esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Prediction Model Using Machine Learning Algorithms
## 3 Machine Learning in Smart Farming
While AI technology may seem daunting to some, there are ways to educate farmers about the benefits of smart farming and the usage of machine learning algorithms. Farmers need to be aware of the great benefits they may achieve if they use automation and AI on their farms. Training and informative workshops are the fastest ways to do so. This training needs to include foundation knowledge of ML, identifying use cases in agriculture, the importance of data collection and preparation for better prediction, ML algorithms and their best applications, and addressing concerns or misconceptions related to ethical and privacy considerations. They should be introduced to success stories of smart farms and encouraged to increase collaboration and share knowledge with their peers. A very important point is to introduce them to user-friendly tools and platforms that do not require programming skills for easy adaption. Since ML is a rapidly evolving field and new techniques and tools emerge frequently, we should encourage farmers to continue learning and stay updated with the latest developments in the field. Despite the optimistic outlook of ML in agriculture, similar to any ML issue, the quality of the results is predominantly influenced by the quality of the input data. The efficacy of crop yield prediction depends heavily on the quality and availability of data. Crop prediction requires wide-ranging data, including weather, soil, historical yield, and satellite imagery. Guaranteeing data quality through efficient collection, preprocessing, and feature selection is critical in model development. However, agricultural big data introduces several challenges, which will be examined in the following section.
## 4 Materials and Methods
### EO Composition Analysis and Sample Preparation
#### 4.4.2 GC-FID Analysis  
For the quantitative characterization of the _H. laricifolium_ by GC-FID, the operating chromatographic conditions and the chromatographic columns were the same as indicated in the previous paragraph. The percentage of each identified EO component was determined by comparing the area of the corresponding GC peak with the total area of peaks in the gas chromatogram, without applying a correction factor.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Data Encapsulation  
Tensioning job data comprise tensioning parameters, No. 1 channel data, and No. 2 channel data, as shown in Figure 4. On the basis of the characteristics of the data sensed during operations and the tensioning process, the data types are divided into flag, conclusion, and process. Flag packets (F) store the parameters and basic information of the tensioning job; conclusion packets (C) store the detection values of tensioning jobs at various stages and are reflections of various technical indicators of tensioning jobs; and process packets (P) continuously record the changing trend of each indicator in the entire operation process. The performance of the reading, analysis, and storage of data transport is considered comprehensively. Data security is combined with network transport cost, and the job data are encapsulated into a structured packet. The packet structure is shown in Figure 5. The packet is composed of data blocks, such as packet header, job number, data length, data type, job data, and check bit. According to the above rules, the structure of process packets is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Data Block Name**} & \multicolumn{3}{c}{**Field**} \\ \cline{3-5}  & & **Name** & **Byte** & **Remarks** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Packet header} & Header & 4 & \\ \cline{3-5}  & & Device ID & 10 & Device ID uniquely identifies each device \\ \hline
2 & Job number & Job number & 13 & E.g.: AA-BBBB-CCCCC \\ \hline
3 & Data length & Data length & 2 & \\ \hline
4 & Data type & Data type & 1 & The range of values is flag, conclusion, and process. \\ \hline \multirow{6}{*}{5} & \multirow{6}{*}{Job data} & Tensioning mode & 1 & The values range from 1 to 9. \\ \cline{3-5}  & & Tensioning progress & 1 & The values range from 1 to 5. \\ \cline{3-5}  & & \multirow{2}{*}{Tensioning time} & \multirow{2}{*}{6} & Binary-coded decimal (BCD) \\  & & & & E.g.: \({}^{*}\)181020154341\({}^{*}\) means the current time is \\ \cline{3-5}  & & & 15:43:41 on 20 October 2018. \\ \cline{3-5}  & & & No. 1 channel & 8 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Force of No. 1 jacks} & 4 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Displacement of No. 1 jacks} & 2 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Force of No. 2 jacks} & 4 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Displacement of No. 2 jacks} & 2 \\ \cline{3-5}  & & & \multicolumn{2}{c}{No. 2 channel} & 8 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Force of No. 3 jacks} & 4 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Displacement of No. 3 jacks} & 2 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Force of No. 4 jacks} & 4 \\ \cline{3-5}  & & & \multicolumn{2}{c}{Displacement of No. 4 jacks} & 2 \\ \hline
6 & Check bit & Check bit & 2 & Cyclic Redundancy Check (CRC) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of process packets.  
Figure 4: Job data structure.  
Figure 5: Structure of structured packets.
## 2 Details and Performance Analysis of QODELFA
### Main Procedures of QODELFA
#### 2.1.1 Procedures of DE  
DE is a simple and efficient meta-heuristic optimization method [33]. The basics of DE are described in this subsection.  
* Mutation: The concept of DE depends on generating new solutions by \[Sl_{m_{i}}=Sl_{md_{1}}+F_{DE}\cdot(Sl_{md_{2}}-Sl_{md_{3}})\] (1) where \(Sl_{m_{i}}\) stands for the mutant solution, \(Sl_{md_{1}}\), \(Sl_{md_{2}}\), and \(Sl_{md_{3}}\) are randomly defined solutions, and \(F_{DE}\in[0,2]\) is an amplifying parameter.  
For better enhancing the DE's performance, the superior solution \(Sl_{su}\) is deduced in every generation, and multiple randomly defined solutions (\(Sl_{md_{1}}\), \(Sl_{md_{2}}\), \(Sl_{md_{3}}\), and \(Sl_{md_{4}}\)) are created. This procedure can be explained as  
\[Sl_{m_{i}}=Sl_{su}+P_{DE}^{m}(Sl_{md_{1}}-Sl_{md_{2}}+Sl_{md_{3}}-Sl_{md_{4}}), \tag{2}\]  
where \(P_{DE}^{m}\) is modified by utilizing it as given in the following equation  
\[F_{DE}^{m}=F_{\max}-(t-1)(F_{\max}-F_{\min})/(M-1), \tag{3}\]  
where \(F_{\min}=0\), \(F_{\max}=2\), \(t\) is the iteration number, and \(M\) is the maximum number of iterations.  
\(\bullet\) Crossover:  
Another improvement of the searching process is done by executing the crossover, where trial solutions \(tr_{i}\) in iteration \(t+1\) are evolved by  
\[tr_{i}^{t+1}=\left\{\begin{array}{ccc}Sl_{m_{i}}^{t+1}&if&r\leq cr\\ Sl_{i}^{t}&if&r\geq cr\end{array}\right., \tag{4}\]  
where \(r\in[0,1]\), and \(cr\) is the crossover rate.  
\(\bullet\) Greedy Selection:  
The last step in DE is to perform the selection by the 'greedy selection', where a comparison between \(Sl_{m_{i}}^{t+1}\) and \(Sl_{i}^{t}\) is made, then the most superior solution will be placed in the population.
## 4 Discussion and Conclusions
### Conclusions and Future Research Directions  
The proposed approach using probability maps improved the segmentation quality for most models, particularly in terms of metrics such as the IoU, F1 score, and recall. FastFCN, ENet, and DeepLabV3+ showed the most significant improvements, while U-Net demonstrated some cases of reduced effectiveness. Comparisons with the state-of-the-art YOLO11x-seg model showed that the proposed approach allowed SegNet (2015) to achieve results comparable to modern architectures, while other models significantly outperformed YOLO11x-seg. The visual results confirmed improved segmentation accuracy, especially for thin and fine cracks. Training time changes were minimal and, in some cases, even decreased.  
Future research directions include the following:  
* Optimization of the classification ensemble: Using more advanced classification models or alternative ensemble strategies to create more accurate probability maps.
* Extending testing to various surface types: Exploring the effectiveness of the approach for cracks in asphalt, metal structures, bricks, and other materials.
* Modifying segmentation model architectures: Investigating the use of attention mechanisms or other methods to integrate probability maps more effectively for enhanced segmentation.  
Conceptualization, V.T., R.T. and Y.T.; methodology, V.T., Y.T. and R.T.; software, R.T. and V.T.; validation, Y.T. and R.T.; formal analysis, Y.T., R.T. and V.T.; investigation, V.T., Y.T. and R.T.; resources, Y.T. and R.T.; data curation, V.T. and Y.T.; writing--original draft preparation, V.T. and R.T.; writing--review and editing, R.T., Y.T. and V.T.; visualization, V.T. and R.T.; supervision, R.T.; funding acquisition R.T. and Y.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure A3: FastFCN model architecture.  
Figure A4: HRNet model architecture.  
Figure A5: SegNet model architecture.
## 3 Results
### Cytoprotective Effects of Ginsenoside Rh1 Are Mediated through PI3K/Akt/GSK-3 Pathway  
To confirm whether ginsengide Rh1 attenuated neuronal death via regulation of the Akt and GSK-3 pathways, we used immunoblotolt analysis. In the co-treated group of ginsenoside Rh1 and A\(\beta\), phospho-Akt/Akt and phospho-GSK-3\(\beta\)/GSK-3\(\beta\) were found to up-regulate compared to the A\(\beta\) oligomers only treatment (Figure 5a,b). Also, phospho-p38 MAPK/p38 MAPK was found to down-regulate as compared to the A\(\beta\)-alone treated group, indicating that ginsengide Rh1 might mediate its effects through the activation phospho-Akt/Akt and phospho-GSK-3\(\beta\)/GSK-3\(\beta\) and deactivation of phospho-p38 MAPK/p38 MAPK signaling pathways (Figure 5c).  
Figure 2: Ginsenoside Rh1 attenuated neuronal cell death induced by A\(\beta\) oligomers in SH-SYSY cells. After 24 h of treatment with 1 uM A\(\beta\) oligomers or/and 40 uM ginsengide Rh1, the SH-SYSY cells were stained with fluorescein diacetate (FDA) and propidium iodide (PI) double staining. PI-positive dead cells (red fluorescence) demonstrated that A\(\beta\) oligomers induced neuronal cell death. Scale bar indicates 100 um.  
Ginsensoside Rh1 Inhibits Cell Death through the Activation of PI3K/Akt Induced by A\(\beta\) Oligomers in SH-SYSY Cells  
To confirm our hypothesis, we further examined the regulation of signaling pathways by ginsengide Rh1 and A\(\beta\) oligomers in the presence of the PI3K/Akt pathway inhibitor (LY294002). It was found that in the presence of the LY294002 (10 uM), Rh1 was unable to show its cytoprotective effects (Figure 6). These results indicated that all the effects of Rh1 observed in A\(\beta\)-treated SH-SYSY cells are mediated through the PI3K/Akt pathway.
## 5 Experiments and Results
### Evolutionary Optimisation  
We performed 30 runs of our proposed HEA per case study to optimise the swarm parameters using surrogate models combined with ARGoS simulations. We have optimised one new, unseen scenario (different initial conditions from all the included in the training and testing datasets) to test the surrogate model (\(gp\_nn\)) in an unseen situation. The achieved optimisation results are shown in Table 6 which presents the minimum and mean fitness values obtained from the 30 runs and their standard deviation. Moreover, the mean elapsed time is reported, which was about one and a half hour for 10 UAVs. It shows the improvement associated with the use of surrogate models, as we spent 9 h per run in our previous work [7] based only on simulations (now it is 6.4 times faster). This also allowed us to optimise swarms of 15 and 30 UAVs in a reasonable time, e.g., 2.6 and 19.8 h on average, respectively. Given the stochastic nature of the initial population of the HEA, some optimisation runs did not converge (1 for 5 UAVs and 12 for 15 UAVs), despite beginning with 100 randomly generated individuals. In the following section, we evaluate how robust these configurations are when tested on 30 unseen scenarios.
## 3 Energy Efficient Joint Power Control and User Association Optimization Algorithm
### Complexity Analysis  
The asymptotic complexity of the proposed algorithm is analyzed in this section. In Algorithm 1, the calculation of each user's association variable and each BS's transmit power entails \(KJ\) operations. Correspondingly, the computation in Algorithms 2-4 also calls \(O\left(KJ\right)\) operations. Suppose Algorithms 1-4 need \(\Delta_{1}\), \(\Delta_{2}\), \(\Delta_{3}\) and \(\Delta_{4}\) iterations to converge. The total complexity of the proposed algorithm is thus \(O\left(K^{4}J^{4}\Delta_{1}\Delta_{2}\Delta_{3}\Delta_{4}\right)\). Compared with the exhaustive search for the joint optimization problem, which has a worst case complexity of \(O\left(2^{KJ}\right)\), the proposed algorithm has a much lower complexity. Moreover, \(\Delta_{1}\), \(\Delta_{2}\), \(\Delta_{3}\) and \(\Delta_{4}\) are small enough, as can be seen in the simulation.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Decision-Making and Actuators  
The decision-making component addresses the problem of mitigating the network situations that may disrupt the normal operation of the network elements and the services provided. In particular cases, it can take also part in the performance optimization process of the services offered by the mobile  
Figure 5: Example of Bayesian Network (Decision Tree) in network diagnosis.  
Figure 6: Issue tracking algorithm.  
network, thereby playing an active role in tasks such as load balancing or management of traffic with multimedia content. To this end, the decision-making processes receive information from the analysis stage (mainly from diagnosis and countermeasures tracking) and then select a set of responses to be executed. According to ISO/IEC 7498-2 [46], such correctives are referred as security safeguards, and they are involved in prevention, mitigation and source identification. The strategy for selecting optimal countermeasures must balance the cost of implementation of the safeguard and the achieved reduction of the incidence impact [47]. The available actions are distributed as NFV-Apps, so that these constitute a large repository of potential countermeasures. The network components that execute the safeguards are known as actuators. An example of their implementation is the mitigation of distributed denial of service attacks: when the analysis component performs a diagnosis related to this category of incidences, the report includes information about the compromised assets, impact, prediction and the attack vector. In the example, the first step of decision-making is to prevent the spreading of the threat by deploying firewall NFV-Apps (specific actuators), thus taking into account the predictions of their distribution. The second step is to mitigate the threat by implementing honeypot NFV-Apps and the malicious traffic redirection towards sinkholes. Once the impact of the attack is minimized, the last step is identifying the sources by applying IP traceback algorithms [48], from which they could be blocked or alerted. The deployment of the various actions is coordinated by an orchestrator agent, which ensures that the virtual resources that implement the countermeasures are available and do not affect the system performance.
# The Effect of the Water Table on the Bearing Capacity of a Shallow Foundation
## 3 Influence of Immersion on the Bearing Capacity of Shallow Buried Foundation
### Influence Mechanism of Reservoir Immersion on Bearing Capacity of Shallow Buried Foundation  
After the completion of reservoir construction and water storage, the groundwater level in the foundation soil of buildings has been at a high level for a long time. Under specific geological and topographic conditions, the buildings in the reservoir area will be immersed, resulting in adverse effects. The rise of groundwater level has a particularly obvious impact on shallow foundation buildings, and the harm is relatively large.  
When the groundwater level is low, the water content of the building foundation soil is small, the effective weight of the soil body is large, and the cohesion and internal friction angle are large. Thus, the bearing capacity of the foundation is high, and the bearing capacity of the building foundation is high, and deformation stability can meet the requirements. From the research on the influence of different saturation degrees on soil mechanical properties in Section 2.2, it can be seen that under the same other conditions, with the increase of saturation, the compressive index of low liquid limit clay gradually increases, while the compressive modulus gradually decreases. At the same time, the cohesion and internal friction angle gradually decrease with the increase of saturation. When the groundwater level rises, the soil changes from an unsaturated state to a saturated state. For the soil immersion below the groundwater level, due to the loss of the apparent cohesion formed by weak binding force or capillary stress, the internal friction angle of the soil gradually decreases, the compressive index increases, and the compressive modulus gradually decreases. The soil gradually softens and the shear strength decreases, resulting in a decrease in the bearing capacity of the foundation and an increase in the deformation. At the same time, due to the existence of groundwater, the effective weight of the soil decreases, which leads to a decrease in the bearing capacity of the soil. The immersion of the reservoir affects the bearing capacity of the shallow foundation in two aspects.
## 3 Result and Analysis
### Damage Parameter Acquisition  
When the damage degree of the RPC structure is slight, the displacement and deflection of the damage source area are small. However, the location and elevation information of the damaged area is constantly changing as the degree of damage continues to develop. This change can be obtained in two ways. On the one hand, the continuous development of cracks in 2D plane can be achieved. On the other hand, the displacement of the crack and the change in height information in the 3D space.  
The 2D image can be captured by the CCD camera built in the 2D laser ranging sensor. According to the principle of the independently developed 3D laser scanning system, the point cloud data for obtaining the scanned test piece is a digital matrix composed of data points having position information.  
Figure 5: Data acquisition.  
Figure 6: Structure point cloud diagram.  
This value can be calculated by the Formula (2). Therefore, the damage region of the digital matrix composed of the 2D image and the point cloud elevation information is selected as the Region of Interest (ROI), respectively. The GLCM of the ROI is constructed separately, the feature parameters are extracted, and the texture features are calculated therefrom. The angles are determined as \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\), the image grayscale is selected as 265, and the distance between the pixel pairs is 2 pixels. Therefore, when the construction factor is d = 2, g = 2\({}^{8}\) and \(\theta\) selects \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\), and six characteristic parameters of the damaged area of the test piece are extracted. In order to satisfy the image rotation invariance, the average of the 4 angles is taken as the final value function parameter.
## 3 Results and Discussion
### Tensile Properties  
The mechanical properties of the Laves phase-strengthened model alloys were determined using small-scale tensile tests performed on sub-sized tensile test specimens, taken from TTS-deformed samples. The strengthening effect of thermomechanically induced precipitation manifests itself in the stress-strain diagram in Figure 9, compared to a particle-free state without deformation. Due to  
Figure 8: Chemical composition in wt. % of precipitated Laves phase particles in model alloys Fe17 Cr2.6W and Fe17 Cr3.6W in comparison to the simulated composition of the unstable LT and the stable HT Laves phase (ThermoCalc®, database TCFE8, nominal chemical composition).  
precipitation strengthening, solid solution strengthening, and strain hardening, a significant level of work hardening was obtained. The progress achieved corresponds to the presence of the homogeneous, finely dispersed Laves phase precipitation. The model alloy Fe17 Cr3.6W exhibited a yield strength of 434 MPa and an ultimate tensile strength of 712 MPa. Compared to the undeformed, particle-free state, a progress in strength of approximately 200 MPa can be observed. The alloy Fe17 Cr2.6W provides slightly lower values: the yield strength amounted to 412 MPa and the ultimate tensile strength to 683 MPa. The lower phase fraction of the Laves phase in the alloy Fe17 Cr2.6W explains the decrease of approximately 30 MPa in strength values.
## 2 The Signal of Faulty Rolling Bearing with Variable Speed: An Angle-Time Cyclostationary View
### The Calculation of the Order-Frequency Spectral Correlation  
The angle-time autocorrelation function (ATCF) \(R_{2x}(\tau,\theta)\) of \(x(t)\) can be computed by the Fourier series at cyclic orders \(i/\Theta\) as  
\[R_{2x}(\theta,\tau)=\mathbb{E}\{x(t(\theta))x^{*}(t(\theta)-\tau)\}=\sum_{i} R_{2x}^{i}(\tau)e^{i2\pi i\frac{\theta}{\Theta}}, \tag{3}\]  
where \(\mathbb{E}\{\cdot\}\) is the statistic means, \(\tau\) represents the time-lag, \((\cdot)^{*}\) denotes the complex conjugation, \(R_{2x}^{i}(\tau)\) is the angle-time cyclic correlation function and \(\theta(t)=\int_{0}^{t}\omega(t)dt\)stands for the angular variable with \(\omega\) the angular frequency. Equation (3) is a periodic function with a cyclic period \(\Theta\).  
Similar to the spectral correlation (SC), the bispectral counterpart contained in the order-frequency spectral correlation (OFSC) can be obtained by double Fourier transform of ATCF as  
\[\mathcal{S}_{2x}(\alpha,f)=\mathcal{F}_{\theta\rightarrow\alpha,\tau\to f}\{R_{2x} (\theta,\tau)\}, \tag{4}\]  
where \(R_{2x}(\theta,\tau)\) is the angle-time autocorrelation function, \(\mathcal{F}_{\theta\rightarrow\alpha}\) denotes the Fourier transform from the variable \(\theta\) to \(\alpha\), \(f\) is the spectral frequency and \(\alpha\) is the cyclic order. The enhanced envelope order spectrum (EEOS) is also introduced to further highlight the faulty characteristics, and the EEOS is defined as  
\[\mathbf{S}_{x}^{\text{EEOS}}(\alpha)=\frac{1}{f_{2}-f_{1}}\int_{f_{1}}^{f_{2}} \lvert\mathbf{S}_{x}(\alpha,f)\rvert df, \tag{5}\]  
where \(f_{1}\) and \(f_{2}\) are the upper and lower limits of the given spectral frequency band.