esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### C-Band SAR Signal Penetration through Dense Vegetation Cover  
The limited penetration of the C-band SAR signal through dense vegetation cover has a direct impact on the effectiveness of irrigation detection using Sentinel-1 SAR data. The method proposed in this study relies on analyzing the change of SSM derived from S1 data and SSM simulations through Optirrig to identify irrigation events. However, when the SAR signal is impeded by dense vegetation, the accurate estimation of SSM becomes challenging. This limitation arises from the S1 signal's inability to penetrate through vegetation and reach the surface soil. Research conducted by [PERSON] et al. [49] focused on the penetration of the C-band SAR signal across different plots of maize, wheat, and grassland. Their findings revealed that the C-band SAR signal's penetration is limited to an NDVI value of 0.7. Therefore, dense vegetation cover can hinder the detection of irrigation events due to the SAR signal's limited sensitivity to surface soil moisture. Therefore, if the C-band SAR signal is unable to penetrate dense vegetation, the estimated SSM values may not adequately represent the irrigation-induced changes in soil moisture. Consequently, the threshold criteria, which were based on S1 and Optirrig SSM simulations, used in this method to identify irrigation events (Section 3.2.2) may not be met, resulting in missed detections or false negative results. In our case, missed detections were observed for peak NDVI values between 0.7 and 0.8 over the P1 and P2 plots (Figure 8a,b). These findings are consistent with the results obtained by [PERSON] et al. [48] for grassland plots and [PERSON] et al. [43] for maize plots. Both studies highlight the limitations of irrigation detection over dense vegetation cover (NDVI > 0.7) due to the restricted penetration of the C-band SAR signal.
# Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
## 2 Materials and Methods
### Electrochemical Performance  
The half cells were galvanostatically cycled between 50 mV and 0.7 V vs. Li\({}^{+}\)/Li on a battery analyzer (MTI, BST8-MA) with a fixed C-rate at C/20 (0.06 A g\({}^{-1}\)) for the formation cycle, followed by repeated cycling at C/10 (0.12 A g\({}^{-1}\)). For the coin cell with Si-graphene electrode, the specific capacities were calculated based on the total mass of Si-graphene in the cell (e.g., the specific discharge capacity = discharge capacity/total weight of Si-graphene in the cell). The electrode was weighed before the cell assembly. After deducting the weight of the corresponding current collector, the Si-graphene weight ratio (80%) was applied to calculate the weight of the actual active material in the cell. Electrochemical Impedance Spectroscopy (EIS) was performed utilizing the Corrtest Instruments Corp. (Wuhan, China) model CS310 in the frequency range of 1.00 mHz to 100 kHz. The cell voltages were 1.34 V and 0.47 V for Li/Si-Graphene and Li/Si coin cells, respectively. Due to the steep voltage profiles at the voltage range, the cells were at the equivalent, fully charged state. The AC voltage amplitude was 10.0 mV.
## 5 Experiments and Results
### Scheduler Learning Rate  
In this section, we present a predefined framework, where different LR decay strategies are used without specifying the cyclic values. The results are in Table 4.  
1. Decay 1: In this case, we examine how the learning rate changes after the entire epoch rather than for individual steps, using functions that take as input the number of epochs and the current learning rate to provide a new learning rate. This experiment achieves 93% accuracy, where in the first epoch the learning rate is \(LR_{initial}=0.001\), so in each epoch the current LR is divided by 3. In Decay 2, LR is divided by a value of 1.5 to observe a smoother decay, in which case the accuracy is improved to nearly 95%.
2. Step decay: We train our network using SGD and a scheduler with inverse time decay. The following formula \(LR_{step}=LR_{initial}/(1+\frac{DR\cdot Step}{DS})\) is used to calculate the learning rate at each step. Here, DR is the decay rate with a value of 0.5, \(LR_{initial}=0.001\) and DS is the decay step and defines the number of steps after which the learning rate decays. In our case, it is 1000, which gives an accuracy almost equal to the previous one.
3. Polynomial decay [7]: In this part, our network is trained using SGD with polynomial decay. With an initial training and final learning rate between the computed bounds in the optimal convergence region for training. This method improves the accuracy a little by achieving an accuracy of 95%.
4. Piecewise Constant Decay: In this last experiment of this section, we train our network using SGD with a piecewise constant decay scheme. In this process, LR decreases by a few steps. We give LR values for each step, with a constant learning rate every 1000 steps. An accuracy of 95% is achieved.  
Figure 9: Different learning rate strategies versus training iterations. In the upper left corner, the Triangular LR strategy is depicted, and in the upper right corner, the Triangular 2 adaptation. In the lower-left corner, the LR strategy with the sinusoidal function and in the lower right corner is the Inverse Time Decay strategy.
## 4 GA Application in Goods Distribution Systems
### Fitness Function  
The fitness function allows one to assess the quality of a particular candidate solution and consequently select the best one from the current population. According to the introductory considerations regarding the considered problem area (Section 3.1), the fitness function should reflect at least two measures of the system's performance:  
* The goods holding cost \(HC\), \(HC\in[0,\)\(HC_{\max}]\), which quantifies the internal efficiency regarding goods redistribution.
* The fill rate \(FR\), where \(FR\in[0,1]\), which quantifies the system interaction with the external actors that generate the demand.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Population Size** & **Fitness Function Value** & **Best Fitness Value Ratio** & **Simulation Time (ms)** \\ \hline
4 & 0.8335 & 0.9362 & 16,914 \\
10 & 0.8778 & 0.9859 & 43,292 \\
50 & 0.8823 & 0.9910 & 479,072 \\
100 & 0.8904 & 1.0000 & 583,971 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results for different population sizes.
## 4 Methods
### Hand-Designed Descriptors
#### 4.1.3 Colour Texture Descriptors  
* Integrative Co-occurrence Matrices _(ICM)_: Co-occurrence features extracted from each colour channel separately and from the R-G, R-B and G-B pairs of channels as described in [83; 84]. The other settings (i.e., statistics and displacement vectors) were the same as in GLCM (Section 4.1.2). The feature vector is six-times longer than GLCM's, resulting in 60 \(\times\) 6 = 360 features. A rotation-invariant version (ICM\({}^{DFT}\)) based on the same scheme as GLCM\({}^{DFT}\) was also considered.
* Local Binary Patterns + Local Colour Contrast _(LBP + LCC)_: Concatenation of Local Binary Patterns and Local Colour Contrast (LCC) features as described in [85]. LCC is the probability distribution (histogram) of the angle between the colour vector of the central pixel in the neighbourhood and the average colour vector of the peripheral pixels. Following the settings suggested in [85], we used histograms of 256 bins for each resolution (i.e., 1 px, 2 px and 3 px) and concatenated the results. Concatenation with LBP gives a total of 108 + 256 \(\times\) 3 = 876 features.
* Local Colour Vector Local Binary Patterns _(LCVBP)_: Concatenation of Colour Norm Patterns (CNP) and Colour Angular Patterns (CAP) as proposed by [PERSON] et al. [86]. In CNP, the colour norm of a pixel in the periphery is thresholded at the value of the central pixel; in CAP, the thresholding is based on the angle that the projections of the colour vectors form in the RG, RB and GB planes. Since the CNP feature vector is the same length as LBP and CAP's is three-times longer, their concatenation produces 108 \(\times\) 4 = 432 features.
* Opponent Colour Local Binary Patterns _(OCLBP)_: Local Binary Patterns computed on each colour channel separately and from the R-G, R-B and G-B pairs of channels [87]. The other settings (type of neighbourhood and features) were the same as in grey-scale LBP (Section 4.1.2). The resulting feature vector is six-times longer than LBP's; therefore, we have 108 \(\times\) 6 = 648 features.
* Improved Opponent Colour Local Binary Patterns _(IOCLBP)_: An improved version of OCLBP in which thresholding is point-to-average instead of point-to-point [88]. This can be considered a colour version of ILBP (see Section 4.1.2).
* Opponent Gabor Features _(OGF)_: A multi-scale representation including intra- and inter-channel features as proposed in [89]. This comprises 2 \(\times\) 3 (channels) \(\times\) 5 (frequencies) \(\times\) 7 (orientations) = 210 monochromefeatures from each colour channel and 2 \(\times\) 3 (channels) \(\times\) 10 (combinations of frequencies) \(\times\) 7 = 420 intra-channel features. The total number of features is 210 + 420 = 630.
###### Abstract  
The purpose of this work is to test the effectiveness of strengthening timber structures by means of composite bars. This article presents the results of preliminary tests carried out on solid beams made of fir wood. The test specimens, which are classified as strength class C24, had dimensions of \(7\times 17\times 330\) cm. Beams were reinforced with 8 mm diameter basalt fiber-reinforced polymer (BFRP) bars. The bars were glued into grooved channels along the bottom surface. Epoxy resin was used as an adhesive. The strength tests were conducted in accordance with the requirements of EN 408+A1:2012. The four-point bending scheme was adopted. The tests were conducted in the following two series: unreinforced beams (A) and beams reinforced with composites (B). Five elements were tested in each series. The reinforcement resulted in an average increase in the bending moment value of 8.41%. The deflection value at failure increased by 19.77%. The work also includes an analysis of the failure mode and a ductility analysis. Further tests should be carried out using a higher reinforcement ratio. A higher reinforcement ratio should make the presented reinforcement configuration more effective.  
basalt fibers; bars; composites; ductility; load-bearing capacity; reinforcement; wood structures +
Footnote †: journal: Applied Sciences
# Surface Charging on Insulating Films with Different Thicknesses in UPS
## 3 Results and Discussion
### Observations of Photoelectron Spectra
Regime III (\(>\)32.1 nm, Figure 3c): Though a further increase in thickness induces measurable spectral intensity reduction, normalized comparative analysis reveals an only 0.64 eV valence band maximum (VBM) shift across the 16-77 nm range (Figure 3d), indicating that the enhancement of surface charging is not significant. Figure 2: Overlaid XPS valence spectra and UPS spectra obtained during the depth profiling of Sample 1 (**a,b**) and Sample 2 (**c,d**). XPS spectra were acquired using dual-beam charge neutralization with subsequent charge correction. UPS spectra were performed without any charge neutralization method or bias application. A comparable evolution pattern emerges in Sample 2, as constrained by its reduced dimension. UPS spectra demonstrate the truncated progression through two characteristic regimes (Figure 3e,f). In the first regime (0-16.7 nm), the valence band maximum (VBM) undergoes an apparent 6.63 eV displacement. This pronounced displacement correlates with the process of establishing continuous charge dissipation pathways through the SiO\({}_{2}\) layer. In the second regime (16.7-50 nm), spectral stabilization occurs with a merely 0.72 eV VBM shift. The spectral shape preservation in this regime indicates the establishment of thermodynamic equilibrium between surface charging and bulk-mediated charge neutralization regimes. Cross-validation with XPS measurements (Figure 2a,c) conclusively demonstrates that this substantial VBM shift primarily originates from charge-induced artifacts rather than genuine band structure modifications. The differential charge accumulation behavior between samples correlates with their distinct substrate resistivities (0.001-0.005 vs. 1-10 \(\Omega\)-cm), where the higher substrate conductivity in Sample 1 enables more effective charge neutralization through the Si/SiO\({}_{2}\) interface.
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 5 Conclusions  
In this paper, we propose a novel Dual Hybrid Attention module and introduce a DHAU-Net for building segmentation in remote sensing images. DHA modules, which replace the skip connections in U-Net, can eliminate redundant information interference and enhances the collection and utilization of important shallow features. We discussed the performance of the DHAU-net and the DHA module in three different scenarios on the Massachusetts dataset, and perform transfer learning of dataset replacement on the Inria Aerial Image Labeling Dataset to explore the generalization of DHAU-Net. The evaluation metrics demonstrate the effectiveness of our proposed method in building segmentation. And the extensive experiments also show the robustness of our method in handling noise interference, preserving building details, and avoiding misclassifications in complex urban environments.  
Conceptualization, J.L., X.L., H.Y., Z.Z. and J.F.; Methodology, J.L., X.L. and Z.Z.; Software, J.L. and H.Y.; Validation, X.L., H.Y. and J.F.; Writing--original draft, J.L. and X.L.; Writing--review and editing, H.Y., Z.Z. and J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Sichuan Science and Technology Program under Grant 2021 YJ0351 and 2022 YS0521, in part by the Natural Science Foundation of Sichuan Province under Grant No. 2023 NSFSC0020, and in part by the Opening Fund of Geomathematics Key Laboratory of Sichuan Province under Grant scsxdz2019 zd03 and Grant scsxdz2021 yb02.  
The raw data used in this study were obtained from the the Massachusetts Buildings Dataset ([[https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)]([https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)), accessed on 1 June 2021) released by the University of Toronto in 2013, and the Inria Aerial Image Labeling Dataset ([[https://project.inria.fr/aerialimagelabeling/](https://project.inria.fr/aerialimagelabeling/)]([https://project.inria.fr/aerialimagelabeling/](https://project.inria.fr/aerialimagelabeling/)), accessed on 1 June 2021) released by [PERSON] in 2017.  
Author [PERSON] was employed by the company Chengdu Gas Group Corporation Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
As virtual reality (VR) and the corresponding 3D documentation and modelling technologies evolve into increasingly powerful and established tools for numerous applications in architecture, monument preservation, conservation/restoration and the presentation of cultural heritage, new methods for creating information-rich interactive 3D environments are increasingly in demand. In this article, we describe the development of an immersive virtual reality application for the Imperial Cathedral in Konigslutter, in which 360\({}^{\circ}\) panoramic photographs were integrated within the virtual environment as a novel and complementary form of visualization. The Imperial Cathedral (Kaiserdom) of Konigslutter is one of the most important examples of Romanesque architecture north of the Alps. The Cathedral had previously been subjected to laser-scanning and recording with 360\({}^{\circ}\) panoramic photography by the Photogrammetry & Laser Scanning lab of HafenCity University Hamburg in 2010. With the recent rapid development of consumer VR technology, it was subsequently decided to investigate how these two data sources could be combined within an immersive VR application for tourism and for architectural heritage preservation. A specialised technical workflow was developed to build the virtual environment in Unreal Engine 4 (UE4) and integrate the panorama photographs so as to ensure the seamless integration of these two datasets. A simple mechanic was developed using the native UE4 node-based programming language to switch between these two modes of visualisation.  
3D modelling; 3D representation; game engine; laser scanning; panoramic photography; virtual reality +
Footnote †: journal: Applied Soft
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Fabric-Based Graphene ECG Electrodes
#### 2.2.2 Silk-Based Graphene ECG Electrode  
Electronic skins are a kind of newly developed skin contact electrode; they are lightweight, and soft like human skin, thereby allowing them to be attached to human skin without palpable sensation [46]. Silk fibroin is a natural protein material, produced by silkworms, with mechanical durability, tunable structures, and biocompatibility, making it an ideal material for making electronic skins. [PERSON] et al. combined the silk with the thinnest electrical-conductive material, graphene, to develop an ultra-soft, tattoo-like ECG electrode [47]. Silkworm cocoons were used to prepare the silk fibroin (SF) solution. To give the electrodes self-healing function, Ca\({}^{2+}\) ions were added to the SF solution. The resulting SF/Ca\({}^{2+}\) mixture contained plenty of hydron and coordination bonds, which allows self-healing of the ECG electrode. By putting the graphene nanoplates into the SF/Ca\({}^{2+}\) mixture to prepare the graphene/silk fibroin/Ca\({}^{2+}\) (Gr/SF/Ca\({}^{2+}\)) suspension, and using mask printing or direct writing to transfer the designed electrode pattern to the SF/Ca\({}^{2+}\) substrate, a tattoo-like, silk-based ECG electrode was realized (Figure 2c). The electrode can detect ECG signals with a high signal-to-noise ratio, and the continuous monitoring time can reach more than 10 h. With biocompatibility, self-healing ability, and good adhesion to human skin, the silk-based ECG electrode can be used for high-comfort, long-term, and high-quality ECG monitoring.
###### Abstract  
As salt stress has a negative impact on plant growth and crop yield, it is very important to identify and develop any available biotechnology which can improve the salt tolerance of plants. Inoculation with plant-growth-promoting rhizobacteria (PGPR) is a proven environmentally friendly biotechnological resource for increasing the salt stress tolerance of plants and has a potential in-field application. In addition, bacterial volatile organic compounds (mVOCs) are signal molecules that may have beneficial roles in the soil-plant-microbiome ecosystem. We investigated the effects of mVOCs emitted by _Pseudomonas putida_ SJ46 and SJ04 on _Mentha piperita_ grown under different levels of NaCl stress by evaluating their growth-promoting potential and capacity to increase salt tolerance effects. Furthermore, we evaluated under control and salt stress conditions the biocontrol ability of VOCs emitted by both these strains to inhibit the growth of _Alternaria alternata_ and _Sclertoum rJsfii_. The VOCs emitted by both strains under control conditions did not lead to an significant improvement in peppermit growth. However, under salt stress conditions (75 or 100 mM NaCl), an amelioration of its physiological status was observed, with this effect being greater at 100 mM NaCl. This led to an enhancement of the number of leaves and nodes and, increased the shoot fresh and root dry weight by approximately twice in relation to control stressed plants. Moreover, the VOCs released by the two bacteria grown in control or saline media showed a significant reduction in the mycelial growth of _A. alternata_. In contrast, _S. rJsfii_ growth was reduced 40% by the mVOCs released only under control conditions, with no effects being observed under salt stress. We also explored the composition of the bacterial volatile profiles by means of a solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) analysis. From the headspace of SJ46, three VOCs were identified: n-octanol, decane and tetradecane. The emission of SJ04 had the same chromatographic profile, with the addition of two more compounds: 1-(N-phenyl carbamyl)-2-morpholino cyclohexene and tridecane. Only compounds that were not present in the headspace of the control groups were recorded. The salt stress conditions where the bacteria were grown did not qualitatively modify the mVOC emissions. Taken together, our results suggest that plant-associated rhizobacterial VOCs play a potentially important role in modulating plant salt tolerance and reducing fungal growth. Thus, biological resources represent novel tools for counteracting the deleterious effects of salt stress and have the potential to be exploited in sustainable agriculture. Nevertheless, future studies are necessary to investigate technological improvements for bacterial VOC application under greenhouse and open field conditions.  
microbial volatile organic compound; mVOCs; plant-growth-promoting rhizobacteria (PGPR); _Mentha piperita_; salt stress; _Pseudomonu putida_; biocontrol; phytopathogenic fungi