposed approach trained the FCMAC network effectively with strong accuracies.  
[PERSON] and his colleagues [13] developed a New Enhanced Harmony Search (NEHS) algorithm to address Dynamic Economic Emission Dispatch (DEED) problems. Test results revealed that NEHS would be more suited for solving five DEED cases. Better quality responses could be generated with a lower calculation pressure.  
[PERSON] et al. [14] modeled a multilayer Smart Home Energy Management System (SHEMS) method. The SHEMS optimum scheduling model was developed to solve the problem by the fusion algorithm of HS and Particle Swarm Optimization (PSO). The experimental optimization results verified the efficiency of the proposed algorithm.
## 3 Results and Discussion
### Water Soluble Vitamins  
Table 2 depicts the effect of SDBD plasma treatment on water-soluble vitamins. The B1 vitamin values of untreated samples varied from 170.00 \(\pm\) 2.89 to 1014.22 \(\pm\) 4.22, while the values of treated samples ranged from 174.08 \(\pm\) 4.61 to 987.76 \(\pm\) 3.99. B1 vitamin values increased in the palm date varieties _Rshadya_ and _Nabtet Ali_ following SDBD plasma treatment, but they reduced in the other palm date varieties.  
The range of B2 vitamin levels for untreated samples was 85.95 \(\pm\) 3.74 to 894.87 \(\pm\) 3.00, while the range for treated samples was 74.08 \(\pm\) 2.89 to 916.64 \(\pm\) 3.32. Except for _Rshadya_, _Maghol_, and _Kladari_, the B2 vitamin content of all palm date cultivars was reduced following plasma treatment with SDBD.  
The range of B6 vitamin levels for the untreated sample was 2301.66 \(\pm\) 59.61 to 9588.19 \(\pm\) 47.88, while the range for the treated sample was 2317.68 \(\pm\) 39.02 to 9549.32 \(\pm\) 28.88. With the exception of the _Maghol_ and _Shalbi_ varieties, all palm date samples treated with SDBD plasma showed a rise in B6 vitamin levels.  
The range of B9 vitamin levels for untreated samples was 35.39 \(\pm\) 2.21 to 240.63 \(\pm\) 2.40, while the range for treated samples was 36.79 \(\pm\) 0.98 to 238.5 \(\pm\) 11.25. The B9 levels of all palm date samples increased following plasma treatment with SDBD, except for the _Rshadya_, _Maghol_, and _Shalbi_ varieties, whose values decreased.  
The range of B12 vitamin levels for the untreated sample was 173.08 \(\pm\) 8.58 to 585.56 \(\pm\) 8.02, whereas the range for the treated sample was 161.11 \(\pm\) 5.88 to 553.46 \(\pm\) 8.72. After plasma treatment with SDBD, B12 levels increased in the palm date varieties _Nabtet Ali_ and _Kladari_ but were reduced in the other treated palm date varieties.  
However, there were no significant changes (\(p>\) 0.05) between treated and untreated samples for B1, B2, B6, B9, and B12 content. These data suggest that the appropriate amount of SDBD plasma treatment has no effect on the water-soluble vitamin content. It demonstrated that non-thermal plasma exposure processing had no effect on the vitamin C and beta-carotene content of _L. burbarum_ samples [76].
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange
### Tuning Reactivity of Ligands  
The energetic disorder caused by the increase of PbS CQD polydispersity restricts the \(V_{\text{OC}}\) and resultantly low _PCE_ in PbS CQD PVs. This energy broadening even becomes wider during PbS CQD ligand exchange and thin film fabrication resulting in large energy loss of CQD PVs over the Shockley-Queisser limit (E\({}_{\text{loss}}=\)qV\({}_{\text{SQ}}-\)qV\({}_{\text{OC}}\)) and low \(V_{\text{OC}}\) value. The E\({}_{\text{loss}}\) of crystalline silicon and perovskite PVs is in the range of 0.1 to 0.2 eV, and this mainly originates from trap recombination losses, leading to destroyed \(V_{\text{OC}}\)[47; 48]. However, the general E\({}_{\text{loss}}\) in the PbS CQD PVs is approximately 0.4 eV, mainly contributed by PbS CQD polydispersity after the ligand exchange process [46]. The replacement of the long-chain ligands by the short-chain conductive ligands can lead to a stripped CQD surface, resulting in CQD fusion [29; 49]. The polydispersity of PbS CQDs can be reduced by controlling the reactivity of ligands during the ligand exchange process [50]. For this purpose, ammonium acetate (AA, reactive species) and tetrabutylammonium acetate (TBAA, less reactive species) mixtures were employed for the solution-phase ligand exchange, and these simultaneously provided improved surface passivation and charge transport with preserved the homogeneity of the PbS CQDs. The less reactive TBAA ligand can preserve a small portion of organic ligand on the CQD surface for preventing PbS CQD fusion and etching. The optimized PbS CQD PVs prepared by this approach exhibits  
Figure 6: Schematic of surface ion distribution of PbS CQDs. (**a**) The PbS CQDs prepared after ligand exchange with iodide in butylamine. Iodide anions attached on the PbS CQD surface are surrounded by methylammonium counter cations. Methylammonium can be removed from the surface by reaction with butylamine. (**b**) As-deposited PbS CQD films. The counter ions residing on the CQD surface to keep neutrality of charge. (**c**) Annealed PbS CQD film. The ion pairs are removed, but most of the iodide ligands remain on the CQD surface. Reprinted from reference [34]. Copyright 2014 American Chemical Society.  
improved device performance compared to conventional CQD PVs (10.1% \(\rightarrow\) 10.9%) with a remarkably enhanced \(V_{\text{OC}}\) value (0.61 \(\rightarrow\) 0.7 V, as shown in Figure 7a,b).