esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
## 2 Study Area and Methods
### Methods
#### 2.2.2 Decoder with Temporal Attention  
As the length of the input sequence increases, the performance of the encoder-decoder framework may deteriorate rapidly [32]. Therefore, we introduce a time attention module to adaptively select the relevant encoder hidden states. For decoder unit \(i(1\leq i\leq\tau)\), given the hidden state sequence \(\{\mathbf{h}_{t}\}_{t=1}^{T}\) and the previous decoder's hidden state \(\mathbf{d}_{i-1}\), temporal attention leverages a scoring function \(\xi\) to return an importance score for input item \(\mathbf{h}_{t}\), and converts this score into attention weights using the softmax function. The calculation process can be described by Formula (5):  
\[\begin{array}{l}e_{t}=\xi(\mathbf{h}_{t},\mathbf{d}_{i-1})=\mathbf{h}_{t} \mathbf{W}(\mathbf{d}_{i-1})^{\mathrm{T}}\\ \lambda_{t}=\frac{\exp(e_{t})}{\sum\limits_{k=1}^{T}\exp(e_{k})}\end{array} \tag{5}\]  
where \(\mathbf{W}\in\mathbb{R}^{m\times m}\) is a learnable parameter used to evaluate the similarity between \(\mathbf{h}_{t}\) and \(\mathbf{d}_{i-1}\). \(\lambda_{t}\) is the attention weight converted from score \(e_{t}\). \(\left(\cdot\right)^{T}\) represents the matrix transpose operation. The output of temporal attention is the weighted sum of all encoder hidden states, and the weights used are attention weights. Specifically,  
\[\mathbf{h}\mathbf{c}_{i}=\sum\limits_{t=1}^{T}\lambda_{t}\odot\mathbf{h}_{t} \tag{6}\]  
where \(\mathbf{h}\mathbf{c}_{i}\in\mathbb{R}^{m}\) is the output of temporal attention, \(\odot\) is element-wise multiplication. Subsequently, decoder unit i updates its hidden state \(\mathbf{d}_{i}\) using feature \(\mathbf{h}\mathbf{c}_{i}\) and the previous predicted value \(\hat{y}_{i-1}\). Finally, the MaRNN model converts the hidden state \(\mathbf{d}_{i}\) into a predicted value \(\hat{y}_{i}\) through a feedforward network. The above learning process can be defined using Formula (7):  
\[\begin{array}{l}\mathbf{d}_{i}=\mathrm{LSTM}([\mathbf{h}\mathbf{c}_{i}: \hat{y}_{i-1}],\mathbf{d}_{i-1})\\ \hat{y}_{i}=\mathbf{W}\mathbf{d}_{i}\end{array} \tag{7}\]  
where \(\mathbf{W}\in\mathbb{R}^{m}\) are trainable parameters.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 3 The Iterative Adaptation VMD Method  
In fact, the VMD method extends the classical Wiener filter to multiple adaptive wavebands. Compared with EMD and local mean decomposition (LMD), the VMD method has the advantages of fast convergence, high robustness, and better performance in antinoise regulation and nonstationary signal processing.  
As shown in Figure 2, the bandpass filter (BPF) method preliminarily decomposes the original input signal according to the frequency range of the LFO interval mode, the LFO local mode, subsynchronous oscillation (SSO), and supersynchronous oscillation (SurSO). The intrinsic mode function of each frequency band signal can be obtained by the IA-VMD method. Finally, the frequency, damping ratio, and oscillation amplitude of each IMF can be extracted by the Prony algorithm.  
Figure 2: Framework of iterative adaptive variational mode decomposition (IA-VMD)-based broadband oscillation identifications.
## 4 Results
### Second Case Study: Chedi Structure  
The results for the Chedi structure can be seen in Table 4. The RMSE horizontal for TLS was 0.069 m, and for UAV was 0.069 m. The RMSE verticals were 0.022 and 0.021 m for TLS and UAV, respectively.  
As shown in Figures 8 and 9, to evaluate the quality of positional accuracy on the pagoda facade, in this study, the selected checkpoints could be clearly seen in a field survey, such as natural features,  
\begin{table}
\begin{tabular}{l c c} \hline  & **Terrestrial** & **UAV** \\ \hline RMSE H (m) & 0.069 & 0.069 \\ RMSE V (m) & 0.022 & 0.021 \\ \hline \end{tabular}
\end{table}
Table 4: RMSE of Chedi, computed on 20 checkpoints for the terrestrial and UAV point cloud.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Terrestrial** & **UAV** \\ \hline RMSE H (m) & 0.068 & 0.066 \\ RMSE V (m) & 0.030 & 0.054 \\ \hline \end{tabular}
\end{table}
Table 3: RMSE of Prang, computed on 20 checkpoints for the terrestrial and UAV point clouds.  
Figure 9: Comparison of the terrestrial and UAV point clouds, computed on 20 checkpoints for Chedi structure. (**a**) Point clouds from TLS. (**b**) Point clouds from UAV.  
the corner of the pagoda, and brick, and also clearly seen in UAV images and TLS data. The red line is a 2D representation obtained from the Fine Arts Department of Thailand's footprint to help assist in determining the checkpoint positions.  
The reason why checkpoints are not selected at the top of the pagoda is that the edge or corner of the pagoda in the UAV and TLS point clouds cannot be clearly seen, as shown in Figures 10 and 11.
## 2 Materials and Methods  
### Sample Fabrication  
Silicon nitride films SiN\({}_{\mathrm{x}}\) with different \(x\) values were deposited by low frequency (55 kHz) discharge plasma enhanced chemical vapor deposition (LF PECVD). Monosilane SiH\({}_{\mathrm{4}}\) and ammonia NH\({}_{\mathrm{3}}\) were used as components of gas mixture. Gas flow ratio \(k\) = [NH\({}_{\mathrm{3}}\)]/[SiH\({}_{\mathrm{4}}\)] defined the stoichiometry coefficient \(x\) in deposited SiN\({}_{\mathrm{x}}\) materials. Gas mixture with \(k\) = 5 and 1.2 were used for deposition of nearly stoichiometric Si\({}_{3}\)N\({}_{\mathrm{4}}\) barriers and silicon rich SiN\({}_{\mathrm{x}}\) layers (\(x\)\(\approx\) 0.85), respectively [19]. In total, 18 pairs of layers were deposited on substrates at a power density of 0.2 W/cm\({}^{2}\). Pressure of gas mixture in plasma-chemical reactor was 250 Pa, and the deposition temperature was 380 \({}^{\circ}\)C. For the fabricated samples the thicknesses of SiN\({}_{\mathrm{x}}\)/Si\({}_{3}\)N\({}_{\mathrm{4}}\) bilayers are equal to 2/2.25 \(\pm\) 0.2 nm (SN1) and 1.6/1.75 \(\pm\) 0.2 nm (SN2), as measured by means of transmission electron microscopy (TEM). Prepared samples were subsequently annealed in a tube furnace under nitrogen atmosphere during 1 h at the following temperatures (T\({}_{\mathrm{ann}}\)): 900, 1150 and 1250 \({}^{\circ}\)C, in order to form Si nanocrystals during phase separation of silicon-rich SiN\({}_{\mathrm{x}}\) layers:  
\[y\ \mathrm{SiN}_{\mathrm{x}}=(y-x)\ \mathrm{Si}+x\ \mathrm{SiN}_{\mathrm{y}} \tag{1}\]  
where \(y\) is supposed to be close to stoichiometric 1.33 value [20].
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments
### Sensitivity Analysis  
Because the type of dynamic demand will directly affect the change in delivery cost, this section only considers the relationship between newly added customers and delivery cost. We change all the dynamic demands of the actual cases to newly added customers and set the dynamic degrees to 0, 0.2, 0.4, 0.6, 0.8, and 1.0, in turn, and run the algorithm 10 times under each gradient to get the smallest objective function value. The changes in delivery costs with the dynamic degree are shown in Figure 12. The results indicate that, under the condition that the total number of customers remains constant, the delivery cost will increase with the increase in the dynamic degree. This is due to the fact that when the number of dynamic demand customers increases, the number of driving routes will increase and the company will need to arrange additional vehicles to provide delivery services for the additional customers.  
It is important to note that there are several limitations in our case study that need to be highlighted to ensure a clearer understanding of the applicability and limitations of the study. First, the experimental results of the actual case study may be node-number-limited. The number of nodes in this instance is 30, which may be relatively small. This means that further experiments using larger groups are still needed. Second, to simplify the research, we have not considered traffic congestion. Future research should consider traffic  
Figure 11: Optimized delivery route.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Latitude** & **Longitude** & **Demand** & **Time Window** & **Customer Type** \\ \hline
19 & 41.69756 & 123.4184 & 2.6 & 9:00–10:00 & a \\
20 & 41.75746 & 123.5306 & 23.5 & 9:00–10:00 & a \\
21 & 41.83218 & 123.432 & 1.6 & 10:00–11:00 & b \\
22 & 41.79236 & 123.4909 & 21.3 & 10:00–11:00 & b \\
23 & 41.77769 & 123.3386 & 2.5 & 9:00–10:00 & b \\
24 & 41.77384 & 123.3875 & 1.6 & 9:00–10:00 & b \\
25 & 41.80065 & 123.3116 & 2.2 & 9:00–10:00 & b \\
26 & 41.89783 & 123.4372 & 2.6 & 9:00–10:00 & b \\
27 & 41.81732 & 123.4693 & 35 & 10:00–11:00 & b \\
28 & 41.83438 & 123.3824 & 17.5 & 9:00–10:00 & b \\
29 & 41.89038 & 123.4706 & 1.5 & 9:00–10:00 & b \\
30 & 41.80003 & 123.4408 & 19.5 & 10:00–11:00 & b \\ \hline \hline \end{tabular}
\end{table}
Table 8: Information for dynamic customers.  
congestion factors to gain a more comprehensive understanding of the actual situation of the cases.
###### Abstract  
Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and \"black-box\" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.  
n  
nature based optimization; artificial bee colony; firefly algorithm; cuckoo search; bat algorithm; flower pollination algorithm; grey wolf optimizer
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 2 Structural Calculation Model for Anti-Sliding Short Piles
### Basic Assumptions  
The model's derivation process is grounded in the following assumptions: (1) The deformation of the anti-sliding pile conforms to the Euler-Bernoulli beam theory and fulfills the assumption of a planar cross-section. (2) A uniformly distributed load \(q(z)\) is applied along the entire length of the loaded section, and the pile material is assumed to be homogeneous and linearly elastic. (3) The model does not incorporate axial pressure from the soil above the loaded section.
## 4 Experimental Results
### Application of EA-ILS Hyper-Heuristic to HyFlex Version 1.0
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Hyper-Heuristic**} & \multicolumn{8}{c}{**Problem Domains**} \\ \cline{2-7}  & **SAT** & **BP** & **PS** & **PSF** & **TSP** & **VRP** & **Overall** \\ \hline TS-ILS & **0.1341** & **0.1052** & 0.3735 & 0.4515 & 0.2852 & **0.3514** & **0.2835** \\ EA-ILS & 0.3677 & 0.1779 & **0.3418** & **0.3964** & 0.4435 & 0.3982 & 0.3542 \\ FS-ILS & 0.1365 & 0.5547 & 0.5505 & 0.5601 & **0.2692** & 0.3653 & 0.4061 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Benchmarking EA-ILS with FS-ILS and TSILS on the six domains of HyFlex v1.0 using the \(\mu\) -norm metric. Figure 5: F1 plots of EA-ILS with other top hyper-heuristics on the HyFlex domains. perturbative heuristics, especially on the SAT domain, that has made it inferior to the two state-of-the-art hyper-heuristics. However, the EA-ILS hyper-heuristic outperformed both the FS-ILS and TS-ILS hyper-heuristics in the PS and PFS problem domains. This is highly commendable because of the strong performances of both competitors in the PFS domain and the strong performance of TS-ILS in the PS domain. The EA-ILS hyper-heuristic was not far from the best method for BP and VRP problems. Overall, it had a better \(\mu\)-norm score than the FS-ILS hyper-heuristic, suggesting its better generalization ability across the original six problem domains.
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 3 Background Study: Building Energy Consumption and the Climatic Context in Shanghai
### Building Energy Consumption in Shanghai and Beyond
The rise in energy consumption can be attributed to the significant net increase in the population of Shanghai due to migration. After 2000, the net migration influx in Shanghai witnessed an eight-fold increase compared to that of the 1990 baseline [36]. However, from 2011, net migration reduced to about 50% of the 2010 baseline and has remained so till date. The variation in net migration could explain the reason for the exponential growth in energy consumption before 2010 and the slow energy consumption rate beyond 2013. For further elucidation, Figure 6b presents the relationship between energy consumption and net population increase resulting from migration from 2000 to 2016. Figure 6b shows that a 99.1% correlation exist between these factors. Further insight into the statistical relationship is shown in a supporting document (Section S1). The statistical analysis confirms that the worsening effect of climate change was not the only factor contributing to the increase in building energy consumption in Shanghai. It is demonstrated that the rise in energy consumption is highly correlated to the increase in population density. Therefore, the appropriate sustainable built environment should account for climate variations, as well as for an increase in population density. From a broader perspective, building energy consumption is affected by a complicated network of factors [51]. Figure 7 conveys the complexity of the relationship by categorizing the variables into five sections: (a) Causes of climate change, (b) indicators of climate change and its (c) environmental impact, effects on (d) buildings and (e) its occupants, with a feedback loop connecting potential activities of occupants that can increase the causative potentials of climate change. In summary, it can be deduced that the increasing energy consumption in Shanghai is associated with a network of factors, including the effect of climate change and population rise. Therefore, the most suitable building retrofit package for Shanghai would be measures that can mitigate the combinative effect of the major contributing factors to the rise in building energy consumption. Figure 6: (**a**) The annual energy consumption in Shanghai from 1970 to 2016, (**b**) Relationship between annual energy consumption and net population increase from migration using the 2000 baseline. Data extracted from China Statistical Yearbook 2017 [49]. Figure 7: Interactions between buildings, climate change and subsequent potential effects on building occupants and the environment. The figure is based on a schematic overview by [PERSON] and [PERSON] [51]), but expanded to elaborate on the cyclic interactive nature.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 3 Algorithm Framework
### Distance Measure  
The distance measure, which is also known as the cluster criterion or intercluster distance, is the core part of both the SLIC and WCFC algorithms. Superpixels correspond to clusters in the color image plane space. This presents a problem when defining the distance measure (\(D\)), which may not be immediately obvious. The distance measure \(D\) computes the distance between a pixel (\(i\)) and a cluster center (\(C_{k}\)) in Algorithm 1, defined as follows:  
\[D=\sqrt{\left(\frac{d_{c}}{N_{c}}\right)^{2}+\left(\frac{d_{s}}{N_{s}}\right) ^{2}+\left(\frac{d_{g}}{N_{g}}\right)^{2}}, \tag{3}\]  
where \(d_{c}\) is the color distance measured as the Euclidean distance in CIELAB color space, and \(d_{s}\) is the spatial distance, which is the same as the SLIC algorithm. Meanwhile, \(N_{c}\), \(N_{s}\) and \(N_{g}\) are the weighted factors which combine the three distances into a single measure. Additionally, \(d_{g}\) is the geometric distance related to the depth and color data, which will be discussed in the next section.  
By defining \(D\) in this manner, \(N_{c}\), \(N_{s}\) and \(N_{g}\) also allow us to weigh the relative importance among color similarity, spatial proximity and geometric resemblance. In this case, \(N_{c}\) can be fixed to a constant like the SLIC algorithm, that is, \(N_{c}=1\). Therefore, when \(N_{s}\) is small, spatial proximity is more important, and the resulting superpixels are more compact. When \(N_{s}\) is large, the resulting superpixels adhere more tightly to image boundaries but have less regular sizes and shapes. Likewise, a small \(N_{g}\) makes geometricresemblance more important and produces superpixels that tightly adhere to the depth borders. On the contrary, a large \(N_{g}\) provides superpixels that are more like RGB results.
## 3 Observation of Topological Defects on Different Timescales
### Topological Defect Dynamics Observed by X-rays and Low-Energy Electron Diffraction  
In principle, diffraction experiments can give information on the detailed structure only if the phase can be recorded. Although this is not yet possible with current state-of-the-art techniques, the time-evolution of the diffraction intensity is more informative than the optical experiments discussed in the previous section, particularly when combined with modeling. [PERSON] et al. have shown the growth of domains in the nearly commensurate phase of 1T-TaS\({}_{2}\) to follow Lifshitz-Allen-Kahn dynamics on a timescale of \(\sim\)100 ps [18]. The observed CDW correlation length in the incommensurate state was shown to be described well by the expected power law \(\xi_{C}\propto t^{1/2}\). [PERSON] et al. [19] also observed a marked anisotropy the size of the photoinduced domains of the I-phase of 1T-TaS\({}_{2}\), which appeared to grow self-similarly, such that their shape remained unchanged throughout the growth process.  
Figure 3: Incoherent relaxation of domains in photoexcitation experiments. The time-dependence of the excess linewidth \(\gamma_{AM}-\gamma_{AM}^{TDM}\) due to the annihilation of topological defects. Here, \(\gamma_{AM}\) is the full linewidth, and \(\gamma_{AM}^{TDM}\) is the thermal linewidth. Data for two different samples show a difference primarily in the long-time behavior beyond 30 ps, which appears as an offset. The insert depicts the laser photoexcitation spot where the phase of the order parameter is represented schematically by arrows.  
Recent ultrafast low-energy electron diffraction (ULEED) experiments [20] show coarsening that follows a power-law scaling of the correlation length, driven by the annihilation of dislocation-type topological defects of the charge-ordered lattice. The technique, in combination with modelling on the level of time-dependent Ginzburg-Landau theory, allows detailed interpretation of the observed electron diffraction patterns, and in particular, interpretation of the dynamics of the domain formation. [PERSON] et al. [22] independently tracked the amplitude and phase dynamics of the CDW by a combination of techniques, finding \(\sim\)1 ps recovery of the CDW amplitude, followed by a slower re-establishment of phase coherence dictated, which they attributed to the presence of topological defects. The timescales of these experiments on the NC-IC transition in 1T-TaS\({}_{2}\) are in good agreement with optical experiments on the IC transition in TbTe\({}_{3}\) discussed in the previous section.  
As much as these techniques agree on the dynamics, they are all limited by spatial and temporal stroboscopic averaging. Features in the structure that are non-periodic, such as dislocations, or that appear on multiple length scales, are not discernible in the diffraction experiments.
# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 6 Conclusions  
In conclusion, the primary objective of this paper was to develop a comprehensive mathematical model for power and energy consumption in electric vehicles (EVs), alongside creating a dependable algorithm using the MATLAB Simulink model to ascertain the state of charge (SOC%) of the battery for any vehicle. Additionally, the aim was to conduct comparisons between different routes to determine the most economical or optimal path for EVs with minimum energy consumption. These models incorporate various factors, including those intrinsic to the vehicle model such as aerodynamics and drag coefficient, as well as external variables like wind speed and direction, and road inclination. In addition to simulation, real-world experiments were conducted, and validated the accuracy of data derived from the model. All these factors will help in saving money if taken into consideration and the lifetime of the wheels and battery will increase when choosing the optimum route for the EV.  
Conceptualization, M.A., H.Y.D. and M.M.H.; methodology, M.A. and K.A.; software, K.A. and M.M.H.; validation, K.A., M.A. and H.Y.D.; formal analysis, K.A., M.A. and M.M.H.; investigation, K.A. and M.M.H.; resources, M.A., H.Y.D. and M.M.H.; writing--original draft preparation, K.A., M.A., H.Y.D. and M.M.H.; writing--review and editing, M.A. and H.Y.D., supervision, M.A., H.Y.D. and M.M.H. All authors have read and agreed to the published version of the manuscript.  
This research didn't receive any external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.