esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 2 Prediction Models for Partially Saturated Soils
### Adopted Empirical Methodology  
In the field of geotechnical earthquake engineering, studies on partially saturated soils are markedly less common than on fully saturated soils, which is probably due to difficulties and problems encountered during laboratory research or field investigation. A review of the literature indicated that only a few models are available to predict the important aspects of the liquefaction mechanism and response of partially saturated sands with occluded gas bubbles, so this topic requires further attention. This study used a potentially simple empirical methodology to evaluate the liquefaction-resistance ratio (\(LRR\)) in terms of porefluid bulk modulus (\(K_{f}\)), maximum volumetric strain (\(\epsilon_{v}^{*}\)), and excess pore-pressure ratio (\(r_{u}\)). This method was built on the basis of experimental observations, analysis of the database compiled from the literature, and theoretical justification.  
The liquefaction-resistance ratio (\(LRR\)) can be estimated as a function of the maximum volumetric strain (\(\epsilon_{v}^{*}\)) or the bulk modulus of the pore fluid (\(K_{f}\)):  
\[LRR=f(\epsilon_{v}^{*})=f\Big{(}K_{f}\Big{)} \tag{4}\]  
As Equation (3) indicates, the maximum volumetric strain (\(\epsilon_{v}^{*}\)) is a function of the degree of saturation (\(S_{r}\)), initial effective confining stress (\(\sigma_{c}^{\prime}\)), back or pore fluid pressure (\(u_{0}\)), and void ratio (\(e\)) or relative density (\(D_{r}\)):  
\[\epsilon_{v}^{*}=f\big{(}S_{r},\;\sigma_{c}^{\prime},\;u_{0},\;D_{r}\big{)} \tag{5}\]  
The normalized excess pore-pressure ratio (\(\frac{r_{u-part}}{r_{u-sat}}\)) can be inversely correlated to the liquefaction-resistance ratio (\(LRR\)):  
\[\frac{r_{u-part}}{r_{u-sat}}=f^{-1}(LRR) \tag{6}\]  
where \(r_{u-sat}\) and \(r_{u-part}\) are the excess pore-pressure ratios generated in fully and partially saturated sands, respectively.
###### Abstract  
The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.  
indoor and outdoor environments; greenhouse; precision agriculture; SLAM; UAV +
Footnote †: journal: Applied sciences
## 3 Materials and Methods
### Models Overview
#### 3.7.6 Gated Recurrent Unit (GRU)  
Gated Recurrent Units (GRU) [37] are a variant of recurrent neural networks that aim to improve upon traditional LSTMs by simplifying the architecture while maintaining the capability to capture sequential dependencies. GRUs are particularly effective for HAR tasks where capturing long-term dependencies is crucial, yet computational efficiency is also a consideration.  
The GRU model consists of two main gates--reset and update gates--that regulate the flow of information. The reset gate \(r_{t}\) controls how much of the previous information is forgotten, while the update gate \(z_{t}\) determines how much of the past information is carried forward. The primary equations for the GRU are given by the following:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]+b_{r}), \tag{17}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z}), \tag{18}\]  
\[\tilde{h}_{t}=\tanh(W\cdot[r_{t}\odot h_{t-1},x_{t}]+b), \tag{19}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\tilde{h}_{t}, \tag{20}\]  
where \(r_{t}\) and \(z_{t}\) are the reset and update gates, respectively, and \(\tilde{h}_{t}\) is the candidate activation. GRUs reduce the complexity of the network by combining the forget and input gates into a single update gate, which often results in faster training times compared to LSTM. Figure 6 illustrates this GRU structure, displaying the flow of data through the reset and update gates for sequential data processing.  
The GRU hyperparameters, optimized using nested cross-validation (outer = 5, inner = 3), are summarized in Table 12. Temporary values include two layers, 64 GRU units, a dropout rate of 0.3, and the Adam optimizer with a learning rate of 0.0015.  
By leveraging GRU's simplified architecture, this model is able to efficiently capture both short-term and long-term dependencies, making it suitable for human activity recognition with complex sensor data. In this architecture, the reset and update gates play critical roles in managing sequential data processing. The asterisk (*) in the figure denotes multiplication, and the \"\(-1\)\" represents subtraction, key operations that adjust the information flow through the GRU unit.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Sensor Configuration** & **GRU Layers** & **GRU Units** & **Dropout Rate** & **Optimizer/Learning Rate** \\ \hline
2 Sensors—Non-overlapping & 2 & 96 & 0.30 & Adam/0.0032 \\
2 Sensors—50\% Overlapping & 4 & 64 & 0.20 & Adamax/0.0053 \\
4 Sensors—Non-overlapping & 4 & 64 & 0.30 & Adam/0.0027 \\
4 Sensors—50\% Overlapping & 3 & 96 & 0.30 & Adam/0.0034 \\ \hline \end{tabular}
\end{table}
Table 12: Optimal Hyperparameters for GRU in Different Sensor Configurations.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
###### Abstract  
Model updating plays an important role in dynamics modeling with high accuracy, which is widely used in mechanical engineering. In this paper, a model updating method using frequency response function (FRF) is proposed based on Sherman-Morrison formula, in which only the initial FRFs and parameter perturbations are employed to calculate the sensitivity avoiding repeated finite element (FE) analyses and improving the computational efficiency. Firstly, the sensitivity of FRFs to the design parameters is calculated by Sherman-Morrison formula based on the QR decomposition of the system dynamic stiffness matrix variation after parameter perturbations, then the influence of damping on the amplitude of FRFs is considered to select an appropriate frequency range, and finally conduct the model updating according to the sensitivity of the FRFs. By employing simulation examples of a truss and a solar wing and the experiment of an aluminum frame, the updating error is still within \(\pm 1.00\%\) in the condition of \(5\%\) random white noise, which shows the proposed method has high accuracy and a certain anti-noise capability. When only a few numbers of frequency points are selected near the resonance peak of the FRFs, the result shows that selecting the appropriate frequency range and points can reduce the computational cost. The results of the experiment study show that the proposed method can effectively identify the structural parameters. The above results verify the feasibility and effectiveness of proposed model updating method using FRFs.  
model updating; Sherman-Morrison formula; frequency response functions; sensitivity analysis +
Footnote †: journal: Applied Physics Letters
# The Impact of Hollow Wear on the Stability of High-Speed Railway Vehicles
###### Abstract  
Hollow wear on wheels is a common form of surface damage often observed in high-velocity vehicles. It is widely recognized that hollow wear of the wheel tread degrades the dynamic performance of rail vehicles, especially in the issue commonly referred to as \"operational stability\", and leads to abnormal wheel-rail contact interactions. However, the evaluation criteria for vehicle stability are not uniform, which affects the assessment of wheel conditions and the timing of wheel re-profiling during maintenance. Therefore, numerical simulations were conducted by matching the measured worn wheel profiles with standard rails, and three different methods were employed to evaluate vehicle stability in this article. The numerical results revealed that the wheel equivalent conicity exhibits a nonlinear characteristic caused by hollow wear, which means that the nominal equivalent conicity is unable to accurately represent the geometric contact relationship between the wheel and rail. Under identical wheel wear conditions, a certain difference was observed in the critical speed of the vehicle determined by the velocity-reducing method and the bifurcation configuration method. Both methods were capable of reflecting the impact of wheel hollow wear on vehicle stability at the critical speed. Compared to the velocity-reducing method, the bifurcation configuration method can better reflect the transition process of a vehicle from stable running to hunting instability. Furthermore, the lateral vibration acceleration values measured above the bogie frame indicated that slight wheel wear is insensitive to increased speed. However, when the wear was severe, the lateral vibration acceleration of the bogie was found to increase sharply, exceeding the established stability criteria. This phenomenon was consistent with the safety alarms that occurred during actual vehicle operation, indicating that the vehicle had failed to meet stability requirements.  
hollow wear; wheel-rail contact; vehicle stability; critical speed; dynamic simulation 2025 15 4060 +
Footnote †: journal: Applied Sciences
###### Abstract  
Faster-than-Nyquist (FTN) signal processing, which transmits signals faster than the Nyquist rate, is a representative method for improving throughput efficiency sacrificed performance degradation due to inter-symbol interference. To overcome this problem, this paper proposed FTN signal processing based on the unequal error probability to improve performance. The unequal error probability method divides encoded bits into groups according to priority, and FTN interference rates are differently applied to each group. A lower FTN interference ratio is allocated to the group to which high-priority encoded bits belong and a higher FTN interference ratio is allocated to the group to which low-priority encoded bits belong, thus performance improvement can be obtained compared to the conventional FTN method, with the same interference ratio. In addition, we applied the proposed FTN signal processing, based on the unequal error probability method, to the OFDM (orthogonal frequency division multiplexing) system in multipath channel environments. In the simulations, the performance of the proposed method was better than that of the conventional FTN method by about 0.2 dB to 0.3 dB, with an interference ratio of 20%, 30%, and 40%. In addition, in multipath channels, we confirmed that by applying the proposed unequal error probability, the OFDM-FTN method improves performance to a larger extent than the conventional OFDM-FTN method.  
F 22 April 2019; Accepted: 10 June 2019; Published: 13 June 2019
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 4 Methodology of Algebraic Point Projection
### Algebraic Point Projection for a NURBS Curve
#### 4.1.4 Improvement to Second Order Algebraic Point Projection  
While second-order algebraic point projection is more accurate, as should be expected, relative to first-order algebraic point projection, it could be further improved, as discussed below. Since the \(\mathbf{n}\) computed locally at a point in the domain is relatively inaccurate for approximating the direction of the vector \(\mathbf{x}-\mathbf{x}_{f}\) at greater distances, Equation (26) will fail to provide the correct solution as distance from the curve increases. As illustrated in Figure 10(a), at test points along the line where \(\mathbf{n}\) does not intersect with the Bezier curve, no valid solution is obtained. This is because the normal vector \(\mathbf{n}\) generated using algebraic level sets usually does not coincide with normal vector \(\mathbf{n}_{f}\) at footpoint, especially when test point is far away. One solution to this issue is to construct a new vector \(\mathbf{n}_{corr}\), which is guaranteed to have positive discriminant in Equation (26). A trial \(\mathbf{n}_{corr}\) that points to the centroid of control polygon achieves the desired result. This choice of the normal direction at points far away from the curve is simple and effective as shown in Figure 10(b). While this correction is not general, it is likely to provide sufficient accuracy in practice for quadrature points where the behavior of the immersed boundary is blended with that of the underlying domain.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Performance Parameters, Evaluation and Comparison
#### 4.1.3 Sensitivity  
The third evaluation metric used for ProSenAD performance measurement is recall. It is also known as sensitivity, which is the ratio between the number of attacks detected by the model and the total attacks present in the dataset. Equation (3) is used to calculate the sensitivity/recall.  
\[\frac{TP}{TP+FN} \tag{3}\]  
Figure 12 shows the average recall/sensitivity results of ProSenAD, GAN-C, ML-LGBM, GB, XGBoost, and baseline LGBM. The comparative analysis shows that proposed model outperforms the approaches used as benchmarks. For instance, it shows an increased average recall for protocol-specific attack scenario in comparison with GAN-C where the difference between their results is 16.1%. SN-inherited attack scenario also shows that the ProSenAD performs well with a 4.2% increase in recall when compared with the same approach. Next, the comparison with GB in the first scenario shows the proposed model's high performance with a 13% improved recall, followed by 16.8% improvement of the proposed model when compared with GB in the second attack detection scenario. XGBoost performs with a 0.837 recall in the first scenario and 0.844 recall in the second scenario, as compared to ProSenAD which performs better with a recall of 0.981 in the first attack case and 0.962 in the second attack case, indicating a 14.4% and 11.8% increase in the comparative performance, respectively. Next, the LGBM shows an average recall of 0.927 in protocol-specific attack detection, and 0.909 recall in SN-inherited attacks detection. ProSenAD outperforms these by an increase of 5.4% and 5.3% in the sensitivity, respectively. The performance comparison of ML-LGBM and the proposed model shows a 0.9% increase in recall of the latter. Although the difference is small, ProSenAD performs better in terms of the number of attacks that it detects as compared to ML-LGBM.  
Figure 11: Comparison of precision/PPV.
# Evaluation of Symmetrical Exercises in Scoliosis by Using Thermal Scanning
## 4 Discussion
Exercise 2 mainly involved the muscles in the upper torso, even if it also involves an extension movement of the lower limbs. It should be noted that in the case of subjects with slightly increased areas of temperature in the trapezius muscles (contracture areas), these areas became more active during the exercise (as can be seen in subject 2 without scoliosis presented in Figure 3). The analysis of the images taken after the execution of exercise 3 in the prone position, highlighted an activation of the muscles in the entire back (mainly trapezius and erector spinae). The analysis of the initial images taken from the subjects in the study group highlighted what was found in other studies in the literature that used the thermal imaging camera [18], namely high temperature areas on the convexity of the scoliotic curve. The acquisition of images after each exercise revealed an imbalance in the functioning of the back muscles. At the thoracic level, the muscles are more active on convexity side during exercises performed with the upper limbs, generally the subjects performing slightly asymmetrical elevations of the upper limbs. Exercise 3 in supine position activates more back muscles than in quadrupeds (four-legged position). In exchange, in the case of subjects with convexity in the lumbar area, it was found that, initially, there is a stronger activation of the muscles on the concave area (we assume due to the eccentric contractions in the concavity and the need to stabilize the pelvic girdle) and only after several repetitions of the exercise there is an increase in temperature in the muscle area of the convex side. This could be observed during the exercises 1 and 3. All these observations were made in the first experiment in which subjects with scoliosis were not corrected by the physiotherapist who participated in the study. In order to evaluate, in more detail, the efficiency of the 3 exercises, we proposed to the subjects diagnosed with scoliosis a second experiment in which they were corrected by the physiotherapist so that the exercises could be performed in symmetrically maintained positions and the movements were conducted correctly and leveled, by the right half body, as well as by the left one. Following this experiment, it was found that muscle activation was much more uniform between the right side and the left side of the body as can be seen in Figures 7-9 which were presented as an example in this study. Following the second experiment, we came to the conclusion that exercises that address the back muscles, performed symmetrically under the supervision of a specialist, can activate the back muscles in a balanced way in the case of children with mild scoliosis. We believe that it is important that these exercises be taught correctly by children with scoliosis under the supervision of specialists and only then, that they be introduced into a program performed at home.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### The Proposed Segmentation Method
#### 2.2.2 Test Set Cropping Rule  
As the model is trained on a training set of \(256\times 256\) and \(512\times 512\) resolution, the test set needs to be cropped to the corresponding size and then input to the model for segmentation, and then the small pieces of the segmented images are combined in order to achieve accurate segmentation of the original remote sensing image. If the test set is directly cropped, the resolution size of the image is often not exactly cropped into integer blocks. Therefore, in order to ensure that the remote sensing image can be segmented in its entirety, we use a method similar to the voting mechanism [16] to fill the original remote sensing image. When cropping by rule for large resolution remote sensing images, the move step is half the length of the resolution edge of the cut image. Additionally, to ensure that each part of the remote sensing image can be segmented four times, the left and top sides of the remote sensing image are filled with blanks of half the resolution edge length of the cut image, and the right and bottom sides of the remote sensing image are filled with blanks of the resolution edge length of the cut image. The schematic diagram is shown in Figure 4, where the red box represents the remote sensing image and the outer yellow represents the filled image. The black box is the initial position and the sequential cropping is performed at one-half the size of the resolution edge of the cut image, and all parts within the red box will have been segmented four times. Semantic segmentation is performed on each pixel point in the image, and the predicted output is a probability value between 0 and 1 [17,18]. As each pixel point in the remote sensing image is computed four times, the sum of the four times is averaged to better represent the true class of the point and thus achieve a more accurate segmentation.
## 4 Results and Discussion
### Effect of PCA -ADASYN Algorithm on the Results  
The PCA and ADASYN algorithms improved the model's F1-Score by 4.02% and 10.79% for the model, respectively. This section examined the impact of combining these  
Figure 9: Radar charts of the four metrics before and after using ADASYN at ResInceptNet-SA: (**a**) Accuracy. (**b**) Precision. (**c**) Recall. (**d**) F1-Score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Testing Time(s)** \\ \hline Original & 94.195 & 85.528 & 92.502 & 88.007 & 11.6 \\ ADASYN & 97.529 & 97.529 & 97.386 & 97.504 & 32.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of metrics before and after model use of ADASYN.  
two algorithms on the model's predictive performance, namely the PCA-ADASYN algorithm proposed.  
Firstly, the PCA was applied to reduce the dimensionality of the original high-dimensional data in the NSL-KDD dataset, while retaining its key features. Then, the ADASYN algorithm is used to balance the dataset samples after dimensionality reduction. The models' trainings using the datasets processed by the PCA-ADASYN algorithm are shown in Sections 4.2 and 4.3, and this section is for classification verification. The experimental results are presented in Table 9. The F1-Score of the proposed model, using the dataset processed by PCA-ADASYN, improved by 12.88%, 8.51%, and 1.88% compared with the original dataset, the dataset processed by PCA, and the dataset processed by ADASYN, respectively. Due to the increased size of the balanced dataset, the test time increased; however, it was reduced by 3.17% compared with the dataset processed by ADASYN, as shown in Figure 10.
## 2 Materials and Methods  
### Study Regions and Flux Towers  
The study region covers the mainland of Europe and part of Russia, excluding England and parts of Siberia. Flux towers provide direct measurements of ecosystem carbon fluxes. In this study, 40 sites located in Europe (Figure 1) were selected from FLUXNET 2015 ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/))) [60] based on the availability of V\({}_{\text{cmax}}\) data. In addition, we screened out EuroFLUX sites with inconsistent profiles of LAI and GPP derived from eddy covariance data. Given the limited pool of shrubland sites in EuroFLUX, the SH site from AmeriFLUX was also included, since only one CSH site (i.e., US-KS2) is available in AmeriFLUX. The land cover types in the study region were extracted from the MODIS International Geosphere-Biosphere Programme (IGBP) classification product (Figure 1), which has a spatial resolution of 500 m. The land cover types were grouped into a total of ten PFTs, including croplands (CRO), closed shrublands (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grasslands (GRA), mixed forest (MF), open shrublands (OSH), and wetland (WET).