esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The flowchart of this paper is shown in Figure 2. This study consists of two main parts: the remote sensing mapping of crop information using the phenology-GPP method and the remote sensing estimation of agricultural NSP. In Section 3.1, based on Sentinel-2 images and MODIS-GPP data acquired in 2019, we extract cultivated land and crop types. Then we simulate the differences in spatial distributions of crop growth in the Yuecheng Reservoir catchment area to estimate agricultural NSP. In Section 3.2, we introduce the calculation formula of NSP intensity for refining crop impact. In Section 3.3, we describe the methods for the verification of crop classification and crop growth spatial difference simulation.  
Figure 2: Flowchart depicting crop information extraction and non-point source intensity estimation.
## 4 Discussion and Results
### Impact of Failure Rate on MTTF  
The MTTF of the system is only related to \(\lambda_{1}\)-\(\lambda_{18}\), \(\lambda_{21}\), \(\lambda_{22}\) and \(\lambda_{24}\). Their impacts on MTTF are as shown in Figure 12a-c.  
From Figure 12a, it can be seen that the \(\lambda_{17}\), \(\lambda_{9}\) and \(\lambda_{16}\) have the biggest impact on the MTTF of the wellhead connector. Decreasing the values of \(\lambda_{17}\), \(\lambda_{9}\) and \(\lambda_{16}\) can effectively increase the MTTF of the system. According to Figure 12b, \(\lambda_{21}\) and \(\lambda_{22}\) are the periodic test completion rate and the periodic test rate, respectively. The growth of the two is consistent, thus, the impacts on the system MTTF can offset each other. As shown in Figure 12c, when \(\lambda_{24}\) is reduced from \(3\times 10^{-6}\) to \(1\times 10^{-6}\), MTTF can be increased from \(3\times 10^{6}\) to \(1.4\times 10^{7}\). It can be seen that reducing the \(\lambda_{24}\) can greatly increase the MTTF. The modes with high impacts should take high priority in system design with regard of reliability. Based on the above results, the most effective way to increase the MTTF of the wellhead connector is to reduce the failure rate of collet deformation and seal ring failure during normal operation, and reduce the number of failures during periodic test.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
###### Abstract  
This paper presents a multi-source data-driven hybrid path planning framework that integrates global A* search with local Deep Q-Network (DQN) optimization to address complex terrestrial routing challenges. By fusing ASTER GDEM terrain data with OpenStreetMap (OSM) road networks, we construct a standardized geospatial database encompassing elevation, traffic, and road attributes. A dynamic-heuristic A* algorithm is proposed, incorporating traffic signals and congestion penalties, and is enhanced by a DQN-based local decision module to improve adaptability to dynamic environments. Experimental results on a realistic urban dataset demonstrate that the proposed method achieves superior performance in risk avoidance, travel time reduction, and dynamic obstacle handling compared to traditional models. This study contributes a unified architecture that enhances planning robustness and lays the foundation for real-time applications in emergency response and smart logistics.  
Keywords:multi-source data; path planning algorithms; A* algorithm optimization; deep reinforcement learning; dynamic decision making +
Footnote †: journal:  
Article
## 3 Results and Discussion
### Historical Storm Surge Event, January 2019
#### 3.1.1 Meteorological Conditions and Situation in the Gulf of Gdansk  
In early January 2019, a significant storm surge event occurred in the Vistula Lagoon due to a combination of meteorological factors, including high sea levels in the Gulf of Gdansk and strong wind action. Observations recorded by IMGW-PIB indicate that the water level in the Gulf of Gdansk reached 1.11 m above mean sea level, approaching the 10% probability maximum level [11]. This elevation in sea level was driven by prolonged periods of strong easterly and north-easterly winds, reaching speeds of over 18 ms\({}^{-1}\). The meteorological pattern included shifts in wind direction and velocity, leading to multiple episodes of water accumulation in the southern part of the Vistula Lagoon.
## 4 Experimental Results
### Improvement of CRB  
A contrast-enhanced residual block (CRB) was designed by fusing both the contrast-enhanced channel and spatial attention within residual learning, which is used in the dual mutual-feedback component as each cell block. In this experiment, we replaced the CRB with several attention-based modules used in existing methods to evaluate their effectiveness, such as CBAM [27], RCAB [26], and CCA [28]. Figure 9 shows a comparison of these attention models. Our CRB and CBAM [27] contain both channel and spatial attention, while CCA [28] and RCAB [26] are models based on the channel attention. We used the above attention models on our DMFN to compare their performance. They are denoted as DMFN-CBAM, DMFN-RCAB and DMFN-CCA. As illustrated in Table 5, our CRB performed better than the above attention-based models. Therefore, our CRB was shown to be efficient and to improve SR performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reconstruction Method**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multicolumn{2}{c}{**MRI13**} & \multicolumn{1}{c}{**ADNI100**} & \multicolumn{1}{c}{**OASIS100**} \\ \cline{5-6}  & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} concat function \\ multi-prediction \\ \end{tabular} } & \multirow{2}{*}{\(\times 3\)} & 583 k & 36.72/0.9485 & 30.17/0.8719 & 34.35/0.9478 \\  & & 582 k & 36.24/0.9468 & 30.08/0.8708 & 34.07/0.9465 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the concat function and multi-prediction used on DMFN.  
Figure 8: DMFN without the concat function, which degrades into the multi-prediction method.  
Figure 9: Existing attention models [26; 27; 28].
###### Abstract  
Flow diverters (FDs) for the endovascular treatment of intracranial aneurysms are effective for sidewall aneurysms, but their use at a bifurcation is problematic because FDs only partially cover the aneurysm neck and impede flow into a daughter branch; they are thus not employed routinely in this anatomy. eCLIPs was developed as a non-tubular implant to completely cover the neck of an aneurysm and serve as a coil retention device necessary for the adequate treatment of wide-neck bifurcation aneurysms. eCLIPs has shown some flow diversion effects in bifurcation anatomy but not equal to those exhibited by clinically accepted flow diverters in sidewall anatomy. A new generation of eCLIPs implant, the eCLIPs bifurcation flow diverter (eBFD), with higher metal coverage, was developed to achieve a similar flow diversion as a Pipeline Embolization Device (PED), a prototypical FD. Particle image velocimetry was used to capture the fluid dynamics and velocity reduction within silicone aneurysm replicas. A circulatory mimicking loop was developed to circulate the flow through the silicone models. All generations of eCLIPs implants had some flow-diverting effect, with increasing metal coverage density of the implant proportionately increasing the flow diversion effect. The eBFD, with a metal density of 35%, showed greater flow diversion than PED, with 30% metal density, for bifurcation anatomy. The eBFD showed similar reduction of flow in a bifurcation anatomy to PED in a sidewall, both sufficient to permit early thrombosis of the aneurysm. Thus, the eBFD can potentially provide sufficient flow diversion for the treatment of bifurcation aneurysms to avoid adjunctive coiling.  
**Keywords:** particle image velocimetry; flow diverter; aneurysm; bifurcation; hemodynamic; velocity reduction; coil +
Footnote †: journal: Applied Sciences
# Influence of Multiple Openings on Reinforced Concrete Outrigger Walls in a Tall Building
## 3 Evaluation of the Influence of Multiple Openings
### Strength Predicted by Strut-and-Tie Models  
According to Chapter 23 of ACI318-14, the nominal shear strength of the outrigger walls can be calculated by using the strut-and-tie model. In this study, the nodal zones are assumed to be stiff enough. Therefore, only the strengths of strut and tie were considered. The diagonal strut in the central zone surrounded by four openings has an angle \(\theta\) with the horizontal tie, as shown in Figure 15.  
The shear strength of the outrigger wall by the strut-and-tie model \(V_{stm}\) was the smaller one of the shear strengths provided by the strut and the tie, as shown by the following equations.  
\[V_{n}=F_{ns}sin\theta\leq 0.85\beta_{s}f_{c}^{\prime}b_{w}w_{str} \tag{11}\]  
\[V_{n}=F_{nt2}sin\theta+F_{nt4}tan\theta\leq A_{s2}f_{y}sin\theta+A_{s4}f_{y}tan\theta \tag{12}\]  
where \(F_{ns}\) is the nominal compressive strength of a strut. \(\beta_{s}\) is the reduction factor to account for the bottle-shaped strut. For a strut of a uniform cross-sectional area, \(\beta_{s}=1.0\) and \(w_{str}\) is the width of strut. \(F_{nt2}\) and \(F_{nt4}\) are the nominal tensile strengths of the top and diagonal reinforcements, respectively.  
The indices representing the relative strength degradation \(\beta_{V}\) as increasing the size of the openings are presented in Figure 16. It can be observed that the \(\beta_{V}\) remained 1.0 before the opening ratio reached 19% (M0-M7). This means that the steel reinforcements yielded before the failure of concrete until the size of the openings reached 19%. When the size of the openings increased beyond 19%, the openings reduced the width of the strut and, consequently, reduced the strength of the strut and strength of the outrigger walls. The strength ratio \(\beta_{V}\) began decreasing sharply from model M8 and dropped to 39% in model M11. These results indicate that the failure of concrete preceded the yielding of reinforcements in model M11. When comparing Figure 14 from the nonlinear finite element analysis and Figure 16 from the strut-and-tie model, it can be noticed that the strut-and-tie model is not conservative in predicting the shear strength of outrigger walls with low opening ratios.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 4 Tests Performed
### Testbench  
The entire set of tests was classified by order modulations between 2 and 32 and power transmissions between 3 and 100 W. For all of them, the bandwidth was set to 2.3 kHz, and we made use of three modulations (PSK, FSK, and QAM). After this, we obtain the results depending on the ionospheric layer reflected and depending on the polarization technique used by MATLAB post-processing. In one hour we perform 25 tests, as we can see in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order of Modulation** & **Power Transmission** & **Minute** \\ \hline
2, 4, 8, 16, 32 & 3 W & 05, 06, 07, 08, 09 \\
2, 4, 8, 16, 32 & 6 W & 15, 16, 17, 18, 19 \\
2, 4, 8, 16, 32 & 12 W & 25, 26, 27, 28, 29 \\
2, 4, 8, 16, 32 & 25 W & 35, 36, 37, 38, 39 \\
2, 4, 8, 16, 32 & 50 W & 45, 46, 47, 48, 49 \\
2, 4, 8, 16, 32 & 100 W & 55, 56, 57, 58, 59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testbench transmitted.  
Figure 5: Frame design.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 3 Methodology  
### Radiometric Calibration Approaches  
The reflectance-based vicarious calibration approach is the most commonly used method, which relies on synchronous measurement of the surface spectral reflectance and atmospheric parameters (including the aerosol optical depth and amount of water vapor). The measurement of the surface spectral reflectance and atmospheric parameters are required as inputs into a radiative transfer model, such as Moderate Resolution Atmospheric Transmission (MODTRAN 6.0), to compute the TOA spectral radiance. The simulated spectral radiance at the TOA from the radiative transfer model is matched with satellite DN and is used to obtain the sensor's absolute radiometric calibration coefficients [17; 18; 19]. The flow chart of the reflectance-based vicarious calibration approach is shown in Figure 5.  
Figure 4: The arrangement of the calibration tarps at the RadCalNet Baotou site.  
Figure 3: The gray-scale permanent artificial targets at RadCalNet Baotou site.
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 2 Related Work
The authors of [23, 24, 25] proposed to organize nodes into groups called clusters. They elected in each cluster a node named cluster head (CH) responsible of data dissemination and forwarding inside the group. In [23], the authors proposed an efficient energy-aware predictive clustering scheme for vehicular networks. They used the previous positions of vehicles to predict their future position and form a cluster of vehicles driving in the same direction. This technique prolongs the duration of cluster membership and avoids re-electing new clusters in case of vehicles that are driving in the opposite direction. Maximizing cluster membership reduces the traffic related to cluster construction and frees the channel for data communications. In Node Ranked Low Energy Adaptive Clustering Hierarchy (NR-LEACH) protocol [24], the authors distributed the energy load of sensors in a good manner among all nodes by CH rotation process in order to prolong the lifetime of the network. NR-LEACH uses node rank algorithm that combines path cost between nodes, number of links between nodes and residual energy. The node with high rank is selected to act as a CH. NR-LEACH helps to prolong the lifetime of nodes, However, re-electing the CH increases the lost packets because the network remains unavailable between two elections. The clustering techniques have proved their ability to enhance the performance of the network and prolong the lifetime of node. However, they still suffer from time delay associated to cluster formation, which slows communication till the clusters are constructed.
## 4 Analysis of Single-Factor Influence Law
### Influence of Grouting Distance on Compensated Level of Horizontal Displacement of the Tunnel  
The example in Section 3.1 was considered as the standard operating condition, and the grouting distance was set at 5, 10, 15, and 20 m; the other parameters remained unchanged.  
Figure 11 depicts the influence of the grouting distance on the compensated level of horizontal displacement of the tunnel, and Figure 12 delineates the relationship between the grouting distance and the maximum horizontal displacement of the tunnel. First, the variation of the grouting distance had a greater impact on the horizontal displacement of the tunnel. Second, as the grouting distance increased, the horizontal displacement of the tunnel gradually decreased. Indeed, at a grouting distance of 5, 10, 15, and 20 m, the maximum horizontal displacement in the center of the tunnel was 17.8, 5.75, 2.6, and 1.4 mm, respectively. Third, as the grouting distance increased, the maximum horizontal displacement of the tunnel gradually decreased, and then the downward trend slowly  
Figure 10: The relationship between the volume of grouting and the maximum horizontal displacement of the tunnel.  
Figure 9: The influence of different volumes of grouting on the compensated level of horizontal displacement of the tunnel.  
plateaued, which confirms that small changes in the grouting distance have a greater impact on the amount of correction of horizontal displacement of the tunnel when the grouting point is closer to the tunnel. Therefore, it is necessary to strictly control the grouting distance during grouting of a tunnel.
## 4 Experimental Results
### Performance Verification Experiment
#### 4.2.5 Cross-Dataset Comparison with Multiple Advanced Models
We found many photos where the apparent age did not match the labeled age, and some images even lacked faces. Additionally, there is a data imbalance issue, with a severe lack of images in younger age groups. As a result, the model performance showed a noticeable decline when tested on this dataset. Due to LCA-GAN employing a more complex attention mechanism for pixel-level de-occlusion reconstruction, it exhibited the best performance on the high-quality AFAD dataset. However, its performance showed significant degradation on the lower-quality CACD and IMDB-WIKI datasets. Experimental results show that our method still performs well under lower image quality, as the layer-by-layer detailed feature reconstruction allows the model to focus on different feature dimensions of the image, which maximizes the model's robustness.
# Influence of Carbon: Metal Ratio on Tribological Behavior of Mo-W-C Coating
## 5 Conclusions  
The C/(Mo+W) ratios of C2, C4 and C6 are found as 5:1, 2.8:1 and 2.2:1, respectively. This ratio is crucial to describe Mo-W-C coating microstructure and related tribological properties:  
1. A decrease in C/(Mo+W) ratio leads to an increase in coating thickness, adhesion strength, hardness and elastic modulus values but a decrease in the degree of graphitization of as-deposited coatings.
2. Outstanding tribological properties (very low friction and negligible wear) are observed irrespective of the C/(Mo+W) ratio during lubricated sliding at ambient temperature.
3. Particularly low C/(Mo+W) ratios (2.8:1 and 2.2:1) are observed to be beneficial to achieve excellent tribological properties at 200 \({}^{\circ}\)C, and two keys to this are (i) in situ formation of solid lubricants MoS2 and WS2 and (ii) presence of amorphous carbon debris particles in the protective tribolayer. This tribolayer gradually lowers the friction coefficient with an increase in sliding distance by protecting both the coating and counterpart from severe wear. On the other hand, noticeable abrasive wear is observed for a high C/(Mo+W) ratio (5:1) at 200 \({}^{\circ}\)C.
4. Abrasive wear mechanism is observed throughout the sliding distance, whereas tribochemical wear mechanism is particularly dominant during the steady-state period irrespective of the C/(Mo+W) ratios.  
This research (a part of author's PhD) was jointly funded by IHI Hauzer Techno Coating BV and Sheffield Hallam University during 2012-2015. After acceptance of the manuscript, the APC was funded by University of Strathclyde, Glasgow.  
Not applicable.  
The experiments and data reported in this manuscript can be found in Author's PhD thesis 'Tribological Study of Novel Metal-Doped Carbon-Based Coatings with Enhanced Thermal Stability' from ETHOS (EThOS ID: uk.blethos.741529) or Sheffield Hallam University's research archive ([[http://shura.shu.ac.uk/20012/](http://shura.shu.ac.uk/20012/)]([http://shura.shu.ac.uk/20012/](http://shura.shu.ac.uk/20012/))).  
The work presented in this manuscript was performed as a part of the author's doctoral (PhD) study at Sheffield Hallam University, UK. The full PhD thesis can be obtained from ETHOS (EThOS ID: uk.blethos.741529) or Sheffield Hallam University's research archive ([[http://shura.shu.ac.uk/20012/](http://shura.shu.ac.uk/20012/)]([http://shura.shu.ac.uk/20012/](http://shura.shu.ac.uk/20012/))). The author would like to thank IHI Hauzer Techno Coating BV and Sheffield Hallam University, UK, for providing financial support during PhD (2012-2015) and University of Strathclyde, Glasgow for funding the APC.  
The author declares no conflict of interest.
# Hydrophobic Fluorinated Porous Organic Frameworks for Enhanced Adsorption of Nerve Agents
## 3 Results and Discussion
### Surface and Pore Hydrophobicity  
The surface and pore hydrophobicity of the synthesized materials were examined by contact angle and water vapor adsorption tests. The contact angles of C-1 and C-1-F were determined at 74 and 98\({}^{\circ}\), respectively (Figure 9). Given that the boundary between hydrophilicity and hydrophobicity is 90\({}^{\circ}\), it is clear that C-1 changed from a hydrophilic to a hydrophobic wetting behavior after fluorine doping.  
Furthermore, the hysteresis loop area observed for C-1-F was smaller than that for C-1 (Figure 10), suggesting that the adsorbed water vapors could be easily desorbed from the material. This narrowing hysteresis loop also suggested a weaker interaction between C-1-F and water vapor due to the more hydrophobic C-1-F channel.  
Figure 8: FT–IR spectra of (**a**) C-1 and (**b**) C-1-F under different relative humidity conditions. (Black, 33% relative humidity; red, 53% relative humidity; fluorescent green, 65% relative humidity; blue, 75% relative humidity; purple, 85% relative humidity; dark green, 97% relative humidity.)  
Figure 9: Contact angles determined for (**a**) C-1 and (**b**) C-1-F.
## 2 Theoretical Background
### Objective Functions
#### 2.1.4 Strain Energy  
Another index sensitive to damages is modal strain energy (MSE). In this method, damage in a region is located between two structural DOFs if a decrease in strain energy is caused. A change in strain energy has an inverse relation with the damage in the structure. MSE for a particular mode for a healthy and a damaged structure is given as:  
\[\text{MSE}=\mathbf{\phi}_{j}^{\prime}\mathbf{K}\mathbf{\phi}_{j} \tag{8}\]  
\[\text{MSE}^{*}=\mathbf{\phi}_{j}^{*\prime}\mathbf{K}\mathbf{\phi}_{j}^{*} \tag{9}\]  
where MSE is the healthy-simulated strain energy for the \(j\)-th mode shape and MSE* is thedamage-simulated strain energy for the \(j\)-th mode shape.  
Therefore, for the undamaged and damaged structure, the difference in MSE is shown as:  
\[F_{4}=\sum_{i=1}^{n}|\mathbf{\phi}_{i}^{*\prime}\mathbf{K}\mathbf{\phi}_{i}^{*}-\mathbf{\phi}_ {i}^{\prime}\mathbf{K}\mathbf{\phi}_{i}| \tag{10}\]The combined objective function is given as:  
\[\begin{array}{l}\begin{array}{l}\prod_{1}=F_{1}+F_{2}\\ \prod_{\text{II}}=F_{1}+F_{3}\\ \prod_{\text{III}}=F_{1}+F_{4}\end{array}\end{array} \tag{11}\]  
Minimization of these functions will maximize the correlation of the experimental and analytical model. Furthermore, the Objective Functions II and III require mode shapes to be expanded (to address the mismatch between the DOF in the FE model and experimental data), which has been done using the expansion using modal data (EMD) technique [42].
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
###### Abstract
An intuitive approach for calibrating the temperature forecasts is also adopted to account for the trend of past temperature forecast errors. To accommodate real-time operation purposes and deal with any unexpected changes in system net load, a real-time short-term load adjustment tool is also developed. An interpolation technique [20] based on the historical load values over the previous 15 min and their differences with the current STLF values is employed to achieve a real-time forecast adaptation in which the load forecast is updated every 5 min, for real time operation applications. The main contribution of this work is to develop a systematic procedure to combine the methodologies of statistical modeling and machine learning in two consecutive stages, so as to provide accurate forecasts of the load demand. To address overfitting in the forecast model used in the statistical modelling stage, the important and effective factors are identified by means of a variable selection procedure, together with an error-reducing calibration of the forecasted temperature using a suitable non-linear transformation. RNN machine learning models are then constructed, to capture the uncertainties of the complex and non-linear load patterns. The procedure for real-time short-term load demand adjustment further enhances the real-time forecast accuracy. As a result of this improved forecasting, the system operator can begin earlier preparations to reduce the impact of net load intermittency, particularly in systems with higher renewable penetration.