esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Inherent water-use efficiency (IWUE) is a vital parameter connecting the carbon and water cycles. However, the factors influencing the IWUE in different forest ecosystems are still a subject of debate. In this work, FLUXNET platform measurements of 67 forest sites were used to detect trends of the IWUE of four forest ecosystems, namely deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), needle-leaf forests (ENF), and mixed forests (MF). The IWUE differed significantly among different forest ecosystems and positively correlated with temperature and solar radiation. The IWUE of EBF was the highest at 32.02 g\(\cdot\)C\(\cdot\)Kg\(\cdot\)H\({}_{2}\)O\({}^{-1}\). The values of DBF and MF were similar and higher than that of ENF. With increasing latitude, the IWUE increased first and then decreased, with a maximum of 35\({}^{\circ}\) N. The IWUE of EBF was negatively correlated with precipitation and leaf area index. Temperature and solar radiation were the main factors controlling the IWUE of forest ecosystems, whereas precipitation was the major factor controlling the inter-annual variation in the \(\Delta\)IWUE of forest ecosystems. Our results provide a scientific basis for the study of forest carbon sinks, forest eco-hydrological processes, and forest ecosystem responses to global climatic changes.  
evaportanspiration; environmental factors; FLUXNET; net primary productivity Article
# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 2 Methodology
### Attention Module  
Based on the basic CBAM (convolutional block attention module) [17], we propose an attention module that is more suitable for our model. Similarly, the attention module also infers the attention map of the middle feature layer from the channel and space dimensions, and is used for adaptive feature refinement.  
The attention module has two sequential sub-modules: the channel attention module and the spatial attention module. The process is shown in Figure 5.  
The channel attention module leverages channel-wise max-pooling to identify the most salient activation for each channel in the feature map. This information is then fed through a multi-layer perceptron (MLP) [18] to get the weight of the max-pooled feature. These attention weights are then multiplied by a higher-parameter \(\sigma\). Usually, we give \(\sigma\) the value 2. Finally, the channel attention weight is obtained through sigmoid and the channel-refined feature is obtained by weighting the channel attention weight to the input feature, as shown in Figure 6. The channel attention is computed as Equation (8).  
\[W_{\text{C}}=sigmoid(\sigma(MLP(MaxPool(\xi)))) \tag{8}\]  
In the design of the spatial attention module, we follow the design of CBAM. We use the spatial max pooling and the spatial average pooling to obtain the maximum and mean values in the channel-refined feature after the channel attention module, and then obtain the spatial attention weight after the convolution and sigmoid operation. Finally, we apply the spatial attention weight onto the channel-refined feature to obtain the refined feature,  
Figure 5: The overview of attention module.  
Figure 6: Diagram of the channel attention module.  
as shown in Figure 7. The channel attention is computed as Equation (9), where \(Conv_{7\times 7}\) represents a convolution operation with the filter size of \(7\times 7\).  
\[W_{S}=sigmoid\left(Conv_{7\times 7}\left(\left[AvgPool\left(\xi^{\prime} \right);MaxPool\left(\xi^{\prime}\right)\right]\right)\right) \tag{9}\]  
We input the intermediate feature \(\xi\) from the dense motion module into the attention module to obtain the refined feature \(\xi^{\prime}\). The calculation formula is shown in Equation (10)  
\[\xi^{\prime}=Attention(\xi)=W_{S}\otimes W_{C}\otimes\xi \tag{10}\]  
Then, we predict the masks, \(Mask^{0}\dots Mask^{k}\) corresponding to the key points by using \(\xi^{\prime}\), as shown in Equation (11).  
\[Mask=Conv_{7\times 7}\left(\xi^{\prime}\right) \tag{11}\]  
The final dense motion prediction \(\mathcal{F}_{S\gets D}(z)\) is given by:  
\[\mathcal{F}_{S\gets D}(z)=Mask^{0}z+\sum_{k=1}^{K}Mask_{k}\odot\mathcal{ F}^{k} \tag{12}\]  
Note that the term \(Mask^{0}z\) is considered in order to model non-moving parts, such as the background.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 5 Conclusions  
We presented a method to train a ship detector in Sentinel-2 images using self-supervised learning. Our method plugs in a SSL-trained backbone in a U-NET architecture. It achieved better or similar results to standard deep-learning approaches and significantly better results than a spectral index based method. The choice of pretext task in the SSL stage is a major source of performance improvements.  
Further studies should focus on the design of a more effective pretext task. Our work shows that there is room for improvement although the direction towards this goal remains unclear. Instead of hand-designed pretext tasks, learning a better pretext task could be a fruitful avenue of research. However, the computational cost of pre-training is high, so it would be necessary to first reduce this cost or to approximate the pre-training stage performance with a light-weight proxy model.  
The SSL pipeline can be applied to networks where no ImageNet pretraining is available, such as custom architectures specific to remote sensing. Thus, an interesting research goal would be training, through SSL, a feature extractor designed for remote-sensing applications, that improves upon learning from scratch or ImageNet pretraining by a large margin.  
For ship detection, our image-based approach is still two orders of magnitude away from the false alert performance of methods applied on SAR images [1]. To improve image-based ship detection, a better network backbone could be studied, more adapted to small objects. Furthermore, it would be better to include cloud filtering and land/water classification explicitly in the network. Although adding more data is always a good idea, the few-shot setting is more challenging and can bring about more methodological improvements in deep learning for remote sensing.  
Software, validation, data curation, A.C.; supervision, A.S.; methodology, writing: A.C. and A.S. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016798. This document reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains.  
[title=!C0!C0,title=black]0  
**Data Availability Statement:** github.com/alina2204/contrastive_SSL_ship_detection (accessed on 10 September 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 4 Conclusions  
This study assessed the influence of calcium oxide on the ureolytic action of _Bacillus subtilis_ within the AAS matrix. The assessment approach included mechanical property testing (compressive and flexural strengths), durability testing (water absorption, porosity, chloride resistance, and electric resistivity), and microstructural investigations through SEM, EDX, and XRD.  
* The experimental evaluation indicated a deterioration in the mechanical properties of bio-AAS composites with calcium oxide incorporation, where a substantial drop (21.6%) in the mechanical properties was reported.
* Calcium oxide addition increased the free calcium ions that motivated the rapid formation of metal hydroxides. Metal hydroxide formation led to a reduction in the localized hydroxyl ions, disrupting the corresponding microbiological reactions of _Bacillus subtilis_.
* The presence of high silicate concentrations within these conditions resulted in instant silicate gelation with larger particle sizes. The overlapping between these larger particles created wider spaces, increasing the porosity of the composite.
* RCPT did not demonstrate the real chloride resistance of AAS mixtures because of the availability of mobile ions such as Na\({}^{+}\), OH\({}^{-}\), and HS\({}^{-}\) in addition to the Cl\({}^{-}\) mobility. Incorporating CaO increased calcium ions that reacted with some of these available ions, reducing the passing charges and improving the RCPT results.
* The surface electrical resistivity proved the good chloride resistance of the AAS mixtures based on the classification of AASHTO T358-15.
* The results of the microstructure analysis highly illustrated the results observed during the mechanical and durability properties tests. However, CaO addition requires more investigation in the presence of microbial bacteria to determine the required concentration of calcium ions for optimum microbial efficiency.  
Methodology, N.H. and A.E.-N.; Validation, A.E.-N. and I.G.S.; Formal analysis, N.H.; Investigation, A.E.-N.; Writing--original draft, N.H.; Writing--review & editing, I.G.S.; Visualization, I.G.S.; Supervision, A.E.-N. and I.G.S.; Project administration, I.G.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Many thanks to the GUC laboratory, represented by the technician [PERSON] and the student [PERSON], who supported us while doing the experimental work.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
#### 3.1.1 The Case with the Operating Pressure P\({}_{1}\) = 7.5 MPa
Thus, the crack expansion (displacement) up to 0.026 m already started at the time \(t=0.75\) ms (Figure 1), while the stop of the growth of the longitudinal crack was observed at the time \(t=3.5\) ms with a displacement of 0.342 m (Figure 13). Based on the finite-element simulation results, it was found that, within the time \(t=3.5\) ms, the crack grew up 5.9 times, from 0.2 m up to 1.18 m, which, in turn, was an increment of 0.3 compared to the working pressure. The analysis of  
Figure 6:4 Equivalent stresses at time \(t=3.5\) ms.  
the equivalent stresses showed the same characteristic phenomenon at a working pressure of 7.5 MPa. The aforementioned outcomes of the stress-strain state and destruction of the pipeline in the crack tip area under working pressure conditions (7.5 MPa) showed that, when analyzing the displacements in the form of crack extension in the annular direction as a function of time (\(t=1.0\) ms, \(t=2.25\) ms, and \(t=3.5\) ms), there is an increment of 23, 46, and 69 mm respectively, after which the crack opening stops (Figures 14-16). The plastic strains in the form of crack elongation along the longitudinal direction from \(t=1.0\) ms, \(t=2.25\) ms, and \(t=3.5\) ms showed an increment of 5.8 times (Figures 17-19). The analysis of the equivalent stresses at equal values of time demonstrated that, from the beginning of the calculation and up to \(t=3.5\) ms, the maximum stresses were concentrated at the crack tip, while the minimum ones were gathered at its free edges. Within the time \(t=3.5\) ms, the equivalent stresses conversely increased twice (Figures 20-22) and after that, the growth of the crack stopped due to the gas decompression. The findings regarding the crack growth in the area of its tip at the maximum pressure (9.8 MPa) instead showed that, assuming the pressure on the edges of the crack, this led to a length higher than 3.6%. Together, the qualitative state of the deformation and fracture of the pipeline in the area of the crack tip did not change. It can also be noted that the influence of the temperature on the crack development was not taken into account and will be further investigated. For future investigations, a method for the localization of longitudinal fractures in steel gas pipelines with the application of prestressing will also be developed [59; 60; 61; 62; 63; 64]. This methodology could be applied by national, international, and private companies engaged in natural gas transportation to the issue of gas pipelines strengthening without stopping their working operations.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
###### Abstract  
As mobile data traffic levels have increased exponentially, resulting in rising energy costs in recent years, the demand for and development of green communication technologies has resulted in various energy-saving designs for cellular systems. At the same time, recent technological advances have allowed multiple component carriers (CCs) to be simultaneously utilized in a base station (BS), a development that has made the energy consumption of BSs a matter of increasing concern. To help address this concern, herein we propose a novel scheme aimed at efficiently minimizing the power consumption of BS transceivers during transmission, while still ensuring good service quality and fairness for users. Specifically, the scheme utilizes the dynamic activation/deactivation of CCs during data transmission to increase power usage efficiency. To test its effectiveness, the proposed scheme was applied to a model consisting of a BS with orthogonal frequency division multiple access-based CCs in a downlink transmission environment. The results indicated that, given periods of relatively light traffic loads, the total power consumption of the proposed scheme is significantly lower than that of schemes in which all the CCs of a BS are constantly activated, suggesting the scheme's potential for reducing both energy costs and carbon dioxide emissions.  
power-saving; green cellular systems; multiple component carriers +
Footnote †: journal:
###### Abstract  
This study investigated the characteristics and sources of heavy metal(loid) pollution in the soil of a key industrial park in Kunming, China. In total, 60 soil samples (40 from agricultural land and 20 from construction land) were collected from and around the park. The soil pH and contents of Arsenic (As), lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), nickel (Ni), and chromium (Cr) were measured. The contents of the eight heavy metal(loid)s were analyzed using the background values of heavy metal(loid)s in the Kunming soil. The pollution load, geoaccumulation, and Nemero Comprehensive Pollution Indices were used for environmental risk evaluation. Cluster and principal component analyses were used to resolve heavy metal(loid) sources. Cd was enriched in construction and agricultural soils. As, Hg, Cd, Pb, Cu, and Zn exhibited large spatial differentiation and were significantly affected by the external environment. A regional pollution load index of 3.02 indicated overall heavy pollution. The pollution load index for each heavy metal(loid) indicated light pollution. The geoaccumulation index indicated relatively severe As, Cd, Cu, Pb, and Zn pollution. The Nemero Composite Pollution Index value showed that the study area was heavily polluted, with construction land being mildly polluted by Cd, and agricultural land being moderately polluted. The results of the spatial distribution show that there were high levels of contamination in the center. Correlation and principal component analyses showed that the pollution sources of the eight heavy metal(loid)s varied. Hg, Cd, and Pb originate primarily from industrial and agricultural pollution. Traffic sources significantly impacted Cu, Pb, Cd, and Cr. Natural sources are the main sources of Cr, Ni, and Cd. Ni is also affected by industrial sources, whereas Zn and Cu are affected by agricultural pollution. The influences of As, Cd, and Pb on the surface soil in the study area were more serious. Cd is more widely polluted and should be a priority in controlling soil heavy metal(loid)s.  
Keywords:different site; pollution assessment; source analysis; Atomic fluorescence method; inductively coupled plasma mass spectrometry +
Footnote †: journal: Applied sciences
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} COMET & Originally stood for Cooperative Program for Operational Meteorology, Education, and \\  & Training; since 2010 referred to simply as COMET \\ COSMIC & Constellation Observing System for Meteorology, Ionosphere \& Climate \\ EUMETSAT & European Organisation for the Exploitation of Meteorological Satellites \\ GCOM-W & Global Change Observation Mission for Water \\ GEO & Geosynchronous/Geostationary Equatorial Orbit \\ GOES & US Geostationary Operational Environmental Satellite \\ IDSS & Impact-based Decision Support Services \\ JPSS & US Joint Polar Satellite System \\ LEO & Low Earth Orbit \\ Metop & European Meteorological operational polar-orbiting satellite \\ MSG & European Meteosat Second Generation geostationary satellite \\ MTG & European Meteosat Third Generation geostationary satellite \\ NASA & US National Aeronautics and Space Administration \\ NESDIS & US National Environmental Satellite, Data, and Information Service \\ \end{tabular}  
NOAA  
US National Oceanic and Atmospheric Administration  
NWP  
Numerical Weather Prediction  
NWS  
US National Weather Service  
POES  
US Polar Operational Environmental Satellite  
SatFC-G  
NWS Satellite Foundational Course for GOES-R  
SatFC-J  
NWS Satellite Foundational Course for JPSS  
UCAR  
University Corporation for Atmospheric Research  
VLab  
WMO  
World Meteorological Organization
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Calculation of Standardized Photochemical Reflectance Index  
Photochemical reflectance index (PRI) values were computed from collected hyperspectral images for individual plots at different sampling time periods. For each plot, a logarithmic model (\(PRI=a\times ln(PAR)+b\)) was established between diurnal PRI and PAR. To compare the potential of using PRI and SIF for characterization of photosynthetic activities, standardized PRI (sPRI) values were calculated by setting the same PAR (1500 \(\upmu\) mol/m\({}^{2}\)/s) used for sETR calculation.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 2 Design of an Ultra-Wideband Antenna Element
### Step 1: UWB Microstrip Monopole Antenna Element Design
As shown in Figure 3, the simulated reflection coefficient curve (for case 5) is fully matched within the required operating bandwidth from 2.17 to 11.96 GHz. The simulated bandwidth for the UWB monopole microstrip antenna for case 5 is 9.79 GHz (BW% = 138.57%), which is sufficient to cover the FCC-UWB spectrum. Moreover, the antenna supports the Bluetooth frequency band that operates from 2.4 to 2.483 GHz. Figure 3: Simulated reflection coefficients (\(|S_{11}|\)) in dB for UWB antenna design steps. Figure 2: The UWB microstrip monopole antenna: design steps. (**a**) Planar cone; (**b**) Case 1; (**c**) Case 2; (**d**) Case 3; (**e**) Case 4; (**f**) Case 5.
## 3 Test Results
### Design Methods of Load-Bearing Capacity
#### 3.3.2 Design Methods for Shear-Bearing Capacity  
Currently, the generally accepted method for calculating the shear capacity of FRP-reinforced concrete beams is mainly composed of two parts: one part is the shear capacity contributed by concrete \(V_{c}\), and the other part is the shear capacity contributed by stirrups \(V_{f}\).  
\[V=V_{c}+V_{f} \tag{10}\]  
The calculation method of the shear-bearing capacity contributed by stirrups in the ACI 440.1R-06 can be calculated according to the following calculation formula:  
\[V_{f}=\frac{A_{f}c_{f}h_{0f}}{s} \tag{11}\]  
\[\sigma_{f}=E_{f}\epsilon_{f} \tag{12}\]  
where \(V_{c}\) is the nominal shear strength provided by concrete, \(V_{f}\) is the shear resistance provided by FRP stirrups, \(h_{0}\) is the effective height of FRP reinforced concrete beam, \(s\) is the stirrup spacing or pitch of continuous spirals, \(\sigma_{f}\) is the allowable tensile stress in FRP reinforcement, \(E_{f}\) is the stirrup elastic modulus, and \(\epsilon_{f}\) is the strain in FRP reinforcement.  
In the ACI 440.1R-06, the calculation method for the shear force borne by the concrete part is as follows:  
\[V_{c}=\frac{2}{5}\sqrt{f_{c}}b_{w}kd \tag{13}\]  
\[k=\sqrt{2n_{f}\rho_{f}+\left(n_{f}\rho_{f}\right)^{2}}-n_{f}\rho_{f} \tag{14}\]  
\[n=\frac{E_{f}}{E_{c}} \tag{15}\]  
\[\rho_{f}=A_{fl}/b_{w}h_{0} \tag{16}\]  
where \(b_{w}\) is the width of the web, \(d\) is the distance from extreme compression fiber to centroid of tension reinforcement, \(E_{c}\) is the modulus of elasticity of concrete, \(n_{f}\) is the ratio of modulus of elasticity of FRP bars to the modulus of elasticity of concrete, \(\rho_{f}\) is the FRP reinforcement ratio, and \(A_{fl}\) is the area of FRP reinforcement. Table 9 provides a comparison between the shear cracking load capacity values computed using the American ACI standards and the experimental results.
# Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes
## 4 Near-infrared Spectroscopy
With their nondestructive process and rapid measurements, portable instruments offer more possibilities for in-field studies [59; 61]. Over the years, NIR spectroscopy (NIRS) has been rapidly developed and applied (Figure 1) in various industries, such as agriculture, the food industry, medicine, environmental protection, pharmaceuticals, the petroleum industry, and the chemical industry. NIRS has been proven as a fast, nondestructive, and noninvasive spectroscopic technique, employed for multicomponent analysis [62]. It requires minimal amounts of samples for analysis. The price of NIRS use for detection, compared to other standard detection methods, is lower because of the absence of sample pretreatment or preparation processes [63; 64]. NIRS can also be used for online analyses, with good data reproducibility [65]. The major drawback of applying the NIRS technique is its inability to explore trace composition in samples due to the fact that the target composition absorption in the infrared range is weaker when compared with other compositions. Therefore, NIRS can be used to detect samples only with more than a 0.1% mass ratio. This limits the accurateness of the NIRS method. Analysis and quantification of the chemical profile variances require the use of conventional analytical methods. Additionally, NIRS requires the application of chemometric methods for the qualitative and quantitative interpretation of complex spectral data [65, 66]. Considering the last few decades, newly developed NIR performances, such as NIR imaging, have significantly contributed to the application of NIR spectroscopy in different sectors, such as biomedical, pharmaceutical, foods, and polymers [40, 43, 67, 68, 69, 70]. The reason for this lies in the fact that the spatial spreading of sample components provides much information using NIR imaging, enabling in situ, noncontact, and nondestructive analyses. Using this technique, samples that are thick or materials that are bulky can be investigated without sample preparation. It is expected that the future improvement of NIRS technology will dramatically expand its application [40].