esa-sceva/satcom-chunk-collection huggingface dataset:
# Grain Boundary--A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction
## 3 Results and Discussion
### Theoretical Prediction of GB-Assisted HER
#### 3.1.3 Exchange Current Density  
For the purpose of the application of the GB concept in the HER experiment, we calculated the exchange current density (\(j_{0}\)), the most common experimental descriptor, to measure the catalysts' HER activity [10; 26]. According to the micro-kinetic model, at equilibrium, \(j_{0}\) can be theoretically computed as an indirect function of \(\Delta\)G\({}_{\text{H^{*}}}\)[10],  
\[j_{0}=Fk^{0}C_{total}\Big{[}(1-\theta)^{1-\alpha}\theta^{\alpha}\Big{]} \tag{4}\]  
\[\theta=\frac{exp\Big{(}-\Delta\text{G}_{*\text{H}}\Big{/}k_{\text{B}}T\Big{)} }{1+exp\Big{(}-\Delta\text{G}_{*\text{H}}\Big{/}k_{\text{B}}T\Big{)}} \tag{5}\]  
where \(k^{0}\) is the standard rate constant, \(\alpha\) is the transfer coefficient (\(\alpha\) was set to 0.5 in this work), \(C_{total}\) is the total number of HER active sites on the surface of the catalyst, and \(k_{B}\) is the Boltzman constant. The relationship of catalysts correlating E\({}_{\text{B}}\)(H*) is presented with  
Figure 2: **(a)** The calculated free—energy diagram of HER at the equilibrium potential (U = 0 V) for Au(110) (black), Pt(111) (green) and different active sites on Au GBs. **(b)** The volcano relation of the limiting potential for HER as a function of E\({}_{\text{B}}\)(H*). The arrow indicates the desired direction for catalyst design with higher activity, and the color pentagrams represent GB sites on Au surface for prodigious promotion of HER. Theoretical overpotentials (\(\eta_{\text{HER}}\)) are the vertical difference between the points and the equilibrium potential (red dashed line).  
the logarithm of \(j_{0}\) (\(\log j_{0}\)) (Figure 3a). Similar to the UL-E\({}_{\text{B}}\)[H*] plot, a typical volcano plot is also clearly observed, which is consistent with that in a previous study [10]. Pt and other HER-active materials are close to the top of the volcano curve, which denotes the scaling relationship between \(\log j_{0}\) and E\({}_{\text{B}}\)[H*]. The flat Au(110) surface has \(\log j_{0}=-10.88\) A-cm\({}^{-2}\) at E\({}_{\text{B}}\)[H*] = 0.50 eV, which implies that Au(110) is not a good HER catalyst because its \(j_{0}\) is extremely small. Once GBs are formed on the Au(110) surface, the \(\log j_{0}\) of the sites around the GBs is significantly increased (s1-s5), and they shift up toward the volcano top. s1-s3 are promoted several to a dozen million times in \(j_{0}\) with respect to the Au(110) surface. It is more surprising that, for s1 and s2, \(\log j_{0}\) jumps to \(-3.30\) A-cm\({}^{-2}\), which is close to the volcano top, outperforming Pt(111) (\(\log j_{0}\)=\(-3.50\) A-cm\({}^{-2}\)). This dramatic increase in \(j_{0}\) is also ascribed to the strong H* binding capability caused by GBs on the Au surface.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 7 Conclusions  
Based on the pull-out tests, numerical analysis, parametric study, and analytical works on the notched perfobond shear connectors, the following conclusions can be drawn:  
1. The pull-out failure modes of notched perfobond shear connectors are characterized by cracks in the concrete slab, shear of the concrete dowel, and deformation of the perforating rebar. At specimen failure, the perfobond ribs are pulled out from concrete and induce great tension forces in the concrete slab. The concrete dowels in the hole fail in shear. The rebars in the notched hole are not pulled out, while those in the circular hole yield with large deformation. There was no obvious deformation in the perfobond ribs.
2. The proposed finite element method is validated by comparison with pull-out test results. The numerical results resemble the pull-out failures of perfobond shear connectors quite well. The analyzed pull-out resistances account for 105% and 97% of the mean test results of perfobond shear connectors with circular holes and notched holes, respectively. It is indicated that the  
Figure 21: Evaluation of pull-out resistance equations. (**a**) [PERSON] et al.; (**b**) [PERSON] et al.; (**c**) proposal.  
proposed finite element model can be used to generate reasonable parametric results for pull-out tests of notched perfobond shear connectors.
3. A total of 33 parametric simulations further indicate that the pull-out resistance of notched perfobond shear connectors could be greatly increased by increasing the hole diameter, the thickness of the perfobond rib, the concrete strength, and the diameter and strength of the rebar in the hole. The strength of the structural steel has little influence on the pull-out resistance of notched perfobond shear connectors. When the cut ratio \(c_{w}/d_{p}\) is greater than 0.75, the pull-out resistance of the notched perfobond shear connector reduces sharply.
4. An analytical model is proposed to estimate the pull-out resistance of notched perfobond shear connectors. Compared with the existing equations for shear resistance, the calculated pull-out resistance from the proposal in this study agrees reasonably well with the experimental and numerical results. Therefore, the proposed equation may be used to evaluate the pull-out resistance of notched perfobond shear connectors.  
Further pull-out tests are still required to extend the scope of the proposed equation for notched perfobond shear connectors in steel and concrete composite structures.  
[PERSON]. ([PERSON]) proposed the topic of this study; [PERSON] designed the process and wrote the paper; [PERSON]. ([PERSON]) performed the finite element analysis; [PERSON] designed the pull-out tests.  
The research described in this paper was financially supported by Grants from Natural Science Foundation of China (51808235), Fujian Provincial Natural Science Foundation of China (2018)05083), and Huaqiao University Scientific Research Funding (16 BS804).  
The authors declare no conflict of interest.
# Failure Mode and Effect Analysis for Cyber-Physical Systems
## 6 Failure Mode and Effect Analysis Application
### Discussion  
While FMEA is a risk analysis method designed for industry and industrial processes, applying its method to CPS has with some limitations. The subdivision step of the FMEA system made our study faster and more intuitive since we intended to focus on the network communication component of the CBTC system. The quantification of risk through RPN ranks also contributed positively to our study. Identifying which failure mode was the most critical for the system was a fundamental part of the process. To assess the system from an economic impact point of view, we had to create new parameters to calculate the RPN equivalent, since the traditional parameters were not suitable. The previously mentioned limitations of the standard RPN formula, that consists of the multiplication of all parameters, prompted us to propose a new formula to estimate RPN ranks, which not only consists of the sum of all parameters but also uses assigned weights to each one of the parameters. Regarding our RPN calculation formula, we perfected the weights as the result of several RPN calculation tests. These final weights are the ones that best portray the economic impact of the parameters. Nevertheless, in other studies, the study team may apply different weights to these parameters, and their approach would neither be wrong nor worse. According to our evaluation, the presented values were considered fit to the circumstances of the study case.  
In this work, we demonstrated how FMEA can be applied to the CBTC system, more precisely to its network communication component. Other CBTC subsystems will be analyzed as future work because they also have vulnerabilities, and are crucial to the critical functioning of the system. We focused our study on system failures caused by cyber-attacks since these are one of the primary sources of failures in the system. We conducted our analysis to assess the risk through an economic impact perspective, however, we will consider other perspectives of risk analysis in our future work.
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 2 Related Works  
### Unsupervised 3D Shape Feature Learning  
Due to the rapid growth of 3D shapes, significant progress has been made in unsupervised 3D shape feature learning. Many research works [22; 23; 24; 25; 26; 27; 28] have been proposed to learn 3D shape features from various 3D modalities, such as multiple views, point clouds, meshes, and voxels. For instance, [PERSON] et al. [23] proposed an autoencoder-based 3D point capsule network to extract 3D shape features via point cloud reconstruction. The method in [26] extracted structure-preserving 3D shape features by effectively encoding the local geometry structures of 3D meshes. [PERSON] et al. [28] proposed a recurrent neural network (RNN) architecture to learn global 3D features via the multiple view inter-prediction task. Furthermore, considering the multi-modal characteristics of 3D shapes, several cross-modal learning methods [18; 19; 29; 30] have been proposed to boost the quality of 3D shape features by adequately exploiting information from different modalities. For example, [PERSON] et al. [29] proposed a 3D generative adversarial network to capture 3D shape features by reconstructing 3D voxels from 2D images. [PERSON] et al. [30] introduced a view encoder into the voxel autoencoder network to learn robust 3D shape features based on image-to-voxel generation. Nonetheless, the above methods are not oriented toward clustering tasks, thus it is difficult to ensure that the learned features are suitable for clustering.
# Monitoring the Capillary Jet Breakage by Vibration Using a Fast-Video Camera
## 4 Results and Discussion
### Mean Diameter and Size Dispersion Distribution  
The droplet diameter was determined by direct measurement using the image analysis. It could be determined from the volume of the droplet using Equations (7) and (8). A good correlation was found between these two approaches (Figure 5C).  
Figure 5: (**A**) Droplet size distribution (Nozzle 750 μm, 300 Hz) in relation to the normal law. (**B**) Jet expansion versus the Weber number (Nozzle 450, 750, and 1000 μm, Amplitude 4). (**C**) Comparing the droplet diameter by direct measurement and volume of the droplet using three nozzle sizes.  
The size distribution was fitted well on the normal log law displayed in Equation (10) (Figure 5A). The size dispersion varied between 5% and 9% without natural tendency in the nozzle size and frequency (Figure 4C). However, the size dispersion was lower with the metallic membrane.  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x-1}{\sigma}\right)^ {2}} \tag{10}\]
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
###### Abstract  
Agro-hydroological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydroological systems for effective water distribution under climate change impacts. A climate-smart agro-hydroological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976-2005) and future (2010-2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.  
agro-hydroological model; water management; climate change; big data analysis; irrigated rice +
Footnote †: journal: Applied Sciences
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.4 Vibration Energy  
_Vibration energy_ based on piezoelectric can be used to charge the battery of sensor nodes to prolong their lifetime. [PERSON] et al. [127] have been analyzed various WSN protocols based on ZigBee (CC2420 and CC2500) and CC1100 with respect to their applicability in the agricultural setting. Furthermore, a fully synchronous protocol with a time-slot architecture for a WSN has been proposed for agricultural machinery to achieve real-time capability, low latency, and deterministic behavior. The objective of their application was to monitor the back door position and filling level of a forage wagon using a ZigBee (CC2420) RF transceiver. Clock synchronization among all nodes is implemented to ensure strict power on/sleep times of each sensor node. Meanwhile, an energy-harvesting unit based on a piezoelectric material has been designed; the unit provides an average power of 200 uW to sensor nodes. In this case, the sensor node can transmit a data payload of nine bytes in 40 ms.
## 3 Improved LEACH Protocol
### Clustering Based on Underwater Energy Propagation Model
#### 3.2.2 Improved Clustering Algorithm  
According to the above discussion, the energy consumption can be reduced by minimizing the total communication distance of nodes in the network, which can be implemented by optimizing the cluster number. Therefore, we propose an improved clustering algorithm by adding a cluster number optimization step into the conventional K-means clustering algorithm. Assuming that the position and the initial energy of each node are known, the proposed clustering algorithm is described in Algorithm 1.
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 2 Description of the Multi-UAV Cooperative Attack Problem
### Targets and UAVs  
Let \(T=\{T_{1},T_{2},\ldots,T_{M}\}\) be the set of \(M\) stationary targets, it is assumed that the information of targets is known at the initial time. Each target requires \(m\)-different types of resources, the resource requirement of target \(T_{j}\) can be expressed as Equation (1), for example, \(R^{T_{j}}=(3,3,2)\) denotes that there are three types of resources required to destroy target \(T_{j}\) and the required amount of each resource is 3, 3 and 2, respectively.  
\[R^{T_{j}}=(R_{1}^{T_{j}},R_{2}^{T_{j}},\ldots,R_{m}^{T_{j}}),\ j=1,\ldots,M \tag{1}\]Let \(Value=(Value_{1},\ldots,Value_{M})\) be the set of values of the targets at the initial time, the value of targets decreases with time as follows:  
\[reward_{j}=Value_{j}\cdot e^{-\beta_{j}} \tag{2}\]  
where \(\beta>0\) is a user-defined coefficient, which denotes the descent rate of the target's value. \(t_{j}\) is the time of arrival of the UAV coalition assigned to attack the target \(T_{j}\).  
Let \(U=(U_{1},U_{2},\ldots,U_{N})\) be the set of \(N\) heterogeneous UAVs. The heterogeneity of UAVs is reflected in their different dynamic characteristics and the amount of resources as shown below:  
\[R^{U_{i}}=(R_{1}^{U_{i}},R_{2}^{U_{i}},\ldots,R_{m}^{U_{i}}),\;i=1,\ldots,N \tag{3}\]  
It is assumed that all UAVs fly in a two-dimensional plane. The motion of UAV \(U_{i}\) is governed by the following equation:  
\[\left[\begin{array}{c}\dot{x}_{i}\\ \dot{y}_{i}\\ \dot{\varphi}_{i}\end{array}\right]=\left[\begin{array}{cc}\cos\varphi_{i}& 0\\ \sin\varphi_{i}&0\\ 0&1\end{array}\right]\left[\begin{array}{c}V_{i}\\ \omega_{i}\end{array}\right] \tag{4}\]  
where \((x_{i},y_{i})\) and \(\varphi_{i}\) represents the Cartesian coordinates and the heading angle of UAV \(U_{i}\), respectively. \(V_{i}\) and \(\omega_{i}\) denote the velocity and turn rate, respectively. It is assumed that the UAVs fly at a constant and same velocity.  
The control input for each UAV can be described as: \(u_{i}(t)=\omega_{i}(t)\), which is subject to the constraints in Equation (5). \(R_{\min}^{i}\) is the minimum turning radius of UAV \(U_{i}\).  
\[\omega_{i}\leq\left|V_{i}/R_{\min}^{i}\right| \tag{5}\]  
In this paper, the UAVs were required to perform the attacking mission to the targets. The objective of the mission planning problem is to obtain the assignment strategy for each UAV to maximize the objective function and provide flyable and safe paths for these UAVs.
## 2 Materials and Methods
### Characteristic of Test Specimens and Electrochemical Cell for the Monitoring of Corrosion  
The reinforced concrete specimens were prisms with dimensions of 150 \(\times\) 150 \(\times\) 150 mm, as depicted in Figure 2. In each specimen were three bars of steel, two for the evaluation of the corrosion behavior (monitoring of \(E_{\text{corr}}\) and \(i_{\text{corr}}\)). The bars were of 3/8\({}^{\prime\prime}\) of diameter, the first of GS and the second of CS, these bars were used as working electrodes (WE), and the third was an AISI 314 SS steel bar as auxiliary electrode (AE), according to the ASTM G-59 standard [82]. The composition for the AISI 1018 CS and GS rebars is included in Tables 6 and 7, respectively. The galvanized process was performed according to ASTM A767, specifying a minimum thickness of 85-87 \(\upmu\)m [83]. Typical coatings thickness for rebars is in the range of 110-120 \(\upmu\)m [83]. The galvanizing process consists of the reaction of steel and molten zinc, producing a coating on top of the steel. This coating is composed of iron-zinc alloy layers such as gamma, delta and zeta that grow from the steel. The outermost layer is composed of pure zinc or eta phase. One advantage of galvanizing compared to typical coatings is that the bonding mechanism depends on the inter-alloying process between the steel and the molten zinc [84].  
To evaluate the \(i_{\text{corr}}\), linear polarization resistance (LPR) technique was applied. A three-electrode configuration was used consisting of the above mentioned WE and AW, and a copper/copper sulfate (CSE) reference electrode (RE), as depicted in Figure 3[85; 86]. The curing stage of all specimens was carried out by immersion in water for 28 days, according to NMX-C-159 standard [87]. After the curing period, the specimens were placed in two environments, DI-water (control medium) and 3.5 wt.% MgSO\({}_{4}\) solution (aggressive medium), the exposure time was for more than 300 days.  
A galvanostat/potentiostat Gill AC (ACM instruments) was used for corrosion monitoring (LPR), with a standard copper-copper sulfate (Cu/CuSO\({}_{4}\)) as reference electrode. The parameters used to perform the LPR test were the same as those used by other researchers [88; 89; 90], the sweep potential was \(\pm\)20 mV with respect to the corrosion potential and the sweep rate was 10 mV/minute, the IR drop potential was considered. The corrosion current density (\(i_{\text{corr}}\)) values were estimated from the polarization resistance (\(R_{\text{p}}\)) data using Stern and Geary equation (see Equation (1)):  
\[i_{\text{corr}}=\frac{\text{B}}{R_{\text{p}}} \tag{1}\]  
where B is Stern-Geary constant (B = 26 mV for carbon steel in active state) [91].
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Pigment Analysis
#### 2.5.6 Mixture Tuned Matched Filtering  
Mixture-tuned matched filtering (MTMF) is a hybrid method based on both linear mixture theory and signal processing [34]. MTMF results are presented as two sets of images, the infeasibility images and the matched filter images with values from 0 (no match and feasible) to 1 (perfect match and infeasible).  
MTMF does not require the knowledge of all the endmembers due to the fact that it maximizes the response of a known endmember and suppresses the response of the composite unknown classes to match the known signatures [35].
## 5 Conclusions  
This paper compared different AI models in order to develop the best crop yield prediction model for the midwestern US. We constructed the input dataset from satellite-based vegetation indices, and meteorological and hydrological variables, in accordance with a high-resolution CDL. Through experiments to examine the effects of phenology using three periods (GS, JA, and OC), we selected the JA database as the best months to predict corn and soybean yields. Using the optimal input database, we built six major AI models for crop yield prediction and conducted a comprehensive and objective comparison of the AI models. Particularly for the DNN model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio, to improve the crop yield prediction accuracy. Consequently, the DNN model with the JA database outperformed the other five AI models, with prediction errors of approximately 7.6% and 7.8% for corn and soybeans, respectively, which is a better result than those of previous studies. In terms of MAE, our DNN model with the JA database was approximately 21-33% and 17-22% more accurate for corn and soybeans, respectively, than the other five AI models. Our model showed a correlation coefficient of 0.945 for corn and 0.901 for soybean. This was a better result than that previous research using AI models, although the experiment conditions were different in terms of the crop types and study areas (Table 8). This indicates that our DNN with JA model can forecast the corn and soybean yields very accurately for a given year in advance, at the end of August or beginning of September. The optimized DNN model developed in this study can also be adopted in other regions and/or for other crops, only if the parameter optimization is conducted for a new region (or crop). Additionally, we analyzed the spatial characteristics of the prediction errors using Gi*. Prediction errors had a tendency to cluster spatially in some areas in several years, indicating that the relationship between crop yield and land environmental factors can have a locally unique or regionally heterogeneous pattern, and that it will be necessary to employ spatial statistical methods to solve these problems. Therefore, a combination of the optimized DNN model and a spatial statistical model, such as GWR, should be investigated in a future work for greater accuracy improvement. This could also help mitigate the slight tendency toward underestimation of our soybean yield model. Also, more appropriate input variables, such as the growing degree days, should be added to a modified DNN model.  
**Author Contributions:** Research design and supervision by [PERSON]; data processing and analysis by [PERSON]; Manuscript preparation by [PERSON] and [PERSON]; literature review and discussion by [PERSON], [PERSON], [PERSON], and [PERSON].  
**Funding:** This research was funded by the NRF (National Research Foundation) of Korean government (2017R1D1A1B03034245 and 2018R1A6A3A01013215).  
**Conflicts of Interest:** The authors declare no conflict of interest.