esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Inversion Results  
### Influence of Different Input Dimensions on the Inversion Results  
The trained CNN was used to predict the validation sample, and the RMSE values are reported in Table 6. The RMSE obtained by the first input condition (peak value of 10 deflection curves in Table 3) was found to be far greater than that obtained by the second input condition (all values of 10 deflection time history curves in Table 3). Therefore, the inversion of the entire deflection curves was more accurate than their peak values. The first input condition does not reflect the overall dynamic characteristics of the pavement structures. As a result, the inversion results have a non-unique problem. In this example, the deflection curves are used to replace the maximum values of the deflection curves as the training samples, which can reflect the dynamic characteristics of the pavement structure more comprehensively. The calculation results also show the high accuracy of the algorithm. The main reason why existing algorithms seldom use the entire deflection time-history curve for inversion is that they cannot cope with huge data sets. The CNN  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Best Training Sample** & **Training Set** & **Validation Set** & **Testing Set** & **Total** \\ \hline
1 & 600 & 100 & 100 & 800 \\
2 & 650 & 100 & 100 & 850 \\
3 & 700 & 100 & 100 & 900 \\
4 & 750 & 100 & 100 & 950 \\
5 & 800 & 100 & 100 & 1000 \\
6 & 850 & 100 & 100 & 1050 \\
7 & 900 & 100 & 100 & 1100 \\
8 & 950 & 100 & 100 & 1150 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The division of the second group of comparative training data sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Convolution Layer** & **Size** & **Step** &
\begin{tabular}{c} **Number of** \\ **Convolution Kernels** \\ \end{tabular} & **Padding** \\ \hline
1 & \(1\times 2\) & \(1\) & 60 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameter settings of the convolution layer.  
inversion model used in this study has better regression performance and can overcome this problem more effectively.
# Responses of Terrestrial Evapotranspiration to Extreme Drought: A Review
## 4 Mechanisms of Terrestrial ET Variation
### Atmospheric Evaporative Demand  
The atmospheric evaporative demand represents the demand for water from the atmosphere (i.e., its evaporating or drying power) [102]. The potential for atmospheric water evaporation can be divided into two parts [103]: (i) radiative, representing the available energy to evaporate water, which is primarily determined by solar radiation; and (ii) aerodynamic, representing the ability of the atmosphere to carry water, which is primarily determined by climate parameters, including air temperature, humidity, and wind speed.  
During droughts, rising air temperatures can enhance the transpiration rate by increasing the VPD, which is always considered as a metric of atmospheric evaporative demand [103; 24; 104]. However, the impacts of rising temperatures and vapor pressure deficits on terrestrial ET are nonlinear because vegetation tends to close its stomata to prevent excessive water loss when the atmospheric demand is high [24]. [PERSON] et al. [105] reported that the transpiration rate increases with the vapor pressure deficit, increasing to a critical maximum value, after which it remains stable or even declines as a result of stomatal closure.  
[PERSON] [103] developed the widespread combination equation for potential ET, which is generally expressed as:  
\[\lambda E_{p}=\frac{\Delta(R_{n}-G)+\rho_{a}C_{p}D/r_{a}}{\Delta+\gamma}, \tag{3}\]  
where \(\rho_{a}\) is the air density, \(\Delta\) is the slope of the saturated vapor pressure curve at a given temperature, \(C_{p}\) is the specific heat of the air constant pressure, and \(\lambda\) is the psychrometric constant. The variables \(\Delta\) and \(\gamma\) are functions of temperature, \(R_{n}\) is the net radiation, \(G\) is the soil heat flux, and lastly, \(r_{a}\) is the aerodynamic resistance of the heat flux.  
The Penman algorithm (Equation (3)) is a physics-based equation for estimating ET under homogeneous and unrestricted water supply conditions, so-called potential evaporation (PET). The PET predominantly determines terrestrial ET through the climate factors listed in the formula, which include solar radiation, humidity, wind speed, and temperature. The relationship between the ET variability and climate changes or climate variability explains why the earth's dimming or brightening, global warming, and calming of winds may all have an impact on terrestrial ET.
# Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
## 2 Pre-Preparation and Problem Description  
### Graph Theory  
\(\mathbf{G}=\{\mathbf{V},\mathbf{E}\}\) represents the communication topology between robots, in which each robot represents a node. \(\mathbf{V}\) is a set of nodes. \(\mathbf{E}\) is a set of edges, representing the connection state between robots. The topology map is represented by a Laplacian matrix, which is \(\mathbf{L}=\mathbf{D}-\mathbf{A}\). \(\mathbf{D}\) is the degree matrix, which represents how many nodes are adjacent to each node. \(\mathbf{A}=[a_{ij}]\) is the adjacent matrix and \(i,j\in\mathbf{V}\). \(N_{i}\) represents all sets of nodes adjacent to the \(i\) node. If node \(j\) is adjacent to node \(i\), then \(a_{ij}>0\). If \(a_{ij}=a_{ji}\) for any \(i,j\in\mathbf{V}\), the graph is an undirected graph; otherwise, it is a directed graph. If there is a directed path on any two nodes in the graph, the directed graph \(\mathbf{G}\) is strongly connected. If there is a directed path to a node in the graph to any other node, then the directed graph \(\mathbf{G}\) contains a spanning tree. If the undirected graph \(\mathbf{G}\) is strongly connected, it is called a connected graph. When the undirected graph \(\mathbf{G}\) is a connected graph, its Laplacian \(\mathbf{L}\) matrix contains a zero root, and the other eigenvalues are positive real numbers. When a directed graph \(\mathbf{G}\) contains a spanning tree, its Laplacian \(\mathbf{L}\) matrix contains a zero root, and the rest eigenvalue's real part are positive.
## 3 Creation of a Numerical Model of Basalt  
### Fundamental Introduction of FDEM  
The modeling domain of the FDEM code Irazu is divided into three-node triangular elements, where four-node crack elements are embedded between all adjacent element boundaries [41], as shown in Figure 6. The mechanical analysis of triangular finite elements adopts the theory of linear elastic continuum. The contact force is generated between all triangular element pairs, where the tangential friction force is calculated according to the Coulomb friction law, and the normal repulsive force is determined using the distributed contact force penalty function method. Adopting the Mohr-Coulomb and maximum tensile stress failure criteria, the cohesive crack elements used to simulate the progressive failure in the material may experience yielding and fracturing in mode I (tensile failure), mode II (shear failure), or mixed mode I-II (tensile-shear failure) based on the local stresses and relative displacements.  
The different defects in rocks can be embedded into an FDEM model, represented by pre-existing fractures distributed along the bedding planes. This method can be extended to a general case containing arbitrary structural planes (i.e., the DFN model), which is called the DFN-FDEM model; that is, the DFN model representing defect structures is combined with the FDEM model representing the rock matrix. A DFN model includes two sets of properties: (1) DFN geometric parameters, such as the length, spacing, orientation, and density of cracks. (2) DFN mechanical properties, composed of crack elements that are different from the mechanical properties of the rock matrix. Based on the bonding force of the discontinuities, crack elements can be divided into two types: cohesive type and  
Figure 6: Element mesh and constitutive models of crack elements in an FDEM model [41]. (**a**) Element mesh in an FDEM model; (**b**) cohesive model for mode I; (**c**) slip-weakening model for mode II; (**d**) I-II mixed mode.  
fractured type. The mechanical behavior of cohesive crack elements is the same as that of the above four-node cohesive crack element model; however, for the crack element of fractured type, the removal of crack elements produces a pure friction discontinuity surface, of which the mechanical behavior is only controlled by the contact model.
## 2 Related Works
### LSTM-Based Prediction for QoS Enhancements  
The prediction of network traffic has gained significance in contemporary scenarios, such as anomaly control, congestion regulation, and bandwidth management. In [29], a framework utilizing LSTM for network traffic prediction was proposed by employing real network traces and aiming for predictions within very short timeframes. To tackle the diversity of network traffic, a feature-based clustering method was employed as a preprocessing stage to group similar time series. The experimental results indicated that the LSTM-based framework performed better in predicting network traffic with minimal errors. In [30], the effectiveness of LSTM in predicting mobile traffic was studied. The authors used datasets that contained multivariate traffic information directly from the LTE control channel's downlink control information. This paper aimed to enhance QoS by focusing on network delays, which include sending, propagation, processing, and queuing delays. In [31], a model named Nefopam was introduced for predicting network flow delay. It combined LSTM and graph convolutional neural networks to effectively capture the spatiotemporal attributes of network flow data. Nefopam incorporated three components (recent, daily cycle, and weekly cycle) to characterize the temporal and spatial aspects. These component outputs were integrated for the final prediction result.
###### Abstract  
The application of donor-acceptor (D-A) conjugated polymer catalysts for hydrogen evolution reaction (HER) has shown great promise because of the tunability of such catalysts to have desired properties. Herein, we synthesized two polymer catalysts: poly[4,4'-(9-(4-aminophenyl)-9H-carbazole-3,6-diamine-_alt_-5-oxido-5-phenylbenzo[b]phosphindole-3,7-diyl)dibenzaldehyde] (PCzPO) and poly[N\({}^{1}\),N\({}^{1}\)-bis(4-amino-2-fluorophenyl)-2-fluorobenzene-1,4-diamine-_alt_-5-oxido-5-phenylbenzo[b]phosphindole-3,7-diyl)dibenzaldehyde] (PNoFPO). The UV-vis absorption spectra showed that the less planar structure and the presence of electronegative fluorine atoms in the donor group of PNoFPO led to a higher optical gap compared to PCzPO, leading to almost five times faster HER rate using PCzPO compared to PNoFPO. However, density functional theory (DFT) calculations show that the frontier orbitals and the highest occupied molecular orbitals - lowest unoccupied molecular orbitals (HOMO-LUMO) gaps of PCzPO and PNoFPO D-A moiety models are very similar, such that, during light absorption, electrons move from donor to acceptor group where proton binding is preferred to happen thereafter. For both PCzPO and PNoFPO D-A moieties, H\({}_{2}\) formation through an intramolecular reaction with a barrier of 0.6-0.7 eV, likely occurs at the acceptor group atoms where protons bind through electrostatic interaction. The intermolecular reaction has nearly zero activation energy but is expected to occur only when the repulsion is low between separate polymers chains. Finally, experimental and DFT results reveal the importance of extended configurations of D-A polymers on HER rate.  
hydrogen evolution reaction; donor-acceptor conjugated polymer; density functional theory; optoelectronic properties; photocatalysis +
Footnote †: journal: Applied Chemistry and Chemical Engineering
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology  
### Program Design  
Figure 1 shows the flow of the real-time water level monitoring algorithm based on GNSS dual-antenna attitude measurement. Firstly, the GNSS data received by the two antennas are parsed to extract the valid data from them. The satellite position and receiver position are decoded from GNSS data. Then, the Geometric Dilution of Precision (GDOP) [35] satellite selection algorithm is used to obtain the best combination of satellites for attitude solution, and the observed elevation angles of all satellites in the combination are calculated. The satellite with the highest observation elevation angle is selected as the reference satellite, and the attitude measurement observation model is established. Using the weighted least squares method to solve the observation model, the float solution of the baseline vector and the integer ambiguity can be obtained. Next, the Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) [36] algorithm is applied to the float solution to search for ambiguities and obtain a fixed integer ambiguity. The fixed integer ambiguity is then used to correct the baseline vector's float solution, resulting in a corrected baseline vector. Subsequently, the attitude solution algorithm can be used to determine the length, azimuth and pitch angle of the baseline formed by the two antennas. Finally, the water level is calculated using the baseline length.
## 4 Conclusions  
We have shown that the VPD method can be successfully used to fabricate NP Al starting from an Al\({}_{20}\)Zn\({}_{80}\) nanocomposite prepared by mechanical processing. Annealing the Al\({}_{20}\)Zn\({}_{80}\) nanocomposite at 550 \({}^{\circ}\)C for 1 h allows removing Zn almost completely, leaving an NP Al structure with a high surface area. Moreover, using the SBF-SEM technique, a 3D reconstruction of the NP structure was obtained. A fractal model based on the Sierpinski carpet as a unit was proposed, and it showed the ability to model the porous microstructure of NP Al well. This phenomenological model allowed us to predict some features of this material, such as specific surface area and Young's elastic modulus, which are very close to experimental ones. The notable capability shown by this model to reproduce the experimental data encourages the development of this methodology which can in principle be applied to any possible NP metals.  
The fabrication of NP Al with this method is unprecedented and can mark progress in the field. Al is a lightweight metal almost ubiquitous in modern technology and the availability of a lighter version of the metal can pave the way for a number of applications in strategic sectors of industry.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11146639/s1](https://www.mdpi.com/article/10.3390/app11146639/s1)]([https://www.mdpi.com/article/10.3390/app11146639/s1](https://www.mdpi.com/article/10.3390/app11146639/s1)), Figure S1: EDS spectra, Figure S2: Examples of inverted SEM-SBF, Figure S3: Inverted and binarized SBF-SEM image, Figure S4: 3D reconstructions of NP Al, Video S1: Representative video of the porous structure evolution during the subsequent cuts of the entire cross-section profile.  
Conceptualization, A.P., G.P., F.D. and L.P.; investigation, A.P., M.F.C., E.S., A.F. and L.P.; modelling, G.P.; image processing and analysis, A.P., G.P., E.S. and A.F.; writing--original draft preparation, A.P., G.P., F.D., E.S., A.F. and L.P.; writing--review and editing, A.P., G.P., M.F.C., F.D., E.S., A.F. and L.P.; supervision, G.P. and L.P.; funding acquisition, G.P., A.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fondazione di Sardegna, through projects F74I19001030007 and F72F20000360007. This research was also supported by King Abdullah University of Science (KAUST) Baseline funding to [PERSON].  
Not applicable.  
The data presented in this study are included in this published article and in the supplementary material.  
Fondazione di Sardegna and KAUST are gratefully acknowledged for supporting this research. One of the authors ([PERSON]) performed his activity in the framework of the International Ph.D. in Innovation Sciences and Technologies at the University of Cagliari, Italy. [PERSON] carried out part of his activity at the NABLA Lab, KAUST, which is gratefully acknowledged.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Ecotoxicity Study
#### 2.5.2 Conditions for Algae Growth Inhibition Tests  
The algae growth inhibition test was conducted according to OECD TG 201, and the alga (_Pseudokirchneriella subcapitata_) used was sub-cultured once per week under the conditions of \(23\pm 2\)\({}^{\circ}\)C, 24 h-light (6000 lux), and 100 rpm (Vision Scientific Co., Ltd., Daejeon, Korea). For quality control, the growth inhibition test was initially conducted with a standard material, potassium dichromate (K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), CAS No. 7778-50-9, Sigma-Aldrich, St. Louis, MO, USA) with three replicates.  
The control group test was performed using OECD media, with and without NOM. Exposure concentrations were set to 53.9, 70.0, 91.0, 118.4, 153.9, 200, and 260 ug/L (common ratio 1.3) in the absence of NOM and 25, 50, 100, 200, and 400 g/L (common ratio 2.0) in the presence of NOM. The test solution was inoculated with algae at a concentration of \(5\times 10^{3}\) to \(1\times 10^{4}\) cells/mL in a triangular flask with a capacity of 100 mL. During the exposure period, the test was performed in a static condition under which the test solution was not replaced, and the rows and columns of flasks in the shaking incubator were moved sequentially at intervals of 24 h for uniform illumination. Samples were collected from all the flasks of the control and treatment groups after 24, 48, and 72 h exposure periods, the number of algae were counted using a Vi-CELL XR cell counter (Beckman Coulter, Brea, CA, USA), and a microscope was used to calculate the inhibition rate of the average specific growth rate and derive EC\({}_{50}\).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Encoder-Decoder Based Approaches  
#### 3.1.1 Basic Encoder-Decoder Models  
Video captioning aims to generate natural language descriptions of video content, and the basic encoder-decoder framework has been widely adopted for this task. Early methods employed models such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to encode video features and decode them into captions. For instance, [PERSON] et al. [18] introduced the first encoder-decoder model for video captioning. As illustrated in Figure 2, features from the \(fc_{7}\) layer are extracted from individual frames. Here, \(fc_{7}\) refers to the fully connected layer features obtained from each frame  
Figure 1: Techniques for Dense Video Captioning.  
using a pre-trained convolutional neural network (CNN), typically extracted from the \(fc_{7}\) layer of models like VGGNet. These features are then mean-pooled over the entire duration of the video and subsequently fed into an LSTM (Long Short-Term Memory) network at each time step. The LSTM generates a word per time step, considering both video features and the preceding word, until the end-of-sentence tag is chosen. [PERSON] et al. [19] further improved this approach by incorporating temporal attention mechanisms.  
However, these basic models often suffer from limited performance. They struggle to capture complex temporal and spatial dependencies in videos, which leads to generated captions that lack specificity and diversity. This limitation underscores the need for more advanced techniques to enhance the quality and accuracy of video captioning.
## 2 Methods
### Adaptive Regularization Algorithm Based on Maximum a Posteriori Estimation
#### 2.2.2 Improvement of Regularization  
On the basis of the two-dimensional Laplacian operator, a Prewitt operator is added to enhance the edge of the image. This operator is represented by W.  
\[\text{Pr}(x)=\exp\left[-\frac{1}{\lambda_{1}}\|Qx\|^{2}--\frac{1}{\lambda_{2}} \|Wx\|^{2}\right] \tag{16}\]Then the maximization problem is transformed into the minimization problem. The formula is as Formula (17):  
\[\hat{x}_{MAP}=\text{argmin}\Big{[}\|y-Ax\|^{2}+\alpha_{1}\|Qx\|^{2}+\alpha_{2}\| Wx\|^{2}\Big{]} \tag{17}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are regularization parameters.  
The necessary condition for minimizing the above formula is that the partial differential value of \(\|y-Ax\|^{2}+\alpha_{1}\|Qx\|^{2}+\alpha_{2}\|Wx\|^{2}\) with respect to \(x\) is zero, as Formula (18):  
\[2A^{T}(Ax-y)+2\alpha_{1}Q^{T}Qx+2\alpha_{2}W^{T}Wx=0 \tag{18}\]  
In the process of iteration, after regularization parameters are determined, the following formulas are used to update the reconstructed image:  
\[x_{k+1}=x_{k}+\Big{[}A^{T}y-\Big{(}A^{T}A+\alpha_{1(k+1)}Q^{T}Q+\alpha_{2(k+1) }W^{T}W\Big{)}x_{k}\Big{]} \tag{19}\]  
\[\alpha_{2(k+1)}=\text{ln}\Bigg{(}\lambda\frac{\|y-Ax_{k}\|^{2}}{\|Wx_{k}\|^{ 2}+r}+1\Bigg{)} \tag{20}\]  
where \(\alpha_{1(k+1)}\) and \(\alpha_{2(k+1)}\) are the regularization parameter of the \(k\)+1 iteration; \(x_{k}\) is the reconstruction image of the \(k\) iteration; \(r\) is a very small number to ensure that the denominator is not zero; \(\lambda\) is the convergence correction factor, and a fixed constant can be selected.
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 2 Methodology and Data Source
### Recurrent Neural Networks  
Recurrent neural networks (RNNs) are sequential data neural networks whose purpose are to predict the next step in a sequence of observations relative to previous steps in the same sequence. RNNs contain hidden layers distributed across time, which allows them to store information obtained in previous stages of reading serial data. Wind speed depends on the short and long term. The simple RNN model is unable to handle long-term time dependencies. One problem that arises from the unfolding of an RNN is that the gradient of some of the weights starts to become too small or too large if the network is unfolded for too many time steps. This phenomenon is called the vanishing gradients problem, and it can store the short-term memory only because it includes the functions of activating the hidden layer of the previous time step only and this causes the loss of information in the long term [17; 18]. A type of network architecture that solves this problem is the LSTM. In a typical implementation, the hidden layer is replaced by a complex block of computing units composed by gates that trap the error in the block, forming a so-called \"error carrousel\" [5]. Figure 2 shows the RNN structure where the output of the previously hidden layer is input to the current hidden layer. The RNN model is expressed by  
\[\left\{\begin{array}{c}h_{t}=tanh\Big{(}w_{hx}x_{t}+w_{hy}y_{t-1}\Big{)}\\ y_{t}=\sigma(wh_{t})\end{array}\right. \tag{1}\]  
where \(x_{t}\) is the input, \(h_{t}\) is the state value of the hidden layer, \(y_{t}\) is value at the output layer at time \(t\), \(w_{hx}\) is the weight from the input layer, \(w_{hy}\) is the weight for the delayed output at time \(t-1\), tanh is the hyperbolic tangent as the activation function at the hidden layer, and \(\sigma\) is the sigmoid function as the activation function at the output layer.