esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FT-IR Analysis of Biosynthesized CH-AgNPs  
T-IR analysis of AgNPs and CH-AgNPs was performed in order to confirm the reduction of the molecular interaction and capping agent which is responsible for synthesizing and stabilizing the nanoparticles. This approach is generally used for qualitative analysis of nanostructures. FT-IR spectra of AgNPs shows peaks at 3280, 2856, 1527, 1295, 1017 and 823 cm\({}^{-1}\) and a similar pattern for CH-AgNPs detected with peaks at 3178, 1653, 1509, 1381, 1278, and 1042 cm\({}^{-1}\), respectively (Figure 4b,c). These peaks represent functional groups at different wavenumbers. The absorbance peak at 3280 cm\({}^{-1}\) can be attributed to the vibrations of amino groups (N-H), which may be the presence of peptides, whereas the peak at 2856 cm\({}^{-1}\) may be because of sym -CH\({}_{3}\) stretching vibrations. However, the peak at 1527 cm\({}^{-1}\) can be assigned to C=N stretching vibration, and the peak at 1295 cm\({}^{-1}\) may be because of C-H deformation vibration. Similarly, the peak 1017 cm\({}^{-1}\) can be assigned to C-O stretching vibration and peak at 823 cm\({}^{-1}\) may be because of C-C skeletal vibration (Table 3). In case of CH-AgNPs, the peak at 3178 cm\({}^{-1}\) is associated with amide group N-H stretching. The vibration peak shown at 1653 cm\({}^{-1}\) can be assigned to C=C stretching vibrations. Moreover, a peak of 1509 cm\({}^{-1}\) may be due to a broad amide II band. The peaks 1381, 1278, and 1042 cm\({}^{-1}\) are assigned to C-H deformation vibration sym, C-H sym deformation vibration, and C-C skeleton vibration, respectively (Table 4).  
Figure 3: Zeta potential analysis of (**A**) AgNPs (\(-\)7.20 mV) and (**B**) CH-AgNPs (9 mV).
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Thermal-Fluid Finite Volume Model of the TPMLG  
The temperature field analysis is quite complex as the multi-physics field requires comprehensive consideration. To lighten the burden of large computation, the solved domain is minimized, and some factors which are trivial for the temperature field are neglected. Hence, simplifying assumptions are proposed as below:  
1. Considering high thermal conductivity and the symmetric structure, it is assumed that the temperatures of the windings and permanent magnets are uniformly distributed in the circumferential direction when the electromagnetic properties of the materials are modified with temperature.
2. Some characteristics which are difficult to model are ignored, such as chamfers, fillets, through-holes, and threaded holes.
3. Mechanical loss and radiative heat transfer are neglected.  
Based on the assumptions, a 3-D thermal-fluid finite volume model of the TPMLG enclosed with ambient air was established by Fluent 2022 R1 software in this study. The model is shown in Figure 4. The stator core, the windings, a casing, slot wedges, PMs, silicon steel sheets, the stainless-steel sleeve, and an air enclosure outside the TPMLG are included in the model.  
Mesh quality is extremely important for the precision and convergence of FVM analysis results. The 3-D TPMLG simplified physical model is meshed with hexahedral elements, encompassing a total of 12,424,469 nodes and 2,856,453 elements. The meshes of small size regions, narrow flow areas and near wall boundary layers are required to be refined  
Figure 4: 3-D simplified physical model of the TPMLG.  
appropriately, as shown in Figure 5. Five air inflation layers are set on the surfaces of the LG as the boundary layers, using the smooth-transition method.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 6 Simulation Result Analysis  
### Analysis of Slurry Diffusion Morphology  
Taking a grouting pressure of 1 MPa as an example, this study analyzes the dynamic grouting process of polymer in a rockfill from a vertical profile perspective (Figure 11). The diffusion of polymer foam within the rockfill is categorized into three stages:  
1. At 1 s, the slurry predominantly gathers near the grouting hole, forming a nascent \"U\" shape. Initially, under the influence of grouting pressure, the slurry fills the grouting hole and adjacent voids. At this stage, diffusion is restricted, primarily influenced by the grouting hole diameter and pressure.
2. Between 2 and 6 s, as grouting progresses, the slurry diffusion range rapidly expands, creating a pronounced \"U\" shape. This expansion suggests an acceleration in the slurry's diffusion rate within the rockfill body. In the X direction, diffusion is notably  
Figure 10: Comparison of slurry diffusion morphology.  
more extensive near the surface, likely due to the looser structure and higher porosity at the surface, which facilitate easier slurry movement.
3. From 7 to 10 s, in the later stages of grouting, the rate of slurry diffusion gradually decreases and eventually stabilizes. This deceleration indicates that the available voids within the rockfill are becoming filled, limiting further diffusion. Nonetheless, the inherent permeability and diffusivity of polymer foam allow it to maintain a consistent diffusion rate even as the process nears completion.  
In conclusion, the diffusion of polymer foam in rockfill exemplifies a typical infiltration grouting process. The slurry initially forms a small \"U\" shape at the grouting hole, then rapidly expands to create a large, opening \"U\" shape, and maintains a stable diffusion rate in later stages. This behavior aligns with infiltration grouting theory and enhances our understanding of polymer foam diffusion dynamics within rockfill.
## 3 The LBP-FFT-CNNs Model
### CNNs with a Self-Attention Mechanism  
To allow CNNs to dynamically pay attention to information at different locations when processing input data, we introduce a self-attention mechanism [21] in hidden layers. In the self-attention mechanism, we first multiply input tensor \(X\) by three weight matrices \(W_{Q}\), \(W_{k}\), and \(W_{V}\) to obtain _Query_, _Key_, and \(Value\) tensors. These tensors are used to calculate attention scores and weighted values as shown in Equation (12):  
\[Q=XW_{Q},K=XW_{k},V=XW_{V} \tag{12}\]  
where \(Q\), \(K\), and \(V\) represent the _Query_ information, _Key_ information, and \(Value\) information of the current location. The \(Attention\_score\) tensor represents the correlation between _Query_ and _Key_, which determines the importance of each position when calculating weighted value. The calculation method is to divide the inner product of \(Q\) and \(K\) using a scaling factor \(\sqrt{d_{k}}\) and then normalize it using the SoftMax function shown in Equation (13).  
\[Attention\_score=\frac{QK^{T}}{\sqrt{d_{k}}} \tag{13}\]  
\[Attention(Q,K)=softmax(Attention\_score) \tag{14}\]  
Here, \(d_{k}\) denotes the dimensionality of the key vectors. The scaling factor \(1/\sqrt{d_{k}}\) is used to prevent large dot product values, which can cause gradients to become too small during the SoftMax operation. This normalization improves train stability and convergence. Once attention scores are computed, they are used to weight the value vectors \(V\). The weighted sum of these values produces the output of self-attention layer.  
\[SelfAttention\_weight(Q,K,V)=Attention(Q,K)V \tag{15}\]  
\[output\_layer=SelfAttention\_weight(Q,K,V)W_{o} \tag{16}\]  
Finally, the obtained weighted value \(SelfAttention\_weight(Q,K,V)\) is multiplied by another weight matrix to obtain the final output layer. The integration of LBP with a self-attention mechanism leverages the local texture encoding capability of LBP and global contextual analysis strength of self-attention, resulting in a more comprehensive feature representation. The self-attention mechanism compensates for LBP's limitations in distinguishing complex scenes or similar textures by focusing on critical regions. This combination enhances model's robustness, computational efficiency, and generalization ability, leading to superior performance in texture analysis tasks.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 2 Methodology
### LSTM and Hyperparameter Selection  
#### 2.2.1 Lstm  
As a special kind of recurrent neural network (RNN), the LSTM neural network was first proposed by [PERSON] and [PERSON] [38]. According to the LSTM structure in Figure 1, the current state of a cell will be affected by the previous cell state, which reflects the recurrent characteristics of LSTM. Based on RNN, the candidate cell, forget gate, input gate, and output gate are added to the hidden layer of LSTM. LSTM with such a structure does not cause the gradient to disappear or explode and can learn the information contained in time series data more effectively [39]. An LSTM unit composed of a candidate cell, an input gate, an output gate, and a forget gate is shown in Figure 2. The input gate controls the extent of the values which flow into the cell. The forget gate controls the extent of the values which remain in the cell. The output gate and the value in the cell determine the output of an LSTM unit [39].  
Figure 1: LSTM network.  
The calculation process of LSTM in Figure 2 is as follows:  
1. Calculate the inputs for three gate units and the candidate cell by Equations (7)-(10): \[net_{i,t} =W_{i}\cdot[x_{t},h_{t-1}]+b_{i}\] (7) \[net_{f,t} =W_{f}\cdot[x_{t},h_{t-1}]+b_{f}\] (8) \[net_{o,t} =W_{o}\cdot[x_{t},h_{t-1}]+b_{o}\] (9) \[net_{\tilde{c},t} =W_{\tilde{c}}\cdot[x_{t},h_{t-1}]+b_{\tilde{c}}\] (10) where \(x_{t}\) represents the inputs at time \(t\), and \(h_{t-1}\) represents the cell output at time \(t-1\). \(net_{i,t}net_{f,t}net_{o,t}net_{\tilde{c},t}\) are input gate, forget gate, output gate, and the candidate cell, respectively. \(W_{i},W_{f},W_{o},W_{\overline{c}}\) are the input gate, forget gate, output gate, and weight matrix of the candidate cell, respectively. \(b_{i},b_{f},b_{o},b_{\overline{c}}\) are the bias of input gate, forget gate, output gate, and candidate cell, respectively.
2. Calculate the three gate units by Equations (11)-(15): \[i_{t} =\sigma(net_{i,t})\] (11) \[f_{t} =\sigma(net_{f,t})\] (12) \[\tilde{C}_{t} =\tan\mathrm{h}(net_{\tilde{c},t})\] (13) \[o_{t} =\sigma(net_{o,t})\] (14) \[C_{t} =f_{t}\cdot C_{t-1}+i_{t}\cdot\tilde{C}_{t}\] (15) where \(i_{t}\), \(f_{t}\), \(C_{t}\), \(o_{t}\), \(\tilde{c}_{t}\) represent the input gate at time \(t\), forget gate, unit state, output gate, and candidate cell vector unit output, respectively. \(\sigma(\cdot)\) stands for sigmoid activation function and is expressed by Equation (16): \[\sigma(x)=\frac{1}{1+e^{-x}}\] (16) \[tanh(\cdot)\] stands for tanh activation function and is expressed by Equation (17): \[\tan\left(x\right)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\] (17)  
Figure 2: LSTM unit.  
Calculate the output by Equation (18): \[h_{t}=o_{t}\cdot\tan\mathrm{h}(C_{t})\] (18) where \(h_{t}\) is the unit output at time \(t\).
## 4 Applicability of Concept
### Production of a 3D-Printed RC Wall Prototype  
To demonstrate the technical feasibility of the AMoRC process, a reinforced concrete wall panel has been printed in the lab (Figure 4a). As the technical implementation of a combined print head for concrete extrusion and joining of reinforcement has not been completed, so far, both processes were performed successively with the welding process preceding the concrete placement.  
A fork-shaped print head with two nozzles was used for concrete extrusion (Figure 4b). Both nozzles had a rectangular opening cross section with a width of 45 mm, producing filament thicknesses of 10 mm at a printing speed of 1.5 m/min. The distance between the nozzles was 1.5 times the reinforcement diameter. The total wall thickness was 100 mm with a reinforcement layer in the center of the wall cross section. The chosen concrete class was C70/85 and the concrete mix (550 kg/m\({}^{3}\) CEM I 52.5 R; 280 kg/m\({}^{3}\) water; 250 kg/m\({}^{3}\) fly ash; 1172 kg/m\({}^{3}\) aggregate with a maximum grain size of 4 mm), with a water-binder ratio of 0.43 deliberately chosen simply for the reproducibility of the  
Figure 3: 3D printing of a non-reinforced demonstration objects at Institute of Structural Concrete of RWTH Aachen University (**a**), preliminary work for joining of reinforcement and additive manufacturing of steel at Institute for Joining and welding technology of RWTH Aachen University: MAG welding (**b**), stud welding (**c**), pin welding (**d**).  
presented investigations. In the H\(\ddot{\text{a}}\)germann flow table test, the concrete showed a spread flow diameter of 10.2 cm without shocks and 15.5 cm after 15 shocks immediately after mixing. The setting time of the concrete was 135 min determined with a Vicat apparatus. The time dependent fresh concrete properties are given in Figure 5. Highly ductile reinforcing steel B500 B with a diameter of 8 mm was used as reinforcement of the prototype. During the production of the demonstrator, only the concrete print head was automatically guided by a CNC machine, while the welding process was carried out manually. In the future, this will either be done by a robot with stud welding gun operating independently of the concrete print head or by a new type of print head combining welding and concrete extrusion process (see Figure 2).
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 3 System Design and Implementation
The implementation of the interfaces consumed from these domain orchestrators will depend on the technology used to build them and the service level agreements established among the different providers. The IoT slice orchestrator shall handle the following entities, making use of interfaces provided by these other orchestrators:  
* The IoT gateway is deployed and configured using an edge or a cloud computing orchestrator, depending on where the gateway is finally deployed. * The network slice at the access network, configuring the right Radio Access Technology (RAT) and reserving the required resources (e.g., radio channels). * The network slice at the transport network, for instance using an SDN controller that defines the corresponding Virtual Private Network (VPN). * The network slice in the core network, deploying or configuring the required Virtual Network Functions (VNFs) to provide this service (e.g., a specific User Plane Function (UPF) or Packet Data Network (PDN) gateway as defined by 3 GPP [53] standardization body). * The IoT server, analytics applications or other functions that are deployed in the cloud, using the corresponding cloud orchestrator. Figure 2: Overall IoT solution orchestration. We want to observe that the development of IoT slice orchestrator is an ambitious work that requires the utilization of diverse 5G-related technologies across multiple domains. For this reason, this paper focuses on the multi-tenancy aspects at the edge and cloud computing domains, which are fundamental in IoT solutions to support the collection, evaluation, storage, and processing of IoT data. Consequently, the implementation of our design, as it is described in later sections, has focused on the IoT gateway and the IoT functions that can be deployed at the edge or at the cloud (i.e., first and last points in the list above). Even the support of network slicing is considered in the design of the orchestrator, as the implementation of the network related part is left for future work.
# Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems
## 4 Physical Experiments  
The system has six degrees of freedom, two of which were varied simultaneously. The initial conditions that remained fixed in each simulation of the dynamical system were: \(\phi_{0}=\pi/2\) and \(\dot{x}_{0}=\dot{\theta}_{0}=\dot{\phi}_{0}=0.0\). The initial conditions that were simultaneously swept were: \(|x_{0}|\leq 0.01\)and \(|\theta|\leq\pi/2\), with \(dx=0.0001\) and \(d\theta=\pi/180\), giving a total of 200 iterations in \(x\) and 180 iterations in \(\theta\). It ought to be noted that the scanning regions are imposed by the physical limitations of the model. In this way, the sweeping of both initial conditions constitutes a total of \(36,000\) Runge-Kutta instances to execute, each one simulated for 600 s (10 min) with \(\Delta t=0.0001\) (\(6\times 10^{7}\) iterations).  
For this experiment, the parameters of the model were initialised as follows: \(A=0.005555\) m, \(k=5.15\) N/m, \(\omega=2\pi\) Hz, \(g=9.78\) kg s\({}^{-2}\) (this value of gravity's acceleration is valid in Mexico City, where the experiments were performed), \(m_{g}=0.0686\) kg, \(m_{\theta}=m_{\phi}=0.0049\) kg, \(l_{\theta}=l_{\phi}=0.22\) m and \(\gamma=0.029\) kg s\({}^{-1}\).  
The system does exhibit both chaotic and stable regions for the time span herein studied. There are three characteristic times in this system: \(t_{\zeta_{x}}=2\pi/\omega=1\) s and \(t_{\zeta_{\theta}}=t_{\zeta_{\phi}}\approx 1.11232\) s (when oscillating pendula are initially dropped from \(\pi/2\)). As an illustrative issue, we show phase planes for \(x\), for chaotic and a non-chaotic cases, in Figure 1. The initial conditions for the chaotic trajectory are given by: \((x,\theta,\phi,\dot{x},\dot{\theta},\dot{\phi})=(-0.007,-1.3439,\pi/2,0,0,0)\). Meanwhile, for the non-chaotic trajectory the initial conditions were: \((x,\theta,\phi,\dot{x},\dot{\theta},\dot{\phi})=(0,\pi/2,\pi/2,0,0,0)\).  
In Figure 2, Lyapunov exponents for varying \(x\) and fixed \(\theta\) are shown. The chaotic phase plane trajectory shown in Figure 1a has a Lyapunov exponent, \(\lambda_{x}=1.584224\), that corresponds to that of Figure 2a with \(x=-0.007\). On the other hand, the non-chaotic phase plane trajectory shown in Figure 1b has a Lyapunov exponent, \(\lambda_{x}=-4.214377\), as shown in Figure 2b with \(x=0\)  
Figure 1: Phase plane trajectories of \(x\) showing long-term chaotic (**a**) and non-chaotic (**b**) behaviour. (**a**) corresponds to \(\lambda_{x}=1.584224\) as shown in Figure 2a, while (**b**) corresponds to \(\lambda_{x}=-4.214377\) as shown in Figure 2b.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 3 Research Method
### Database Anthology  
Table 1 presents the system tables and relationships in a simplified version, which is in response to the system requirements.  
The operationalization of the system comprises several phases of implementation. The first phase concerns the definition of strategic sites for establishing Gateways, which will serve the populations and inhabitants of isolated agricultural regions and are essential to make the system effective. The next phase involves installing a pilot project consisting of a functional prototype, residential or personal, which allows assessing the quality of the service and the usability characteristics of the system. Finally, it is a fundamental step to invest in a solution that can increase the quality of life of the elderly population in isolated regions, where they are deprived of communication services or where mobile network coverage is deficient.  
The following phases include in the pilot project the various entities that constitute interest groups, namely municipalities and companies with installed technological capacity (e.g., wind farms). In addition, it could create synergies with isolated populations and other local entities, such as fire brigades and security forces.
## 3 Dynamic Network Layout Algorithm
### Node Mobility Factor and Layout  
Finally, we define mobility factors for nodes by combining the influence and age factors. These mobility factors are used to adjust the forces acting on the nodes, ultimately yielding the final layout result.  
The node's mobility in the subsequent layout is together determined by the node influence factor and node age. The larger node influence factor encourages nodes to move to achieve a better layout, while the larger node age stabilizes nodes in the network that haven't experienced updates for a long time. Consequently, a node's mobility in the network can be defined as Formula (7).  
\[M_{i}(t)=\alpha\times I_{i}(t)+(1-\alpha)\times e^{-\beta a_{i}(t)} \tag{7}\]  
where, the left portion represents the influence factor, the right portion corresponds to the age factor, and \(\alpha\in[0,1]\) is the parameter for tuning the trade-off between network stability and the layout quality. The aging formula reuse the exponential decay approach in reference [16] and its \(\beta\in IR\) is the aging rate which allows for tuning the evolutionary process. After combining the results of these two factors, the mobility factor is scaled within the range of \([0,1]\). The lower a node's mobility factor, the less likely it is to move during the layout process.  
The mobility factor is then applied to the layout method to yield the final layout result, as a mean to alter the force on nodes to improve the layout stability. In our experiments, the value of \(\alpha\) is set to 0.5. Users can adjust these tuning parameters from the user interface of the visualization system as needed, considering their preferences for layout quality, stability, and specific data characteristics.
## 4 Experiments, Results, and Analysis
### Performance of the DNN  
We employed several metrics to evaluate the accuracy of the DNN in estimating the parameters of the GGD: \(R^{2}\) value, explained variance, mean-squared error (MSE), and MAE. Table 3 summarizes the metric scores for each distribution parameter, along with the overall scores on all three parameters. The DNN demonstrates the highest accuracy in estimating the scale parameter \(s\), achieving an \(R^{2}\) of 0.9985 and an MAE of 0.0109 on the test set. The similarity between the \(R^{2}\) and explained variances scores indicates that the mean of the error is close to zero. This suggests that the estimation of the distribution parameters by the DNN is largely unbiased, which implies the DNN is less prone to overestimation or underestimation. Figure 8 reveals the DNN's challenges in accurately predicting parameter \(a\) values beyond 0.8. The scatter for parameters \(a\) and \(b\) appears more dispersed compared to that for parameter \(s\), with parameter \(b\) showing the largest contribution to overall  
Figure 7: Average MAE training and validation loss trends across 10-fold cross-validation.  
MAE and MSE. This discrepancy is likely due to the higher target values for parameter \(b\), resulting in greater error magnitudes. Additionally, the smaller magnitude of parameter \(a\) compared to parameters \(b\) and \(s\) reduces its relative contribution to the loss function during training, leading to less precise estimates and contributing to the increased scatter observed in Figure 8.  
Figure 9 illustrates how varying the fraction of the total dataset used for training affects the DNN's test set accuracy, as measured by the MAE. We observe a marked decrease in the overall MAE as the proportion of data used for training increases, showcasing rapid improvements in model accuracy. The rate of MAE reduction diminishes when training involves more than 50% of the data. Beyond this point, additional data yield only marginal improvements in accuracy, especially for parameters \(a\) and \(s\). Marginal gains can be seen for parameter \(b\) until all the training data are exhausted, suggesting that parameter \(b\)'s estimates can potentially benefit from additional training data. The trends suggest that increasing the dataset size can enhance model performance, but there exists a threshold beyond which the benefits become incremental.  
Figure 8: \(R^{2}\) plot for each of the individual parameters of the GGD, estimated using the DNN.  
Figure 9: Trend in MAE on the hold-out test set with increasing amounts of training data, expressed as a percentage of the total dataset used for training.