esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Thin Cement-Based Renders on the Structural Response of Masonry Wall Panels
## 2 Description of the On-Site Experimental Campaign
### The Render  
Today, the most commonly used materials for coating renders are ordinary Portland cement (OPC) with or without lime, which are mixed along with sand and water in various proportions to form a hardened adhesive material. Wall panels cut off from building No. 2 were rendered with a cement-based render. This was applied on both sides with a thickness of approx. 20 mm.  
A [PERSON] hammer was used to estimate the in-place strength of the mortar render. The test consisted of hitting the render with a spring-driven pin at a defined energy, and then the rebound value was measured. It is well known that the rebound depends on the surface hardness of the tested material and, by referring to conversion tables or formulas, the rebound result of the test can be used to determine its compressive strength. For a horizontal impact on a vertical surface, the probable cube strength of the material can be estimated using the conversion table of the producer (Schmidt Hammer Type N, Proceq) [32]. After discarding the highest and lowest values, results from 15 on-site tests showed an average value of the compressive strength of 18.8 MPa, with a coefficient of variation (CoV) of 23.3%. This value could be reduced to a characteristic strength \(f_{ck}\) of 150 mm diameter by 300 mm concrete cylinder of 12 MPa (strength class C12/15). The tensile strength could be finally estimated using [33]:  
\[f_{t,render}=\frac{0.3\times\left(f_{ck}\right)^{2/3}}{1.5}=1.047\ MPa, \tag{1}\]
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 3 Results
### Seasonal Analysis  
Table 3 shows the results of ITA performed to detect seasonal trends in the climate. These indicate that air temperature has an upward trend, short-wave radiation a downward trend, and long-wave radiation shows a downward trend in the lakes of central Yunnan and an upward trend in the lakes in northwest Yunnan. No significant changes were detected for snowfall, whereas wind speed and relative humidity have downward trends. In spring, rainfall shows an upward trend, which is opposite to the trend detected for annual rainfall.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods  
Figure 2 shows the image defogging algorithm flow. First, we perform Retinex-based enhancement on the input image. Next, the image is binarized using Otsu thresholding to segment potentially dense haze and high-brightness regions. The binarization results are then fused with an edge map to isolate haze-affected regions while excluding bright objects. In order to improve the localization accuracy of atmospheric light regions, a morphological expansion operation is performed on the fusion result to obtain an approximate region of atmospheric light. Then, in the enhanced image, the pixel values of the positions corresponding to the top 0.1% brightest pixels in the corresponding region are selected, and their average values are the atmospheric light estimates. At the stage of acquiring the dark channel map of the image, the edge-preserving optimization technique is introduced to protect the edge information of the image, the mean value filter is used to obtain the mean dark channel map, and the weighted fusion of the minimum dark channel map (the original dark channel image) and the mean dark channel map realizes the compensation of the original dark channel, and at the same time, the transmittance value is refined. Furthermore, the depth map estimated using the CAP is corrected for transmittance values in the sky region via the dark channel prior. Finally, we recover the fog-free image using the atmospheric scattering model.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results  
Several experiments were performed to confirm our method's effectiveness. First, the quality of sharpened S2 images was evaluated through quantitative and qualitative comparisons with leading S2 sharpening methods. Further, the validity of the method architecture was confirmed using an ablation study of its key components. Finally, its extendibility was confirmed by applying it to S2 and Landsat 8 image fusion, and the results were compared with leading methods using [PERSON]'s protocol for evaluation at a reduced scale.
## 4 Discussion
According to the data presented in Figure 8b, the \(n\) values exhibit a range between 2.10 and 2.75 in the studied range of the strain and within the studied strain rates and temperatures, and this revealed that the grain boundary sliding and dislocation glide mechanisms controlled the hot deformation. The phenomenon of grain boundary sliding is observed to occur simultaneously with continuous dynamic recrystallization in both deformed region and substructured volume as the applied strain increases. The observed decrease in the \(n\) value at higher strains could potentially be attributed to the development of a recrystallized grain structure dominated in all samples. Furthermore, it is likely that the process of active grain boundary sliding is facilitated by dislocation mechanisms. The material constant \(\alpha\), as shown in Figure 8d, exhibits a consistent decrease as the strain increases, and the same characteristic is observed for the activation energy \(Q\). The specific deformation mechanisms and related mechanisms were also proposed by the effective activation energy of deformation [84]. The Q value exhibited an impressive decrease, reaching a strain value of 0.6 (Figure 8a). The observed reduction in \(Q\) as strain increases is consistent with the findings reported in previous studies [82; 83; 85; 86]. The observed decrease in the \(Q\) value provides clear evidence of a reduction in the stored energy within the material as a result of strain [84; 87].
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 6 Conclusions  
Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems. The propagation-based recommendation methods are mainstream knowledge graph-based recommendation methods, but they usually ignore the complex relations between entities in the multi-hop ripple sets and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. To overcome these shortcomings, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT). This method can learn refined ripple set embeddings, thereby generating accurate user  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(K\) & **2** & **3** & **4** & **5** \\ \hline Last.FM & 0.1173 & **0.1481** & 0.1071 & 0.0762 \\ Book-Crossing & 0.0208 & 0.0298 & **0.0603** & 0.0450 \\ MovieLens 20M & 0.0477 & 0.0672 & 0.1014 & **0.1319** \\ Dianping-Food & 0.0570 & 0.1257 & 0.1345 & **0.1855** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Recall@10 results in terms of the number of neighboring entities \(K\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(L\) & **1** & **2** & **3** & **4** \\ \hline Last.FM & 0.1042 & 0.1269 & **0.1481** & 0.1203 \\ Book-Crossing & 0.0371 & **0.0603** & 0.0417 & 0.0201 \\ MovieLens 20M & **0.1319** & 0.1072 & 0.0808 & 0.0742 \\ Dianping-Food & **0.1855** & 0.1409 & 0.1125 & 0.1061 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Recall@10 results in terms of the maximum hop number \(L\).  
embeddings and item embeddings, so as to accurately capture users' potential interests in items. The extensive experiments on four real-world datasets have demonstrated that the proposed CKGAT method outperforms the state-of-the-art methods for knowledge graph-based recommendation, in terms of recommendation accuracy and diversity.  
Our future work will focus on investigating how to extend the method to exploit the relations between users and the user information contained in social networks, so as to provide more accurate recommendations.  
Conceptualization, Z.X. and J.L.; methodology, Z.X. and J.L.; software, J.L. and H.L.; validation, J.L. and H.L.; formal analysis, Z.X., J.L. and H.L.; investigation, J.L. and H.L.; resources, Z.X. and Y.T.; data curation, J.L., H.L. and Q.Z.; writing--original draft preparation, J.L. and Z.X.; writing--review and editing, Z.X., H.L. and Q.Z.; visualization, J.L., H.L. and Q.Z.; supervision, Z.X.; project administration, Z.X. and Y.T.; funding acquisition, Y.T. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFC0405805 and the APC was funded by 2017 YFC0405805.  
Not applicable.  
The datasets are available from the URLs provided in the article.  
The authors declare no conflict of interest.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 3 Data Enhancement Network  
The data enhancement network is primarily divided into two parts: designing adversarial loss functions and constructing the generative model WDCGAN-SA with self-attention embedding.
## 5 Discussion
### The Availability of Remote Sensing Images  
Laver cultivation reveals a seasonal cycle, with a growing period spanning from winter to spring and a non-growing period from summer to autumn. Thus, the influence of laver cultivation on the tidal flat sediment is more obvious on the seasonal scale, especially before and after the establishment and demolition of the structures required for aquaculture. Impacted by the Jiang-huai quasi-stationary front (in June and July), images recorded in summer (from June to September) are heavily cloud-covered and the number of available images is significantly less than that of winter (from December to February). Thus, for coastal zones with complex depositional dynamics and rapidly varying topography, the availability of optical images is restricted when analyzing the characteristics of tidal flats geographic evolution within a short time. With the development of RS technology and improvements in the spatial and temporal resolution of satellite images, it will be possible to construct monthly-scale or even day-scale DEMs corresponding to the periods of laver sowing, growth and frame set up and demolition. With this data, more regular conclusions may be inferred.  
Meanwhile, the Synthetic Aperture Radar (SAR) data with all-weather day-night [55] capability compensates for the absence of optical images caused by imaging quality, such as ENVISAT, ESR, Sentinel satellites launched by European Space Agency (ESA). Therefore, coalescing multisource RS data such as optical images with ultra-high temporal resolution and SAR data will provide data support for inverting the changing terrain of tidal flats terrain.
## 4 Results Evaluation and Discussion
### Path Loss (PL) Analysis  
When discontinuities occur in communication, and the sensor nodes are no longer in communication, the results include a vast path loss, the signal not being able to be delivered from the source to the destination, and so on. The cooperation and relay optimization scheme can work in such situations in order to avoid path loss or communication disruption. The relay nodes can move from one side to another in order to fill the gaps in which the nodes become dull and are not able to communicate further. Figure 10 and Table 10 illustrate the PL evaluations of the Co-UWSN, CEER, NBEER, and CR-NBEER protocols in terms of a total simulation time of 1000 s. From the given illustrations, it can be concluded that, due to the cooperation and relay optimization, the proposed CR-NBEER protocol has a lower path loss value in dB than the other routing protocols (Co-UWSN, CEER, and NBEER). The critical factors are cooperative routing and advancements in relay optimization. On the other hand, in Figure 11, the total average evaluations of the four routing protocols are depicted, and it can be seen that the proposed protocol achieved better performance than the other three.  
From the overall illustrations and representations, and their discussion, it can be concluded that the sensor relay nodes operating in a cooperative manner have the best performance in terms of boosting the network function. The proposed CR-NBEER protocol achieved the best results in all considered scenarios. Due to advancements in the relay optimization schemes, the proposed protocol positively impacts the considered settings. Representations in graphical and tabular form are given in Table 11 and Figure 12, respectively. The overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was 26.7 ms, and NBEER was measured to be 27.6 ms, whereas CR-NBEER was measured to be 19.3 ms. Likewise, the overall EC of Co-UWSN was measured to be 10.759 j, CEER  
Figure 10: Evaluation of the path loss (dB) vs. simulation time (seconds).  
Figure 9: Average PDR evaluations of all four protocols.
## 2 Dynamic Model of DCT Vehicle with Mount
### Gear System Model  
The torsional dynamic model of a pair of gears is shown in Figure 2a. The dynamic model of a DCT gear system was established by the lumped parameter method [22; 23; 24], and the equations are as follows:  
\[I_{1}\ddot{\theta}_{1}+c_{12}r_{1}(r_{1}\dot{\theta}_{1}-r_{2}\dot{\theta}_{2 })+k_{12}r_{1}(r_{1}\theta_{1}-r_{2}\theta_{2})=T_{1}, \tag{7}\]  
\[I_{2}\ddot{\theta}_{2}+c_{12}r_{2}(r_{2}\dot{\theta}_{2}-r_{1}\dot{\theta}_{1} )+k_{12}r_{2}(r_{2}\theta_{2}-r_{1}\theta_{1})=T_{2}, \tag{8}\]  
where \(l_{i}\) (\(i\) = 1, 2) is the inertia of gear 1 and gear 2; \(\theta_{i}\) (\(i\) = 1, 2) is the torsion angle of gear 1 and gear 2; \(r_{i}\) (\(i\) = 1, 2) is the base circle radius of gear 1 and gear 2; \(k_{12}\) and \(c_{12}\) are the time-varying meshing stiffness and meshing damping of the gear pair, and \(T_{i}\) (\(i\) = 1, 2) is the torque acting on gear 1 and gear 2.  
During gear pair meshing, the meshing stiffness changes due to the alternating of the single and double tooth meshing, as shown in Figure 2b. The time-varying meshing stiffness is fitted as a function with a constant period change of the gear rotation angle by the Fourier series method [25; 26] as follows:  
\[k_{12}(\theta_{1})=\overline{k}_{12}+\sum_{l=1}^{\infty}a_{l}\cos[l(Z_{1} \theta_{1}+\gamma_{12})], \tag{9}\]  
where \(\overline{k}_{12}\) is the average value of the meshing stiffness within a meshing period; \(a_{l}\) is the Fourier expansion coefficient; \(l\) is the number of harmonics; \(Z_{1}\) is the number of teeth of gear 1; \(\gamma_{12}\) is the initial phase of the meshing stiffness; \(k_{s}\) is the meshing stiffness of a single tooth region and \(\epsilon_{\alpha}\) is the contact ratio.  
Figure 2: Dynamic model of gear system: (**a**) Dynamic model of gear pair; (**b**) Schematic diagram of time-varying meshing stiffness of gears.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
###### Abstract  
The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images.  
PolSAR; image classification; composite kernel; polarimetric features; spatial features; feature fusion +
Footnote †: journal: Applied  
_Applied_  
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## 2 Results and Discussion
### Assessment of Anti-Aging Properties  
In the subsequent study stage, the possibility of the tested hydrogels influencing the activity of two enzymes that degrade collagen and elastin fibers was investigated. For this reason, collagenase and elastase activities were determined. The results show that only hydrogels containing CBD, specifically H-CBD and H-CBD-TER, limit the activity of these enzymes. This impact is concentration-dependent, with the maximum concentration (5.0%) of H-CBD-TER inhibiting collagenase and elastase by 27.1 and 28.1%, respectively. H-CBD produced similar results, but H-TER had no significant effect on the activity of these enzymes (Figures 15 and 16).  
The ability to inhibit the activity of collagenase and elastase is an essential element in assessing the anti-aging properties of active compounds, both synthetic and of natural origin [69]. These enzymes are responsible for the breakdown of elastin and collagen in the extracellular matrix, which significantly affects the loss of strength and elasticity of the skin and interferes with proper wound healing and skin regeneration [70; 71]. Since their activity can be induced by various external factors, such as free radicals, UV radiation, and genetic conditions, compounds that can reduce the activity of these enzymes are constantly searched for [72]. In previous research, we showed that Cannabis sativa herb extracts can decrease the activity of these enzymes, which is most likely the result of the interaction of physiologically active chemicals found in this extract [71]. This work shows the possibility of inhibiting these two matrix metalloproteinases by CBD-loaded hydrogel and carriers  
Figure 16: Inhibition of elastase activity by the tested hydrogels. *** \(p<0.0001\), *** \(p=0.0095\) versus the control.  
Figure 15: Inhibition of collagenase activity by the tested hydrogels. *** \(p<0.0001\), *** \(p=0.0002\), *** \(p=0.0012\) versus the control.  
containing both CBD and TER. Since the endocannabinoid system (ECS) receptors, CB1 and CB2, have endogenous ligands located in the skin, cannabinoid compounds, including CBD, can act as a stimulant or an inhibitory agent for the ECS, responsible for maintaining homeostasis, regulating septum production, inhibiting or promoting the proliferation of keratinocytes, or inhibiting inflammatory promoters [73; 74; 75]. Thus, the regenerating and protective effects of cannabinoids and their positive effect on the skin aging processes may result from both the influence on the ECS and the inhibition of the activity of collagenase and elastase. As a result, innovative drug carriers containing molecules that can slow the aging process and increase skin regeneration are crucial in modern dermatology and cosmetology.
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 3 Experiments
### Experimental Results
#### 3.3.1 Case A  
We selected the 2013-2015 load data of eight regions and the whole system in the ISO dataset for model training and the 2016 data as the test dataset.  
To present the experimental effect clearly, Figure 4 intercepts the data from the 4000 th hour to the 4400 th hour of the year, showing the total load prediction results for these areas and the absolute error curves of the proposed LF-NSNP model, where the upper figure is the predicted absolute error curve, and the lower figure is the original load curve and the predicted load curve. Figure 4 presents the predicted load curve of the proposed LF-NSNP model, which can be more accurately matched with the actual load curve, and the absolute error can also be stable at a low level of 0-600. This indicates that the proposed LF-NSNP model can afford to splendidly capture the variation law of the actual load curve and extract the temporal correlation of short-term load characteristics.  
In the case study, the proposed LF-NSNP model is compared with the prediction model based on conditional residual modeling (CRM) [17]. The results of the CRM model are retrieved from Ref. [17]. Table 1 presents the predictive performance under the three metrics for the proposed LF-NSNP model and CRM model. The proposed LF-NSNP model outperforms the CRM model for all regions and metrics, as shown in Table 1. Moreover, compared with the CRM model, the proposed LF-NSNP model achieves more than 50% improvement in RMSE, MAE, and MAPE.  
Therefore, this case study illustrates that the proposed LF-NSNP model is clearly up to the short-term load forecasting task.
## 2 Simulation Models
### Grounding Grid Models  
This study investigates three variations of a grounding grid. To further elaborate, all three grids are constructed by 35 mm\({}^{2}\) copper, consisting of two main sections. The initial section comprises a meshed square grid (1.2 \(\times\) 1.2 m) positioned 0.3 m beneath the Earth's surface, while the second section involves a circular conductor positioned at the same depth, connected with the square section via copper. The grounding grid configurations, identified as Grids A, B, and C, are depicted in Figure 6. Specifically, Grid A serves as the foundational grounding grid, while in Grid B, the radius of the circular loop is increased by 50%. Additionally, Grid C incorporates a vertical rod when compared to Grid A and B. The primary objective is to mitigate touch and step voltages during SC occurrences, ensuring adherence to specified safety thresholds outlined in [9]. These values shall not exceed the limits, defined by the below-mentioned equations for a body of 70 kg [9]:  
\[\text{E}_{\text{step70}}=(1000+6C_{s}\rho_{s})\frac{0.157}{\sqrt{t_{s}}} \tag{2}\]  
\[\text{E}_{\text{touch70}}=(1000+1.5C_{s}\rho_{s})\frac{0.157}{\sqrt{t_{s}}} \tag{3}\]  
Figure 5: Protection coordination TCCs (Green: Fuse cut-outs, Red: Overcurrent Relay).  
The safety threshold for a person weighing 70 kg approaching a faulty area is marginally higher than for someone weighing 50 kg. Consequently, the scenario involving a 70 kg individual has been modeled as a best-case scenario. In the case that safety thresholds are not met in this case, they will not be met in the case of a 50 kg individual as well. The latter could be a subject of future research.  
The safety thresholds for touch and step voltages are automatically calculated by the SES CDEGS 19.0 software tool for each simulated scenario. However, according to [9], the values of Cs are determined using the following empirical equation:  
\[C_{s}=1-\frac{0.09\cdot\left(1-\frac{\rho}{\rho_{s}}\right)}{2\cdot h_{s}+0.09}\]  
where:  
\(\rho_{s}\) is the resistivity of the surface material in \(\Omega\)-m,  
\(\rho\) is the soil resistivity in \(\Omega\)-m,  
Figure 6: Grounding grid variations: (**a**) Grid A: top view of meshed grid with 1 m radius circular conductor, (**b**) Grid B: top view of meshed grid with 1.5 m radius circular conductor, (**c**) Grid C: top view of meshed grid with 1.5 m radius circular conductor with the addition of 1 vertical rod underneath the concrete pole, and (**d**) side view of Grounding Grid C.  
\(h_{\rm s}\) is the thickness of surface material in m.  
The value of \(t_{\rm s}\) represents the fault clearing time for each scenario utilized, determined by the operational sequence of the overcurrent relay depicted in Figures 4 and 5.