esa-sceva/satcom-chunk-collection huggingface dataset:
# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
###### Abstract  
Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefEf, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the Relief-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.  
lithological mapping; Sentinel-2; feature selection model; machine learning; textural analysis +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.12. Stringing or Oozing  
Stringing or oozing occurs when marginal lines of plastic are left behind or in between the objects while the nozzle moves to another point due to incorrect retraction settings, a loose belt, printing temperature set too high, and/or poor quality filament being used. Solutions include the following:  
1. Running a basic retraction test to adjust the optimum retraction value [75]. If retraction is enabled, the filament will be pulled back into the nozzle to act as a countermeasure against oozing. The optimum speed is 6000 mm/min (20-100 mm/s) [76].
2. Ensure that all tension belts are tightened.
3. Lower the printing temperature by 5 to 10 \({}^{\circ}\)C.
4. Post-process the parts to remove strings by using a heat gun. For PLA and ABS parts, set the heat gun at 200 \({}^{\circ}\)C to melt the strings and the printed object should remain undamaged (refer to 3.3.19: Hairy prints).
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 5 ROS Robot Experiment
### Multi-Robot Formation Experiment
#### 5.1.1 Square Formation Experiment  
When the density function was set to a constant, the map was evenly divided into four generation points, as shown in Figure 17a. Figure 17c shows the initial positions of the robots. When the number of iterations \(k=200\), the CVT algorithm converges, as shown in Figure 17b. At this time, the cost function reaches the minimum value, and the robot completes the square formation on the map, as shown in Figure 17d.
## 3 Methodology
### Artificial Neural Network (ANN)  
ANN is a data-driven technique widely applied in time series forecasting and anomaly detection [56]. ANN is well known for its capability to deal with non-stationary data. Among many different kinds of neural networks, the multilayer perceptron (MLP) received the most attention due to its simplicity and capability to solve complex nonlinear problems [57]. As such, MLP is used in this paper to detect the change point of lake surface area and predict the future behavior of lake surface area.  
The MLP is structured with a particular topology consisting of connected neurons and layers. Generic architectures of MLP networks are depicted in Figure 3. The input values are transmitted through links and neurons to the output values. All links between each neuron have an associated weight to scale the value traveling on that link [58]. During the model training process, the weight of each connection is adjusted iteratively to minimize a \"cost function\" such as the mean squared error (MSE). Once the topology of the MLP is designated, the model could learn the data behavior automatically and generate a mapping function to represent the mathematical relationship between inputs and outputs as:  
\[Y_{j}=f\left(\theta_{j}+\sum_{i=1}^{n}w_{ji}X_{i}\right) \tag{12}\]  
where \(X_{i}\) = the ith input variable; \(Y_{j}\) = the jth output variable; \(\theta_{j}\) = the bias in the hidden layer; \(n\) is the number of neurons in the hidden layer; \(w_{ji}\) = the connection weight; and \(f\) = the transfer function between layers.  
The MLP model used in this paper is trained with backpropagation. The model is built using the Keras package in Tensorflow, all of which are coded in Python. Each layer is densely connected to the adjacent layers. The hyperparameters of the MLP model (e.g., the number of neurons or the number of layers, the learning rate) are determined based on the grid search strategy, and a detailed description of the parameter settings is summarized in Table 1. As shown in Figure 3, two MLP models are developed in this study. The first model (Figure 3a) is used to map the relationship between the extraneous factors (e.g., temperature, streamflow, rainfall) and the lake's surface area. The second model (Figure 3b) is used to generate a multi-step forecasting model based on the historical data of lake surface area to predict its future behavior. It should be noted that the dataset has been divided into two mutually exclusive sets, where 60% has been used as the training set and the remaining 40% has been used to test the trained model.  
Figure 3: Overview of the topology of the two proposed MLP models. (**a**) is MLP model using extraneous factors and (**b**) MLP model using historical date of lake surface area.
###### Abstract  
In this study, the Visible Infrared Imaging Radiometer Suite (VIIRS) Enhanced Vegetation Index (EVI) was spectrally cross-calibrated with the Moderate Resolution Imaging Spectroradiometer (MODIS) EVI using a year-long, global VIIRS-MODIS dataset at the climate modeling grid (CMG) resolution of 0.05\({}^{\circ}\)-by-0.05\({}^{\circ}\). Our cross-calibration approach was to utilize a MODIS-compatible VIIRS EVI equation derived in a previous study [[PERSON] _et al._, _J. Appl. Remote Sens._, vol.7, 2013] and optimize the coefficients contained in this EVI equation for global conditions. The calibrated/optimized MODIS-compatible VIIRS EVI was evaluated using another global VIIRS-MODIS CMG dataset of which acquisition dates did not overlap with those used in the calibration. The calibrated VIIRS EVI showed much higher compatibility with the MODIS EVI than the original VIIRS EVI, where the mean error (MODIS minus VIIRS) and the root mean square error decreased from \(-\)0.021 to \(-\)0.003 EVI units and from 0.029 to 0.020 EVI units, respectively. Error reductions on the calibrated VIIRS EVI were observed across nearly all view zenith and relative azimuth angle ranges, EVI dynamic range, and land cover types. The performance of the MODIS-compatible VIIRS EVI calibration appeared limited for high EVI values (_i.e._, EVI \(>\) 0.5) due likely to the maturity of the VIIRS dataset used in calibration/optimization. The cross-calibration methodology introduced in this study is expected to be useful for other spectral indices such as the normalized difference vegetation index and two-band EVI.  
Keywords:EVI; VIIRS; MODIS; spectral compatibility; cross-calibration; CMG data +
Footnote †: journal: Remote Sensing
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for PRESENT Block Cipher
#### 3.1.1 AddRoundkey of PRESENT Block Cipher  
In the AddRoundkey operation, the leftmost 64 qubits of master key are used as the round key. The round key is exclusive-ored with the 64-qubit block (\(B(b_{63},...,b_{0})\)), which becomes the ciphertext. The XOR operation can be performed using the CNOT gate. At this time, the qubit whose result value changes should be \(B\). The implementation of PRESENT AddRoundKey quantum circuit is shown in Algorithm 1.  
Figure 8: Grover key search quantum circuit for block cipher.  
Figure 7: State after diffusion operator.  
```
0: 64-qubit block \(B(b_{63},...,b_{0})\), 64-qubit round key \(RK(rk_{63},...,rk_{0})\)
0: 64-qubit block \(B(b_{63},...b_{0})\) after AddRoundKey
1:for\(i=0\) to 63 do
2:\(b_{i}\leftarrow\) CNOT \((rk_{i},b_{i})\)
3:endfor
4:return\(B(b_{63},...,b_{0})\)
```  
**Algorithm 1** Quantum circuit for AddRoundKey of PRESENT block cipher
## 2 Physical Experiments and Results
### Soil Erosion Area  
Increased detachability produced by surface runoff shear forces and the impact of raindrops is considered as the main force driving erosion [14]. Figure 4 illustrates that the toe of the slope is more eroded than other positions, in consideration of more ground water supply from the upper parts. The erosion of the top part of the slope without ground water supply from other parts shows a slow increase. Thus, this is typical progressive soil erosion. Figure 5 shows the erosion of different materials under the same time domain and rainfall intensity. It is found that the coarse particles (such as soil Sample 3) have lower erosion than fine particles (such as soil Sample 1) because of their higher permeability and stronger framework.  
Figure 3: Area calculation by AutoCAD (soil Sample 1 and rainfall intensity-65 mm/h): (**a**) establishment of coordinate; (**b**) extraction of contour; and (**c**) calculation of areas.  
In Figure 6, soil Sample 1 (25 mm/h) has the most obvious initial phase (threshold of soil erosion action) because of the low rainfall intensity. By comparison, soil Sample 1 (45 mm/h) and soil Sample 1 (65 mm/h) have fewer time lags. In the second acceleration stage, the erosion under higher rainfall intensity obviously occurs faster because of the greater surface runoff production. In the last phase, for one thing the coarse particles remain and the loss of fine particles makes the soil structure stronger; for another, the water erosion develops towards the deep direction. Therefore, the soil erosion area has a maximum value, but the volume could still increase. By contrast, for different materials, soil Sample 1 (more fine particles), with the weakest structure, has the earliest start point of the second erosion phase and a high erosion rate during the second stage because more surface fine particles are lost. Until the final stage (relatively stable structure), the erosion rate is still higher than for the coarse materials, but only towards the deep direction. For the area of soil erosion in the final stage, the values of the three materials can be close, while for the erosion volume or weight the coarse material has a lower value. This is because coarse particles like soil Sample 3 have a stronger structure (high friction) and higher infiltration rate (producing less surface runoff) than fine particles like soil  
Figure 4: Different time domains in Sample 1 and rainfall intensity-65 mm/h test: (**a**) \(t=6\) min; (**b**) \(t=19\) min; (**c**) \(t=45\) min; and (**d**) \(t=70\) min.  
Figure 5: Different materials under the same time domain (45 mm/h; \(t=52\) min): (**a**) soil Sample 1 and erosion area-771.21 mm\({}^{2}\); (**b**) soil Sample 2 and erosion area-421.31 mm\({}^{2}\); (**c**) soil Sample 3 and erosion area-359.46 mm\({}^{2}\).  
Sample 1. In addition, it can also be seen that the stronger the structure of coarse material against soil erosion is, the greater is the time lag of the beginning and accomplishment of the erosion area compared to fine material. In summary, the soil erosion is decided by both rainfall and the material's strength.
## 2 Related Work  
### Traffic Flow Prediction  
Traffic flow prediction offers accurate projections of traffic volume within a specified area at future time intervals. The exploration of traffic forecasting proves valuable in alleviating congestion and promoting safer, more cost-effective travel [11]. The number of studies on traffic flow prediction has increased significantly during recent years due to a rising awareness of the environmental and social impacts of the increasing traffic congestion in most cities and the development of big data and deep learning methodologies. There are different approaches to predict traffic flow.  
For instance, recently, ref. [12] reviewed and compared hybrid deep learning models. The authors recognized the escalating complexity of models, which now incorporate a growing array of finer-grained, multi-type data sourced from transportation systems. Most of the approaches reviewed use convolutional neural networks, recurrent neural networks, and long- and short-term memory units. Ref. [13] provided a quick review of machine learning algorithms for short-term traffic forecasting and introduced the challenges, followed by various ways for modeling temporal and/or geographical dependencies. Also, ref. [14] integrated a bootstrap methodology with the conventional parametric ARIMA model to form an ensemble approach. This ensemble, derived from random subsamples of data, aims to enhance prediction accuracy while maintaining adherence to theoretical principles.  
The traffic flow data are classified as similar, volatile, and irregular parts. Based on the autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model, ref. [15] created a methodology to forecast the similar and volatile portions. Also, the study compared the strengths and weaknesses of linear and non-linear hybrid methods.  
Ref. [16] harnessed cutting-edge object detection and tracking technologies to pinpoint, categorize, monitor, and gauge vehicles on the road via video analysis. This comprehensive approach offers sturdy backing for urban traffic management strategies and future planning. Leveraging digital twin technology, they created a virtual replica of traffic patterns using camera data, forming the foundation for training various algorithmic models.  
Figure 1: Diagram of research flow.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Guidelines and Recommendations  
In this section, we provide comprehensive guidelines and actionable recommendations to enhance the seismic resilience of power stations, addressing the specific needs of engineers and designers involved in the construction and retrofitting of critical infrastructure:  
**Seismic risk assessment:**  
* Conduct detailed seismic risk assessments using updated geological data and advanced modeling techniques to identify potential seismic hazards specific to the location of the power station. This assessment includes evaluating fault lines, soil composition, and historical earthquake data.
**Design recommendations:**  
* Design critical components with enhanced seismic standards that exceed the minimum code requirements to ensure robustness against higher magnitude earthquakes.
**Anchoring and retrofitting:**  
* Implement anchoring solutions for all critical machinery and equipment to withstand the region's anticipated seismic forces.
* Use high-quality, durable materials for anchoring to improve longevity and effectiveness.
* For existing structures, conduct thorough inspections to identify vulnerabilities and retrofit them with modern anchoring techniques to improve their seismic resilience.
**Isolation systems:**  
* Employ advanced base isolation systems and vibration control technologies to decouple the structure from the ground motion, significantly reducing the impact of seismic waves.
**Monitoring and maintenance:**  
* Install state-of-the-art seismic monitoring systems to provide real-time data on structural integrity during and after earthquakes.
* Routine maintenance schedules to inspect and maintain seismic mitigation equipment, ensuring all components function correctly and adhere to safety standards.
**Emergency response planning:**  
* Develop comprehensive emergency response plans that include evacuation routes, communication protocols, and recovery strategies to minimize downtime and ensure safety.
* Train staff regularly on these plans to ensure everyone knows their role during and after an earthquake.
**Collaboration with Experts:**  
* Engage with seismic experts, structural engineers, and researchers to stay updated on the latest advances in earthquake engineering and integrate these innovations into the design and maintenance of power stations.  
**Regulatory compliance:**  
* Ensure all designs and modifications meet or exceed local and international building codes and standards related to seismic safety.
* Work with regulatory bodies to update and improve seismic standards based on the latest research and post-earthquake assessments.
# Rate of Penetration Estimation with Parameter Correction
## 5 Case Study
### Case 1: MD Correction
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **Symbol** & **Value** & **Unit** \\ \hline Discrete mass & \(M\) & \(3.4\times 10^{3}\) & kg \\ \hline Discrete rotary inertia & I & 116 & kg m\({}^{2}\) \\ \hline Axial stiffness & \(K_{a}\) & \(7\times 10^{5}\) & N/m \\ \hline Torsion stiffness & \(K_{t}\) & 938 & Nm/rad \\ \hline Axial damping & \(C_{a}\) & \(1.56\times 10^{4}\) & N/m \\ \hline Torsion damping & \(C_{t}\) & 32.9 & Nsm/rad \\ \hline Specific energy of rock & \(\epsilon\) & 0–110 & Mpa \\ \hline Contact strength & \(sigma\) & 60 & Mpa \\ \hline Radius of drill bit & \(a\) & 108 & mm \\ \hline Wearflat length of drill bit & \(\ell\) & 1.2 & mm \\ \hline Cutter face inclination & \(\zeta\) & 0.6 & - \\ \hline Frictional coefficient for rock–bit interaction & \(\mu\) & 0.6 & - \\ \hline Geometry parameter of drill bit & \(\gamma\) & 1 & - \\ \hline Number of blades of drill bit & \(n\) & 4 & - \\ \hline Ratio of axial to torsion natural frequencies & \(\eta\) & 1.6 & - \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters for drill string dynamic model. The correction factor (i.e., the difference between the corrected length and the nominal length) shows how much elongation needs to be accounted for at different points in time. The simulation demonstrates that without proper correction, the measured depth can be overestimated by several meters depending on the external conditions. This overestimation can lead to incorrect assumptions about the bit's position in the wellbore, affecting decisions related to drilling parameters. The plot shows that static elongation remains relatively constant over time. This is expected, as static forces like thermal expansion and pressure-induced ballooning do not fluctuate significantly within the short time frame of the simulation. The dynamic elongation correction varies periodically, reflecting the transient nature of the dynamic forces. These variations may become more pronounced during certain operational phases, such as when the drill bit encounters harder formations or when rotational and axial velocities fluctuate. The static elongation is much larger than the dynamic elongation in this simulation. Dynamic elongation contributes periodically to the total elongation, but its magnitude is smaller. However, in more extreme dynamic conditions (e.g., highly vibrating drill strings), dynamic elongation could become a more significant factor, especially if resonances occur in the system. From a practical perspective, these elongation corrections are crucial for accurate depth measurement. Without them, the MD could be overestimated, leading to incorrect assumptions about the drill bit's progress. This inaccuracy can affect the optimization of drilling parameters such as WOB, rotational speed, and mud flow rates.
## 3 Results
### Mangiferin Down-Regulated the Expression of Genes Involved in Biofilm Formation  
To study the effect of mangiferin on biofilm-related gene expression, we performed quantitative real time PCR to quantify the effect on the biofilms formed by _S. mutans_ using test concentrations of 500 \(\upmu\)m and 1000 \(\upmu\)m. Among the genes examined, the expression of _gtfB_ involved in water-soluble extracellular polysaccharide synthesis was found to be down regulated by the treatment with mangiferin (500 and 1000 \(\upmu\)m). Similarly, the expression of genes involved in water-soluble and insoluble glucan production (_gtfC_) was also down regulated by mangiferin in dose dependent manner. In addition, the expression of _gtfD_, involved in synthesis of water-soluble glucan was down regulated by mangiferin treatment. In addition, mangiferin also down regulated the expression of _gbpB_ gene encoding glucan binding protein. The gene encoding _ComDE_, involved in an intraspecific cell-cell communication quorum sensing system in _S. mutans_, was also down regulated by mangiferin (Figure 6).  
Figure 4: Effect of mangiferin on _S. mutans_ biofilm in teeth analyzed as viable colony forming units. Data are means \(\pm\) standard deviations (SD) (n = 3). * \(p<0.05\) represents statistical significant difference from the control group.