esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.3 Integration of Satellite and Ground-Based Observational Data for SM Monitoring Dynamics  
SM monitoring products and models derived from reanalysis and data assimilation processes are advancing rapidly with the introduction of innovative in situ measurement, satellite, and other remote sensing technologies alongside improved modeling capabilities [68]. Given that many SM datasets are derived from models, the application of satellite remote sensing has grown to supplement the gaps in observational data that models alone cannot fill [71].  
The availability of remote sensing products and SM monitoring networks is extensive, particularly in the semi-arid regions of the Western US, where a high-density SM measurement station network is found. Leveraging satellite data and SM monitoring network data, a range of global reanalysis products, especially in the USA, have been developed. These reanalysis products integrate satellite and ground-based observational data products and aim to create gridded data for entire territories, employing semi-empirical and physical models to enhance measurement precision by applying the modeling and data assimilation framework (Table 2).  
Among the main products that integrate terrestrial and satellite observation data for SM monitoring we find the Climate Prediction Center's (CPC) ([[https://www.cpc.ncep.noaa.gov/](https://www.cpc.ncep.noaa.gov/)]([https://www.cpc.ncep.noaa.gov/](https://www.cpc.ncep.noaa.gov/)), accessed on 24 April 2024) [72]. The CPC encompasses global monthly SM, evaporation, and runoff data starting from January 1948, offering both daily and monthly updates from 2008 to the present. This product leverages the Leaky Bucket hydrological model, utilizing observed precipitation and temperatures to estimate SM [73]. Also the \"Groundwater and Soil Moisture Conditions from GRACE Data Assimilation\" delivers weekly drought indicators for groundwater and SM, based on Gravity Recovery and Climate Experiment (GRACE) satellite data and other observations, integrated through a numerical model [74]. The NASA SPoRT-LIS Soil Moisture Products provide high-resolution (\(\sim\)3 km) real-time SM data to support regional and local modeling, employing the Noah land surface model, updated every 6 h since 2010 [75]; and the Soil Climate Analysis Network (SCAN), initiated in 1991, focuses on US agricultural areas, with over 210 stations collecting comprehensive SM and atmospheric data [76].  
These products are typically utilized for the monitoring of regenerative agriculture, particularly in grasslands/farmlands as well as for assessing soil conditions, crop growth, drought monitoring over large spatial areas, and various metrics and indicators of land degradation in semi-arid lands. We identified that this product presents valuable opportunities to develop methods for transparent, consistent, and replicable monitoring and reporting systems for SM at a landscape scale. Table 3 systematizes and shows the main and most used products that integrate satellite and ground observation data for SM monitoring.
## 4 Mesh Regularity: Geometric Quality and Gradient Reconstruction in MFAMs  
Recent studies on the effects of mesh regularity on the numerical solution, such as the one performed by [PERSON] [29], indicate that the quality of the mesh cannot be truly assessed without taking into account the nature of the problem, the numerical discretization approach, and the expected computational output, in addition to purely geometric quality indicators. Particularly, the FVM method is referred to by [PERSON] [29] as being resilient against the effects of non-regularity in non-structured meshes, which provides a good argument favoring its utilization in conjunction with the MFAM approach.  
In the following subsection an assessment of the MFAM quality, both geometric and gradient based, without delving into their effects over a particular problem is presented. The idea of this assessment is that, however resilient, errors in terms of discretization, interpolation and gradient calculation (which can be linked both to geometric properties of the mesh and to the methods used for gradient reconstruction) may plague our solution. The conclusion is that it is best to minimize these issues from the start.
## 3 Computational Examples and Analysis
### Case Study I: Bi-Objective Optimization  
A lot of research has shown that basalt fibre has a definite improvement influence on the crack resistance of asphalt materials [41]. For basalt fibre-reinforced asphalt mixture, the detailed preparation parameters are discussed and analyzed including fibre length and its content as well as asphalt-aggregate ratio. At the same time, three preparation parameters are optimized based on the volumetric and strength properties.
## 2 Materials and Methods
### Information Fusion in Tree-Based IOT Platform Using Proposed Method
#### 2.10.2 Method of Performance Evaluation  
The proposed statistical information fusion approach is tested on a Bidirectional graph using constructed using OMNeT++ simulations. Here, the distance between the sensor nodes are fixed; however, there is no protocol used in defining which sensor nodes will be neighbor node of a particular node as seen in Figure 3. The network constructed can be considered as an instance of an Ad-Hoc network.  
Figure 3: Non-hierarchical Bidirectional network with 20 sensor Nodes.  
The information accuracy of the unstructured network in Figure 3 is tested by comparing the network MSE for three different data fusion techniques. There is no modification to EBDKF algorithm for unstructured graph. But, a modified approach for the proposed method and average data fusion are employed here.  
A data cycle of proposed method will consist of obtaining a sensor measurement(\(s_{k}\)) at \(i\)th data gathering cycle and broadcasting it to neighbors. Then, the received information broadcasted in this manner is fused according to Equation (33). \(s_{k}\) in this equation is the sensor's own measurement at the \(i\)th data gathering cycle. This broadcasting will be done only one time since the diffusion technique also produce one diffusion estimate per data gathering cycle. In contrast, in consensus approaches, the measurements are broadcasted n times per data gathering cycle until all the nodes reach global estimate of mean of all sensor measurements [39; 40; 41; 42; 57]. Similar approach to proposed method is used for the average fusion technique in which only the data fusion technique is different.
###### Abstract  
This paper presents an experiment-based synthetic structural analysis method that combines digital image processing (DIP) and the particle difference method (PDM), which is a strong form-based meshfree method. The proposed method uses images to determine the displacement of deformed specimens, interpolates the displacement onto nodes of the PDM model without meshes or grids, and calculates the kinematic variables. Furthermore, the pixel extraction method for the target area and the method of setting the region of interest for expediting DIP were used during the synthetic structural analysis. A method for effectively expanding the number of tracking points and an improved method for labeling tracking points are also presented. To verify the performance of the analysis, the experimental and numerical analysis results of a three-point bending test on a rubber beam were compared in terms of various mechanical variables as well as with the PDM results of a simulated bending test. It was found that tracking point expansion and adjusting the radius of the domain of influence are advantageous for performing an accurate calculation without losing computational efficiency. It was demonstrated that the synthetic structural analysis effectively overcomes the shortcomings of the conventional experiments and the limitations of pure simulations.  
synthetic structural analysis; digital image processing (DIP); particle difference method (PDM); target tracking; bending test; numerical simulation +
Footnote †: journal: Applied Physics Letters
# A Simplified Analysis Method for Seismic Response of
## 2 Methods (EBSM-RAM)
### Solution of Deformation Effect in Free Field (RAM)  
The action effect of free field deformation, that is, the movement interaction between pile and soil, and the soil deformation will drive the deformation of the pile. Regarding the RAM [29; 30; 31] used in the seismic calculation of underground structures, inertial acceleration is applied to each layer of soil as a load to establish the maximum deformation of each layer  
Figure 1: Schematic diagram of EBSM-RAM.  
Figure 2: Calculation diagram of horizontal earthquake action of structure (EBSM).  
of soil element closest to the free field movement (Figure 3). However, the pile foundation is different from the underground structure. There is an obvious boundary between the underground structure and the soil. The load is clearly and directly transmitted from the soil to the underground structure. For pile foundations (especially large-scale pile groups), there are many complex pile-soil interactions when the pile body is inserted into the soil, rather than the simple state of clear separation of soil and pile body like the conventional underground structure. Therefore, the method for determining the effective inertial acceleration of pile foundations should be different from that in the seismic calculation of underground structures and should be adjusted.  
Firstly, the free field response of the soil layer is analyzed. The steady-state seismic response of multilayered linear elastic soil can be solved by a one-dimensional wave equation through programming [32], and the horizontal displacement \(U_{n}\) of the \(n\)th layer soil can be obtained as shown in Formula (1):  
\[U_{n}(z)=(E_{n}\exp(ik_{n}z)+F_{n}\exp(-ik_{n}z))\exp(\mathrm{i}\ \omega\ \mathrm{t}) \tag{1}\]  
where \(k_{n}=\omega/c_{n}\) and \(c_{n}=(u_{n}/\rho_{n})^{1/2}\) are the shear wave number and velocity of the \(n\)th layer, respectively, and \(E_{n}\) and \(F_{n}\) are the amplitude coefficients of the upward and downward waves in the \(n\)th layer medium, respectively. The detailed calculation process and derivation formula are recorded in the book [33] and will not be repeated here.  
Then, the second-order derivative of Formula (1) on time \(t\) is obtained, which is the horizontal acceleration of the soil layer. In this paper, the acceleration of each soil layer corresponding to the maximum acceleration of the top soil layer in the free field analysis results is extracted as the effective inertial acceleration load in the quasi-static calculation, and the shear stress distribution of the pile body finally obtained is closest to the time history analysis results. The distribution of inertial acceleration load can best reflect the distribution characteristics of shear stress of pile body under real horizontal seismic load.
# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 2 Methods
### Platform Design and Parameters Setting  
Figure 2 shows a block diagram of the proposed method. It consists of two ultrasonic transducers, one signal conditioning circuit, one NI platform for signal sampling, and two post-processing modules, including micro-Doppler processing and recognition modules. The ultrasonic transducers are MA300D1-1 by Murata Manufacturing Co.. The MCU on the signal conditioning circuit generates a designed pulse signal. The pulse signal is amplified and stimulates the ultrasonic transceiver to emit ultrasonic waves. The echos with interference reflected from finger gestures are received and amplified by the signal conditioning circuit and then sampled by ADC in the NI platform [44]. The raw data is firstly saved on the NI platform and processed offline on a laptop.  
The center frequency of MA300D1-1 is 300 kHz and bandwidth is very small. So the transmit signal is designed as modulated pulses, showed in Figure 3. The \(\tau\) is the width of pulse, \(T\) represents Pulse Repetition Interval (\(PRI\)). \(M\) is the total number of pulses for coherent processing, and \(T_{CPI}\) denotes Coherent Processing Interval (\(CPI\)). To obtain a high-quality micro-Doppler feature map requires high Doppler frequency resolution and range resolution. So the parameters of modulated pulses should be elaborately designed. The Doppler frequency resolution \(fd_{res}\) and the range resolution \(R_{res}\) are as [45]:  
\[\begin{split} f_{dres}&=\frac{1}{M\cdot T}\\ R_{res}&=\frac{c\cdot\tau}{2}\end{split} \tag{2}\]  
where \(c\) is the speed of ultrasonic wave travel in air and \(\lambda\) is wavelength of ultrasonic wave. In order to accurately recognize finger gestures, some parameters are designed carefully and are shown in the  
Figure 1: The micro-Doppler effect.  
Figure 2: The block diagram of the proposed method.  
left part of Table 1, and the performance indicators are calculated as shown in the right half of Table 1. As can be seen from the table, the Doppler frequency resolution is 15.625 Hz, the speed resolution reaches 9 mm/s and the distance resolution reaches 6.9 mm. Such high speed resolution and distance resolution are sufficient to clearly distinguish different finger motions.
## 6 Conclusions  
Traffic state prediction is one of the most critical components of intelligent transportation systems (ITS). However, traffic is highly fluctuating and most often unforeseeable. Hence, conventional models, for example, multinomial logit [39] and support vector regression models, fail to deliver effective results. On the contrary, deep neural network-based models can resolve these issues in a controllable manner.  
This article proposed a 1D-CNN and LSTM-based traffic state prediction model. Traffic flow and speed datasets were gathered from the Caltrans Performance Measurement System (PeMS) and Zenodo databases of a section of the road network from California's main urban areas and expressways of Guangzhou, China, respectively. The 1D convolution layers extract the features from the input data, and the LSTM deals with the learnt features for predicting traffic flow and speed. The results demonstrated improvements in terms of MAPE, RMSE, MAE, and \(R^{2}\) scores by 0.6-20.1%, 1.91-50.6%, 3.02-58.7%, and 0.58-1.15%, respectively, on the used traffic flow dataset. On the other hand, for the used traffic speed dataset, it achieved 12.1-21.83%, 7.6-96.6%, 25.7-82.6%, and 1.64-14.1% performance improvements in terms of MAPE, RMSE, MAE, and \(R^{2}\) score, respectively.  
One of the main drawbacks of the proposed model is its dependency on the data of a particular road section. Implementing the proposed model for a specific road section requires pre-training on the data of that road network. Unlike traditional models, it does not require weather data or information from adjacent road sections. Besides, the proposed model can predict traffic states over a longer time horizon. Future research aims to apply a multi-head attention-based transformer to overcome the LSTM and GRU's demerits and further improve the model's performance.  
Conceptualization, funding acquisition, and supervision by J.M.R.S.T.; investigation, data collection, code implementation, formal analysis, and writing--original draft preparation by S.R.; writing--review and editing by J.J.M.M., M.C.F. and J.M.R.S.T. All authors have read and agreed to the published version of the manuscript.  
This article is the result of the project Safe Cities--\"Inovacao para Construir Cidades Seguras\" with reference POCI-01-0247-FEDER-041435, cofunded by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020) under the PORTUGAL 2020 Partnership Agreement.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Loss Function** & **LSTM** & **GRU** & **CNN–GRU** & **CNN–LSTM** \\ \hline MSE & 6.47 & 6.515 & 6.44 * & 6.52 \\ MAPE & 6.39 & 6.9 & 7.1 & 6.01 * \\ LogCosh & 5.86 & 5.88 & 6.06 & 5.75 * \\ \hline \end{tabular} Here, * represents the best found result among the models under comparison.  
\end{table}
Table 6: MAE obtained by the models under study in terms of the used loss function on the traffic flow dataset.
## 4 Discussion  
### Discussion of Lateral Ultimate Capacity  
Figure 11 presents the normalized ultimate capacity of the monopile versus the L/D ratio for various soil conditions (i.e., different normalized stiffness of the monopile models with respect to the soil). The normalized ultimate capacity of the monopile varies linearly with increase in normalized stiffness and reaches a plateau at \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) of 984 for cases of L/D \(\leq\) 5. For cases with L/D \(>\) 5, the normalized ultimate capacity showed a trilinear trajectory, noting there is a very wide range between \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) of 4536 and \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) of 10,322. Given the observed trends, the authors believe that they can be assumed to be linear.  
For a given \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) ratio, the variation of monopiles' normalized ultimate capacity with different L/D ratios differs significantly between \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) of 10,250, where a 'rigid' response was observed for all L/D ratios, and for \(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\), a value of 127 where a flexible pile response was observed. This can be explained by inspecting the variation of the normalized ultimate capacity versus L/D ratio for all soil profiles considered. Figure 11 shows that for a stiff to hard clay site (\(\text{E}_{\text{p}}\,^{*}/\text{E}_{50}\) of less than 499), the pile lateral capacity reached a maximum at a L/D ratio of between 5-8. For soft clay sites, the capacity continues to increase as L/D increases, Figure 12.  
Figure 10: Load displacement curves of different lengths monopiles in (**a**) Clay1 and (**b**) Clay6.
## 4 Experimental Results and Discussion
### Second Experimental Scenario: Real Data
In summary, the FS-WOA imputation approach demonstrates promising results compared to various state-of-the-art methods, consistently ranking first across different datasets and missing-data rates. While other imputation methods performed well on some datasets, they struggled with others. For instance, the KNN method performed excellently on three specific datasets (Ionosphere, Sonar, and Vowel) with varying missing rates, but did not rank first on the remaining datasets. The ability of the FS-WOA approach to achieve consistently good results across a wide range of real-world datasets highlights its potential as a reliable missing-data imputation technique. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Single** & **KNN** & **MICE** & **GAIN** & **FS-WOA** \\ \hline \hline \multicolumn{6}{c}{**Missing rate 5\%**} \\ \hline Iris & 0.0490 (5.0) & 0.0123 (3.0) & 0.0117 (2.0) & 0.0375 (4.0) & **0.0112 (1.0)** \\ Breast cancer & 0.0402 (2.0) & 0.0408 (3.0) & 0.0501 (5.0) & 0.0454 (4.0) & **0.0293 (1.0)** \\ Wine & 0.0521 (5.0) & 0.0275 (2.0) & 0.0278 (3.0) & 0.0385 (4.0) & **0.0240 (1.0)** \\ Digits & 0.0487 (3.0) & 0.0215 (2.0) & 0.0590 (4.0) & 0.0664 (5.0) & **0.0209 (1.0)** \\ Diabetes & 0.0546 (4.0) & 0.0511 (3.0) & 0.0505 (2.0) & 0.0658 (5.0) & **0.0440 (1.0)** \\ Ionosphere & 0.0472 (2.0) & **0.0445 (1.0)** & 0.0586 (4.0) & 0.1120 (5.0) & 0.0538 (3.0) \\ Sonar & 0.0579 (3.0) & **0.0473 (1.0)** & 0.0695 (4.0) & 0.1029 (5.0) & 0.0535 (2.0) \\ Vowel & 0.0496 (4.0) & **0.0048 (1.0)** & 0.0437 (3.0) & 0.0566 (5.0) & 0.0129 (2.0) \\ Vehicle & 0.0506 (5.0) & 0.0100 (3.0) & 0.0087 (2.0) & 0.0196 (4.0) & **0.0082 (1.0)** \\ Mnist & 0.0508 (3.0) & 0.0216 (2.0) & 0.0595 (4.0) & 0.0603 (5.0) & **0.0165 (1.0)** \\ \hline \hline \multicolumn{6}{c}{**Missing rate 15\%**} \\ \hline Iris & 0.1220 (5.0) & 0.0836 (4.0) & **0.0322 (1.0)** & 0.0629 (3.0) & 0.0332 (2.0) \\ Breast cancer & 0.1256 (2.0) & 0.1567 (3.0) & 0.1677 (4.0) & 0.2283 (5.0) & **0.0952 (1.0)** \\ Wine & 0.1530 (5.0) & 0.1037 (4.0) & 0.0980 (2.0) & 0.1017 (3.0) & **0.0884 (1.0)** \\ Digits & 0.1483 (3.0) & 0.1071 (2.0) & 0.1761 (5.0) & 0.1737 (4.0) & **0.0783 (1.0)** \\ Diabetes & 0.1569 (3.0) & 0.1760 (4.0) & 0.1393 (2.0) & 0.1885 (5.0) & **0.1265 (1.0)** \\ Ionosphere & 0.1512 (2.0) & **0.1486 (1.0)** & 0.1931 (4.0) & 0.2725 (5.0) & 0.1577 (3.0) \\ Sonar & 0.1514 (2.0) & **0.1419 (1.0)** & 0.1817 (4.0) & 0.2232 (5.0) & 0.1575 (3.0) \\ Vowel & 0.1506 (5.0) & **0.0305 (1.0)** & 0.1234 (3.0) & 0.1379 (4.0) & 0.0562 (2.0) \\ Vehicle & 0.1565 (5.0) & 0.0373 (3.0) & 0.0286 (2.0) & 0.0472 (4.0) & **0.0257 (1.0)** \\ Mnist & 0.1478 (3.0) & 0.0992 (2.0) & 0.1751 (5.0) & 0.1677 (4.0) & **0.0596 (1.0)** \\ \hline Average ranks & 3.5 & 2.5 & 3.2 & 4.2 & **1.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the performance of imputation strategies on real-world datasets with a missing-data rate of 5%, 15%, and 25% (in the form of MSE \((rnk)\)).
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
## 4 Result Analysis
### Settlement Analysis  
Under the influence of seismic loads, large-span shield tunnel structures are prone to experiencing uneven settlement along their longitudinal axis. To investigate the impact of different degrees of preload loss on the structural longitudinal strength and deformation, vertical displacement time history curves at the bottom centre of the tunnel were selected under different PGAs for both normal conditions and cases where preload is completely lost, as shown in Figure 15a. It can be observed that tunnel settlement increases with the increasing intensity of seismic loads. Under the same seismic intensity, the presence of preload reduces the settlement of the shield tunnel. For seismic peak accelerations of 0.1 g, 0.3 g, and 0.5g, in cases where preload is not lost, the settlement at the bottom increased by 1.24%, 0.54%, and 0.51%, respectively.  
To study the influence of preload loss on the longitudinal stiffness of the shield tunnel, the relative settlement of the entire structure can be obtained by taking the difference between the maximum vertical displacement at the bottom centre and the average vertical displacement at both ends, as shown in Figure 15b. Under the influence of seismic loads, the overall relative settlement of the structure increases with the degree of preload loss. This indicates that the application of preload has an optimizing effect on the reduction of longitudinal stiffness under seismic loads. In this case, the relative settlement along the depth direction at different preload loss levels can be used as an indicator to measure the reduction in longitudinal stiffness of a multi-ring shield tunnel. Taking the peak acceleration of 0.5 g as an example, with an increase in the degree of preload loss, the relative settlement shows an increasing trend. When the preload loss reaches 57.14% (corresponding to a preload force of 120 kN), the vertical displacement increases by 28.46%. In the case of complete preload loss, the vertical relative displacement increases by 32.97%. According to existing literature, the specific method for calculating stiffness reduction is related to the magnitude of static loads applied. Due to the complex loading conditions experienced by this structure during the dynamic phase, it is not possible to accurately calculate the reduction in longitudinal stiffness. However, based on the results of this study, it is clear that preload loss has a significant diminishing effect on the longitudinal stiffness of the structure.