esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Network Model and the Problem Formulation
### Cluster Head Selection  
In the cluster head (_CH_) selection, the residual energy of the nodes plays a significant role [32]. The Euclidean distance parameter also provides a _CH_ selection criterion between the _CH_ and the sink. The data transmission to the sink is handled by the _CHs_, that use a significant amount of energy in the process. As a result, it is important to choose the nodes that are closest to the sink. The weight of the sensors is determined using three alternative ways, and the simulation discusses the results. The weight of sensors \(Wt_{i}\) is represented by Equations (3)-(5).  
\[Wt_{i}=E_{res\_i}\ \ \ \ \ \ \ \textit{when}\ ieG,i<Rs,i<Rc\ and\ 1-\textit{hop}\ \textit{to}\ \textit{sink} \tag{3}\]  
\[Wt_{i}=\frac{E_{res\_i}}{D_{i\_to\textit{Sink}}}\ \ \ \ \textit{when}\ ieG,i<Rs,i<Rc\ \textit{and}\ \textit{more}\ \textit{than}\ 1-\textit{hop}\ \textit{to}\ \textit{sink} \tag{4}\]  
\[Wt_{i}=\frac{E_{res\_i}^{2}}{D_{i\_to\textit{Sink}}}\ \ \ \ \ \textit{when}\ ieG,i>Rs,i<Rc\ \textit{and}\ \textit{more}\ \textit{than}\ 1-\textit{hop}\ \textit{to}\ \textit{sink} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline G & Sensor Field \\ N & Number of Nodes \\ E & Number of Links \\ \(N_{CH}\) & Cluster Head Count \\ \(CH_{id}\) & Cluster Head of Node \\ \(\varnothing\) & Angle of a Sector \\ \(Wt_{i}\) & Weight of Sensor \\ \(E_{res\_i}\) & Residual Energy of Current Node \\ \(D_{i\_to\textit{Sink}}\) & Distance between Current Node to Sink Node \\ \(E_{1}(S_{i\_}{i\_}{CH}S_{i\_})\) & Energy Consumption Using Direct Communication \\ \(d_{S_{i},CH_{is}}^{i}\) & Distance between CH and Node with a Distance Threshold of do \\ \(\epsilon_{fs}\) and \(\epsilon_{emp}\) & Free space and Multipath Loss Factor \\ \(E_{2}\left(S_{i},S_{j},CH_{is}\right)\) & Energy Consumed during Entire Routing Path \\ \(E(S_{i})\) & Energy Required for Intra Cluster Communication \\ \(E_{dc}\) & Transceiver and Receiver Electronics \\ \(ETX\) & Transmission Energy \\ \(E_{RX}\) & Reception Energy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and notations used in the proposed model.  
where \(E_{res,i}\) represents the residual energy of the current node and \(D_{i,to,Sink}\) is the distance between the current node and the sink. The node nearest to the sink will initially notify its weight to other nodes in the cluster and declare itself to be a potential _CH_. The nodes that broadcast their own weight and become new _CH_ candidates are those with a greater weight. The last \(CH\) in each cluster will be chosen from the node with the maximum weight.
## 2 ACS-SSI Algorithm and Its Verification
### Numerical Simulation in Multi-DOF System  
#### 2.2.1 Multi-DOF Numerical Simulation  
A three-DOFs numerical simulation model is established in Matlab/Simulink software to verify the robustness of the improved stochastic subspace algorithm and its reliability for high damping ratios (Figure 1). The mass blocks m\({}_{1}\), m\({}_{2}\), m\({}_{3}\) are connected by the fixed stiffness \(k_{1}\), \(k_{2}\), \(k_{3}\) and damping \(\epsilon_{1}\), \(\epsilon_{2}\), \(\epsilon_{3}\), respectively. The three natural frequencies of the system \(f_{n1}=2.5\) Hz,\(f_{n2}=5.8\) Hz,\(f_{n3}=9.9\) Hz, are determined by both mass and stiffness. Three low-pass filtered white noise signals, \(w_{1}\), \(w_{2}\), and \(w_{3}\), are applied to the three mass units to simulate dynamic behaviors under random excitation.  
Figure 1: Model of the three degrees of freedom (DOFs) system.  
The sampling frequency of the simulation system is 200 Hz, the solution time is 20 s, and the average correlation calculation is carried out 50 times. The dynamic responses of the vehicle suspension are concentrated primarily in the frequency range of 0-50 Hz [31]. Therefore, according to the Nyquist sampling theorem, the sampling frequency of the simulation system is selected as 200 Hz. As seen in Figure 2, the calculated acceleration of the three masses exhibits obvious random characteristics, while correlation function signals are more regular. The average correlation calculation reduces the random component in the original acceleration response signal, so the correlation function obtained after averaging has a higher periodicity than the original acceleration signal.
# Light-Responsive Polymeric Micellar Nanoparticles with Enhanced Formulation Stability
## 3 Results
### Cellular Uptake Study and Cytotoxicity  
The cell internalization of **NR-NPDNQ** was characterized by a fluorescence microscopy. As shown in Figure 4, **NR-NPDNQ** quickly entered into MCF-7 cells. After incubation with **NR-NPDNQ** for 30 min, most of MCF-7 cells showed red fluorescence in the cell and then the fluorescence intensity from the cells was saturated after 1 h. As shown in Figure 3d, less than 7% of hydrophobic NR was released from the micelle within 6 h. Thus, the saturation of fluorescence intensity after 1 h indicates that the cell uptake of **NR-NPDNQ**, not the free NR (e.g., NR fully stains cells with 10 min [55]), is the dominant factor of this fluorescence increase. Through the cellular uptake study, we confirmed that **NR-NPDNQ** efficiently entered into MCF-7 cells within 1 h.  
Human umbilical vein endothelial cells (HUVECs) were used to evaluate the cytotoxicity. The viability of HUVECs was determined by MTT assay in corresponding culture medium after 24 h incubation with DTX, polymers and micelles. PEG-PLL-DNQ and PEG-PLL-Oct showed low cytotoxicity against HUVECs with the concentrations up to 0.5 mg/mL (>90% viability, Figure S5). Furthermore, the cytotoxicity of DTX, **NPDNQ** and **DTX-NPDNQ** showed that LC50 for DTX was 99 mg/mL and **DTX-NPDNQ** was 310 ng/mL in HUVECs (Figure 5a). The results show a lower cytotoxicity with **DTX-NPDNQ** than free drug **DTX**, against HUVECs, because **NPDNQ** strongly retains the hydrophobic aromatic DTX in the core of micelle due to hydrophobic and \(\pi\)-\(\pi\) stacking interactions.  
Figure 4: Fluorescence images of the internalization of **NR-NPDNQ** by MCF-7 cells for different time intervals: Nuclear staining with DAPI (blue color) and **NR-NPDNQ** (red color). (Scale bar: 20 μm).
# Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph
## 3 Obstacle Avoidance Using EVG
In Proceedings of the 45 th IEEE Conference on Decision and Control, San Diego, CA, USA, 13-15 December 2006; pp. 5699-5705. * (58) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Formation control and collision avoidance for multi-agent non-holonomic systems: Theory and experiments. _Int. J. Robot. Res._**2008**, _27_, 107-126. [CrossRef]* (59) [PERSON]; [PERSON]; [PERSON] [PERSON] 3D navigation and collision avoidance for a non-holonomic vehicle. In Proceedings of the American Control Conference, Seattle, WA, USA, 11-13 June 2008; pp. 3512-3517. * (60) [PERSON]; [PERSON]; [PERSON] Avoidance maps: A new concept in UAV collision avoidance. In Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA, 13-16 June 2017; pp. 1483-1492. doi:10.1109/ICUAS.2017.7991382. [CrossRef]
* (61) [PERSON]; [PERSON]; [PERSON] Nonlinear control for autonomous trajectory tracking while considering collision avoidance of UAVs based on geometric relations. _Energies_**2019**, 12, 1551. [CrossRef]
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###### Abstract  
This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.  
haze removal; dark channel; atmospheric-light estimation; coarse-to-fine search strategy +
Footnote †: journal: Applied Sciences
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 1 Introduction
used fully convolutional networks (FCN) to improve the detection accuracy of informal residential areas in high-resolution satellite images [15]. [PERSON] et al. successfully applied the FCN model to weed identification in paddy fields [16]. However, FCN is not sensitive to the details in the images, and its up-sampling results are often blurred. [PERSON] et al. proposed a classic deep learning method (i.e., Segnet) for image semantic segmentation, which was used for automatic driving or intelligent robots [17]. The model has obvious advantages over FCN in storage, calculation time, and segmentation accuracy. Inspired by the great success of Segnet in image semantic segmentation [17; 18], we used Segnet as a segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of oil spill. Each scene image is cropped according to four different window sizes, and samples containing oil slicks and seawater are selected from the cropped pieces as data sets. Four hundred and twenty samples were selected from each oil slick scene, with a total of 2100 sample data. To enhance the robustness of the training model, 21 samples in each oil slick scene were added with 10 noise levels of multiplicative and additive noise, respectively, totaling 2100 noisy images. The training set consisted of 1800 original samples and 1800 noisy samples, totaling 3600. The testing set consisted of 600 samples, including 300 original samples and 300 sets of noisy samples (20 noise level data corresponding to three samples randomly selected in each oil slick scene). The effectiveness of the method is demonstrated through the comparison with FCN and some classical segmentation methods (such as support vector machine (SVM), classification and regression tree (CART), random forests (RF), and Otsu, etc.). The segmentation accuracy based on Segnet can reach 93.92% under high noise and weak boundary conditions. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show higher robustness. The rest of this paper is organized as follows. Section 2 focuses on the preparation process of the data set, which includes description, preprocessing, and sampling of five SAR oil slick scenes acquired by C-band Radarsat-2. In Section 3, we describe the segmentation based on the Segnet model and the parameter selection in the training process. The validity of the algorithm is verified through analysis and compared with the experimental results of the semantic segmentation model FCN8s. In Section 4,we analyze the validity and stability of the model. The conclusions and outlooks are discussed in the final section.
## 4 Emerging Biogas Upgrading Technologies
### Biological Technologies
#### 4.4.3 Ecological Lung  
The enzyme carbonic anhydrase (CA) is present in human blood, and it catalyzes the dissolution of CO2 formed during metabolism in the cells [49; 78]. Once the dissolved CO2 reaches the lungs in the form of carbonate, the same enzyme catalyzes the reverse reaction (Equation (13)) [49].  
\[\mathrm{H_{2}O+CO_{2}\rightleftharpoonskip H+}+\mathrm{HCO_{3}}. \tag{13}\]  
Thus, the potential of the enzyme to dissolve CO2 from biogas and obtain higher concentrations of methane through a process named ecological lung or industrial lung has been identified. This technology aims to mineralize carbon dioxide using CA, an enzyme present in nearly all vertebrate organisms. In 2005, a research group achieved purity of 99% methane using an immobilized CA enzyme for biogas upgrading [146]. The production cost of the enzyme remains high, and the viability of the process is affected by factors such as the lifetime of the immobilized enzyme [147].  
The reaction rate of CA catalysis is much faster than the rate at which CO2 is combined with chemically absorbing molecules such as amines [148]. Enzyme immobilization can broaden the applications of natural catalysts by enhancing the catalytic stability of enzymes [149].  
[PERSON] et al. [150] conducted a study of rate-based modeling for biogas upgrading by applying reactivated slow tertiary amines to reduce energy consumption. This was achieved using CA enzymes as additives in the slow-reacting solvent.  
In 2014, a Canadian company announced the development of a high-performance CA enzyme [151]. The enzyme can be used to catalyze scrubbing processes of carbon capture using salt solutions as solvents, with the potential to reuse the sequestrated CO2. The obtained CO2 absorption kinetics are similar to those of amine solutions, with the advantage that regeneration is achieved at temperatures below 80 \({}^{\circ}\)C. The advantages are lower capital and operational costs compared to AS and the generation of high-purity CO2 suitable for reuse applications [151].
###### Abstract  
Vegetation water-use efficiency (WUE), which represents the trade-off between carbon assimilation and water consumption, is a key indicator of ecosystem adaptation to environmental change. While previous studies have addressed the climatic controls on WUE in alpine ecosystems, the quantitative response mechanisms along elevation gradients remain insufficiently explored. This study investigated the growing season WUE patterns of alpine grasslands across elevation zones on the Qinghai-Tibetan Plateau by integrating partial correlation analysis and structural equation modeling (SEM). The findings revealed a clear triphasic pattern in WUE variation: a modest increase below 3000 m, a pronounced peak near 3700 m, and a steady decline at higher elevations. The dominant hydrothermal drivers shift with elevation. At lower altitudes, WUE was primarily influenced by the vapor pressure deficit (VPD), whereas soil temperature (ST) and VPD jointly govern WUE at mid-to-high altitudes. The SEM results indicated that the total effect of temperature on WUE increased from 0.51 at low elevations to 0.95 at high elevations, while the total effect of precipitation rose from \(-\)0.36 to \(-\)0.18. ST and VPD mediate the effects of temperature and precipitation on WUE, reflecting indirect and nonlinear regulatory pathways. Moreover, contribution rate analysis showed an elevation-dependent shift in WUE control: evapotranspiration (ET) exerted a dominant influence at low elevations (contribution rate: \(-\)82.50%), while net primary productivity (NPP) became the primary driver at high elevations (contribution rate: 54.71%). These findings demonstrate that alpine vegetation's carbon-water coupling exhibits threshold-like behavior along altitudinal gradients, governed by differentiated hydrothermal constraints, offering new insights into ecosystem resilience under climate change.  
WUE, Water-use efficiency; elevation gradient; hydrothermal factors; alpine grasslands; Qinghai province 2025  
4025 14 1173
###### Abstract  
Single-object tracking algorithms based on Siamese full convolutional networks have attracted much attention from researchers owing to their improvement in precision and speed. Since this tracking model only learns a similarity model offline, it is not able to obtain more useful feature discrimination information to adapt to the various variations of targets in complex scenes. To improve the performance of this tracking model, we propose a Siamese network tracking algorithm that incorporates multiple attention mechanisms and an adaptive updating strategy for background features. First, a backbone feature extraction network is proposed that utilizes a small convolutional kernel to fuse jump-layer connectivity features, thereby improving the feature representation capability of the network. Second, an adaptive update strategy for background features is proposed to improve the model's ability to discriminate between the object and background features. Third, the fusion of multiple attention mechanisms is proposed so that the model learns to focus on the channel, spatial, and coordinate features. Fourth, the response fusion operation is proposed after the inter-correlation operation to enrich the output response of the model. Finally, our algorithm is trained using the GOT-10K dataset and evaluated by testing on the object tracking benchmark datasets OTB100 and VOT2018. The test results show that compared with other algorithms, our algorithm can effectively cope with the problem of degradation of the tracking performance in complex environments, and it can further improve the tracking precision and precision under the premise of ensuring the tracking speed.  
fusion of multiple attention mechanisms; background adaptive updating strategy; Siamese network; single-object tracking algorithm +
Footnote †: journal: Applied Sciences  
Article
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## Abbreviations
Available online: [[http://www.eea.europa.eu/data-and-maps/data/eu-dem](http://www.eea.europa.eu/data-and-maps/data/eu-dem)]([http://www.eea.europa.eu/data-and-maps/data/eu-dem](http://www.eea.europa.eu/data-and-maps/data/eu-dem)) (accessed on 12 February 2016). * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; _et al._ High-Resolution Global Maps of 21 st-Century Forest Cover Change. _Science_**2013**, _342_, 850-853. [CrossRef] [PubMed]
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## 2 Materials and Methods
### Reliability Evaluation Method
* Using data from the historical record generate randomized hourly PV generation time series data \(\mathrm{P_{s}(h)}\). * Using data from the historical record generate the hourly chronological curve of annual demand \(\mathrm{P_{d}(h)}\). * Combine \(\mathrm{P_{s}(h)}\) and the annual failure sequence to get the generating capacity sequence GCS(h) for the simulated year. * FOR h = 1 to 8760 DO:
* Using GCS(h) and \(\mathrm{P_{d}(h)}\), solve Equation (7) to get SOC(h). * Update the number of interruptions i and evaluate the duration in hours of each interruption \(\mathrm{H_{i}}\). * If SOC = SOC\({}_{\mathrm{min}}\) and GCS(h) \(<\)\(\mathrm{P_{d}(h)}\), update LOEE: LOEE = LOEE + Pd(h) \(-\) GCS(h). * If SOC = SOC\({}_{\mathrm{max}}\) and GCS(h) \(>\)\(\mathrm{P_{d}(h)}\), update ENU = ENU + GCS(h) \(-\) P\({}_{\mathrm{d}(h)}\). * Determine FOI index: FOI = i. * Assess the loss of load expectation (LOLE) index: LOEE = \(\sum\)Hi (h/yr). * Assess the loss of load probability (LOLP) index: LOLP = 100-LOLE/8760. * Evaluate average values of the indices for the n simulated years (Consider a possible convergence criterion). * Evaluate reliability indices frequency histograms per year. To establish a number of iterations to make the probabilistic calculation with an acceptable error, a study of the standard deviation of the averaged LOLP index is carried out as a function of the number of repetitions, taking into account the time used by the simulation (Figure 3). Above 500 iterations, the error is around 2%, with a simulation time of approximately 2.25 s. From this point on, the error reduction function becomes horizontally asymptotic, while the execution time increases proportionally to the iterations. To reduce the error to 1%, the execution time is multiplied five times. Carrying out tests of the code, it has been observed that with 500 repetitions, errors occur in the convergence of the program. For that reason, the number of iterations has been increased to 1000. Figures 4 and 5 show the reliability indices average as a function of the number of iterations. Figure 4: Value of the LOLE index as a function of the number of iterations. Figure 5: Value of the ENS index as a function of the number of iterations. Figure 3: Error of the result and simulation time versus number of iterations.