esa-sceva/satcom-chunk-collection huggingface dataset:
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 6 Conclusions  
This paper investigates the deployment and optimization of HATBBSs for emergency communications. Compared to existing research, this study especially considers the impact of the complex mobility of base stations on network performance. Based on the proposed system model and constraints, the deployment problem is transformed into a multi-objective optimization problem for maximizing coverage and energy conversion, and MOPSO is used to address the problem. By comparing and analyzing with SA, the simulation results confirm that the proposed method shows significant advantages in several key performance indicators: user coverage, system energy efficiency, and total throughput are effectively improved, while average delay and packet loss rate are reduced. Thus, MOPSO is verified to be efficient and practical in emergency communication scenarios, offering valuable insights for future deployments of aerial communication networks in disaster response situations.  
Conceptualization, M.G., Z.F., S.J., and W.Z.; investigation, M.G. and Z.F.; methodology, M.G. and S.J.; software, M.G. and Z.F.; validation, M.G.; writing--original draft, M.G.; writing--review and editing, M.G. and S.J.; supervision, S.J.; project administration, S.J.; and funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Innovation Program of Shanghai Municipal Education Commission of China under Grant No. 2021-01-07-00-10-E00121.  
**Institutional Review Board Statement:** Not applicable.
## 3 The Algorithm
### New Architecture  
Before the implementation, further simplifications should be done in order to reduce the complexity, increase bandwidth, but, at the same time, without affecting results and performance. Taking into account the symmetry of the problem, as it is shown in Figure 3a, the number of NNs can be reduced to only two frequency-domain models in the QPSK cases, i.e., \(NNF_{RE,1}\), \(NNF_{Im,2}\).  
For this purpose, new blocks \"Quadrant Adaptation\" and \"Quadrant Recovery\" are needed, at the transmitter and the receiver, respectively, for constellation quadrants adaptation to/from the operating frequency-domain models. The architecture is shown in Figure 5. This new architecture will save space and energy. In addition, in DCM cases, for the same reason (Figure 3b), the number of neural networks can be reduced from 24 to 6. In fact, two models \(NNF_{RE,1}\), \(NNF_{Im,2}\) can be used for regions 1, 4, 7 and 10. Two other models \(NNF_{RE,3}\), \(NNF_{Im,3}\) for regions 2, 3, 8 and 9. In addition, in regions 5, 6, 11 and 12, two models \(NNF_{RE,5}\), \(NNF_{Im,5}\) are also used. Finally, regions 13, 14, 15 and 16 do not undergo any processes since interior points cannot be moved [10]. Figure 6 shows the new architecture for DCM modulation.  
Figure 4: Time-domain and frequency-domain Neural Network training.  
Figure 5: New time-domain and frequency-domain NN for QPSK.  
Each NN constituting the architectures proposed in Figures 5 and 6 is in three layers:  
1. An input layer: acquires the input signal of the system.
2. A hidden layer: contains two neurons adopting triangular function activation.
3. An output layer: contains a single neuron with a linear activation function.  
The designed NN is shown in Figure 7.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 5 Simulation Results
### Predicting Compensation of ASPF  
The data of original solar power is from 1 January to 31 December 2016 in Hekou town. For the _k-means_ algorithm, the initial cluster centers calculated by the potential function need less iterations compared with traditional stochastic way, as shown in Figure 8a. As explained in Section 3.1, the range of \(k\) is from 2 to 19. In addition, the initial cluster centers can be found fast and better to determine the optimal number of clusters, and effectively avoid the occurrence of non-clustering situation. Equations (4)-(6) are used to determine the optimal number of clusters. Based on the methods described in Section 3.1, the number of clusters for different \(k\) under NN and MLR are shown in Figure 8b, where we can see that the optimal number of clusters is \(k^{*}=2\) in NN, and \(k^{*}=3\) in MLR.  
In order to modify the differences in original method for forecasting day-ahead solar power, LARS is used to analyze the key factors which affect the forecasting most in NN and MLR. In addition, the critical parameters are different in NN and MLR, shown in Tables 2 and 3. The forecasting results of NN and MLR for different groups are summarized in Tables 4 and 5, which show that our proposed method has greatly improved the accuracy of PV prediction no matter the PV power is predicted by machine learning or MLR.
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 3 Research on Semi-Active Pipeline Vibration Control System
### Comparative Analysis of Simulation of Different Vibration Control Algorithm
#### 3.3.2 PID Vibration Control Simulation  
The PID control has the advantages of simple control principle, low precision requirement for the controlled object model and overcoming the non-linearity of the controlled object. The basic  
Figure 7: Simulation of two-state pipeline vibration control system with dead zone: (**a**) Two-state simulation model; (**b**) Vibration displacement response; (**c**) Control force and disturbance dynamics curve.  
principle of PID control is that the controller generates the control force \(u(t)\) to control the controlled object according to the control error \(e(t)\), so that the output \(y(t)\) of the controlled object meets the requirements. When using the PID control, the control force \(u(t)\) can be expressed as:  
\[u(t)=k_{p}e(t)+k_{i}\int e(t)dt+k_{d}\frac{de(t)}{dt} \tag{21}\]  
where \(k_{p},k_{i}\) and \(k_{d}\) are the proportional gain, the integral gain and the differential gain.  
Since the MR damper can only output the control force opposite to the structural motion direction, when the vibration control reference control force is opposite to the structural motion direction, the PID control algorithm is used to control the output control force of the MR damper. When the reference control force is the same as the structural motion direction, the MR damper is controlled to output a minimum control force. The PID drive control law of the MR damper is as follows:  
\[\mathbf{U}=\begin{cases}\mathbf{u}_{pid}&u_{r}\dot{\mathbf{x}}<0\\ \mathbf{F}_{\min}&u_{r}\dot{\mathbf{x}}\geq 0\end{cases} \tag{22}\]  
where \(\mathbf{U}\) is the damping force of MR damper, \(u_{r}\) is the reference control force, \(u_{pid}\) is the control force generated by PID control algorithm, \(\dot{\mathbf{x}}\) is the relative velocity of piston and cylinder.  
The Simulink simulation model of the PID pipeline vibration control system based on MR damper is shown in Figure 8a. When the sinusoidal excitation amplitude is 2 mm and the frequency is 5 Hz, the vibration displacement response and control force simulation results of the pipeline under the PID vibration control are shown in Figure 8b,c, respectively.  
Figure 8: Simulation of PID pipeline vibration control system: (**a**) PID simulation model; (**b**) Vibration displacement response; (**c**) Control force and disturbance dynamics curve.  
As shown in Figure 8, the PID vibration control strategy can effectively control the pipeline vibration. In the transient phase of vibration response of pipeline system, the vibration of pipeline can be well controlled and no amplitude overshoot occur. The attenuation amplitude of the pipeline vibration adopting the PID vibration control in the steady state phase is 66.73%.
## 3 Image-Servo Control and Pattern Recognition of the Positioning Mark  
The cross marks are usually used as the alignment fiducial mark in past researches and the cross marks were used as the fiducial marks to perform the alignment tasks in our previous paper [5].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Alignment error and time**} & \begin{tabular}{c} **Linear** \\ **Approximated** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **ANN-Based** \\ **Motion** \\ **Planning** \\ \end{tabular} & \
\begin{tabular}{c} **Linear Approximated** \\ **Method with the** \\ **Second Alignment** \\ \end{tabular} &
\begin{tabular}{c} **ANN-Based Motion** \\ **Planning with the** \\ **Second Alignment** \\ \end{tabular} \\ \hline Rotational RMSE & 0.007 (\({}^{\circ}\)) & 0.007 (\({}^{\circ}\)) & 0.006 (\({}^{\circ}\)) & 0.005 (\({}^{\circ}\)) \\ Positioning RMSE & 29.4 (\(\upmu\)m) & 19.9 (\(\upmu\)m) & 11.0 (\(\upmu\)m) & 6.8 (\(\upmu\)m) \\ Maximum of rotational error & 0.0172 (\({}^{\circ}\)) & 0.016\({}^{\circ}\) (\({}^{\circ}\)) & 0.019 (\({}^{\circ}\)) & 0.013 (\({}^{\circ}\)) \\ Maximum of positioning error & 66.1 (\(\upmu\)m) & 45.6 (\(\upmu\)m) & 33.6 (\(\upmu\)m) & 17.0 (\(\upmu\)m) \\ Average of the first alignment time & 3.1860 (s) & 3.4308 (s) & 4.0519 (s) & 4.1211 (s) \\ Average of the second alignment time & none & none & 0.7188 (s) & 0.7347 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the linear method and the proposed method.  
Figure 5: (**a**) Positioning error using the linear method; (**b**) Positioning error using the ANN-based motion planning.  
Figure 6: (**a**) Rotational error using the linear method; (**b**) Rotational error using the ANN-based motion planning.  
However, the real-time implementation is difficult as the computation burden and the processing time for image processing is too much to meet real-time demands. To improve the real-time performance of the image-servo system, a novel image-servo method is proposed in this paper. Two types of fiducial marks are used in this study, where the radius of the upper mark is about 0.545 mm and the radius of the lower mark is about 0.144 mm. The images of the upper and lower marks captured by the CCDs separately are shown in Figure 7a,b.
## 2 Theoretical Background
### Bearing Faults and Lubrication
#### 2.1.2 Bearing Grease Lubrication  
Grease is a common lubrication method used in rolling element bearings. Its primary function is to prevent contact between the moving parts of the bearing, which is achieved by the liberation of a small amount of oil contained in the grease that will adhere to the surfaces; the oil degrades gradually by oxidation and is lost because of evaporation, centrifugal forces, and other phenomena [27].  
Grease lubrication has advantages when compared to liquid lubricants, as it reduces wear and friction, helps to dissipate heat from friction, prevents rusting, and provides some sealing between particles and the bearing as well as reduced maintenance as the proper quantity of lubricant is confined in the housing and no oil level needs to be maintained [28].  
This method is generally used for low-speed operations and reubrication depends mainly on the specifications from the manufacturer, as well as bearing and grease type, size, speed, operating temperature, and ambient conditions. Also, when changing the grease, the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic Frequency** & **Equation** \\ \hline Inner race defect frequency & \(\frac{n}{2}\frac{N}{60}\left(1-\frac{d}{D}Cos\alpha\right)\) & (1) \\ Outer race defect frequency & \(\frac{n}{2}\frac{N}{60}\left(1+\frac{d}{D}Cos\alpha\right)\) & (2) \\ Ball defect frequency & \(\frac{D}{20}\left[1-\left(\frac{d}{D}Cos\alpha\right)\right]\) & (3) \\ Cage defect frequency & \(\frac{1}{2}\frac{N}{60}\left(1-\frac{d}{D}Cos\alpha\right)\) & (4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic frequencies for the identification of bearing defects using vibration signals.  
operator must apply the correct amount to prevent corrosion and seal the bearing correctly to prevent contamination [27].  
Bearing failure due to lubrication is classified as inadequate lubrication, including the incorrect selection or amount of lubricant, the incorrect interval for lubrication, and contamination, caused mostly by seal failure and improper handling [5]. Grease condition is challenging to monitor, as its distribution and the movement of the parts degrade the grease unequally, making it complex to use online techniques, with vibration and AE signals being the most used, and meaning laboratory tests with samples from the grease are more accurate [29]. Vibration and AE are primarily analyzed with statistical indicators in the time domain and characteristic frequencies in the frequency domain as the particles interact with the surface of the elements inside the bearing, creating changes in contact pressure that generate vibration and noise.
## 1 Introduction
### Contributions of This Study  
Despite significant advancements in deep learning for drilling anomaly detection, previous studies have faced limitations such as dataset imbalance, lack of interpretability, and insufficient real-time adaptability. This study addresses these challenges by introducing a robust deep learning autoencoder-based approach for detecting stuck pipe incidents with improved accuracy and real-time feasibility. The contributions of the study can be summarized within the following points:  
* Implementation of an unsupervised parameter-sensitive deep learning autoencoder trained exclusively on normal drilling conditions to enhance anomaly detection accuracy and reduce false positives and validated against traditional AI methods.
* Development of a reconstruction error-based detection threshold that ensures improved sensitivity to subtle variations in the drilling parameters.
* Comprehensive evaluation using real-world drilling data from the Volve field to demonstrate the model's effectiveness in predicting stuck pipe events with minimal computational overhead.  
The remainder of this paper is structured as follows: Section 2 details the dataset used and the processing steps. Section 3 presents the proposed deep learning autoencoder methodology utilizing LSTM. Section 4 discusses the results of the classification. Finally, Section 5 concludes the study with findings, limitations, and future research directions.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 2 Increasing the Surface Area of Graphene
### Nanostructures on Graphene Surfaces  
The introduction of nanostructures on graphene surfaces entails the deposition, growth, or attachment of nanoscale materials, such as nanowires, nanotubes, or nanoparticles, onto the graphene substrate. This process leads to a substantial increase in its effective surface area, with profound implications for various applications. First and foremost, the deposition of nanoscale materials onto its surfaces introduces a three-dimensional aspect to the otherwise two-dimensional lattice [45]. The nanostructures create an intricate topography, producing a wealth of additional surface features, including protrusions, pits, and facets. This increased surface complexity enhances the surface area available for interactions, making it particularly well-suited for adsorption, catalysis, and sensing applications. The presence of nanostructures on the graphene surface augments the density of active sites and edge sites. Nanostructures often have high surface-to-volume ratios, which means that the active surface area per unit volume is significantly increased [46]. As a result, there are significantly more active sites available for chemical reactions or adsorption processes. The intricate spatial arrangement of nanostructures also introduces a level of roughness and porosity to the surface [47]. These surface irregularities offer additional opportunities for molecular adsorption, making them well-suited for applications such as gas storage, sensing, and catalysis. In addition to providing extra surface area for molecular interactions, nanostructures can offer specific functionalities due to the unique properties of the nanostructures themselves [48]. For example, CNTs can be highly conductive, while metal or metal oxide nanoparticles can catalyze reactions. These functional nanostructures not only increase the surface area but also contribute to the overall performance of the G-b materials in applications like electrocatalysis or electrochemical sensing [49]. Nanostructures on graphene are particularly advantageous in fields like energy storage and conversion. The integration of metal nanoparticles into it can improve the electrocatalytic activity of fuel cells, while the attachment of semiconductor nanostructures can enhance the performance of photovoltaic devices [46]. The incorporation of nanostructures onto its surfaces results in a substantial increase in the effective surface area due to the added topographical complexity, higher mass in a unit volume of active locations, and the introduction of surface irregularities.
# Three-Dimensional Geological Modeling Method Based on Potential Vector Fields
## 3 Results
### Validation of Model Accuracy  
To evaluate the quality of the constructed 3D geological model, this study employs the leave-one-out cross-validation (LOOCV) method. Specifically, 99 out of the total  
Figure 12: Local geological interfaces at different grid sizes: (**a**) 90 \(\mathrm{m}\times 90\,\mathrm{m}\times 3\,\mathrm{m}\); (**b**) 180 \(\mathrm{m}\times 180\,\mathrm{m}\)\(\times\) 6 \(\mathrm{m}\); (**c**) 270 \(\mathrm{m}\times 270\,\mathrm{m}\times 9\,\mathrm{m}\).  
100 borehole datasets are used as the training set to construct the 3D geological model, while the remaining borehole is used as the test set. The model then predicts the depth values of stratigraphic interfaces at the test borehole location. Finally, the predicted values are compared with the true values obtained directly from the original data using quantitative error assessment metrics. To minimize randomness, all borehole data are included in the validation process. Three quantitative metrics, namely mean absolute error (MAE), root mean square error (RMSE), and Lin's concordance correlation coefficient (CCC), are employed for evaluation, with the results being presented in Table 1. The results indicate that the model achieves an accuracy level suitable for practical applications. Moreover, the modeling accuracy for relatively flat stratigraphic interfaces is significantly higher than that for inclined interfaces. This discrepancy arises due to the limited availability of borehole data in regions with complex topography and specific drilling constraints, which negatively impacts the accuracy of certain stratigraphic interface models. To enhance the quality of the 3D geological model, future work may consider integrating multi-source data to impose additional constraints, thereby improving modeling accuracy.
## 2 Background and Preliminaries
### Translation-Invariant Signal Decomposition Using the Derived Wavelet Frames
#### 2.1.2 Implicit Wavelet Packets and the Frequency-Scale Paving of Derived Wavelet Frames  
The frequency response curves of the dyadic wavelets overlap each other at the boundary, so that the performance of extracting incipient vibration signatures located in transition bands is not perfect. In order to improve its performance supplementary, implicit wavelet packets were constructed based on DCWPD. The derivation process of the implicit wavelet packets (IWPs) mainly includes the following steps, wherein \(\left\{x(n)\right\}\) denotes the input signal.  
Figure 2: Hybrid wavelet bases of dual tree complex wavelet packet decomposition. (**a**) Scaling function of Symlet10; (**b**) wavelet function of Symlet10; (**c**) complex scaling function of Q-Shift20 and (**d**) complex wavelet function of Q-Shift20.  
Step (1): Decompose the original signal into a set of sub-signals \(D_{k}\) at multiple scales based on the dual tree wavelet packet decomposition: \(D_{k}=\left\{D_{k}^{q}(n)\right|q=1,2,\ldots,2^{k}\right\}\).  
Step (2): Reorganize set \(D_{k}\) according to the order of central frequency, and generate a new sequence of sub signal \(R_{k}=\left\{R_{k}^{q}(n)\right|q=1,2,\ldots,2^{k}\right\}\):  
The conversion relationship of the sequence number of a sub-signal in these two sets is:  
For \(R_{k}^{q}\), let the binary code of the index \(q\) be:  
\[q=\sum\
olimits_{m=0}^{k-1}2^{m}n_{m}+1. \tag{7}\]  
A new integer \(q^{\prime}\) is expressed as:  
\[q^{\prime}=\sum\
olimits_{m=0}^{k-1}2^{m}n_{m}^{\prime}+1, \tag{8}\]  
where \(n_{m}^{\prime}\) is defined as:  
\[n_{m}^{\prime}=\left\{\begin{array}{ll}n_{m},&m=k-1\\ \mathrm{mod}(n_{m}+n_{m+1},2),&m=0,1,\ldots,k-2\end{array}\right.. \tag{9}\]  
Step (3): Generate the implicit wavelet packet:  
\[iwp_{k}^{q}(n)=R_{k}^{2q}(n)+R_{k}^{2q+1}(n),\;1\leq 2^{k-1}-1. \tag{10}\]  
The frequency-scale paving of implicit wavelet packets are represented by the block identified as \(iwp_{k}^{*}\), in Figure 3. It can be seen that implicit wavelet packets realize multiresolution analysis around fixed central frequencies. Mathematical definitions of such sets can be expressed as:  
\[\begin{array}{l}IWPS_{k,j}=\left\{iwp_{k+k^{\prime}-1}^{(2q-1)\times 2^{k^{ \prime}-1}}\right|k^{\prime}\in\mathbb{Z},\;k^{\prime}\geq 1\right\},\\ (k,q\in\mathbb{Z},\;k\geq 1,\;1\leq q\leq 2^{k-1})\end{array}. \tag{11}\]  
Figure 3: Frequency-scale paving of derived wavelet frames (DWFs).
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions
### The Contribution of Each Component in the HC-MMK Approach  
#### 5.2.1 The Multi-Source and Multi-Temporal Data  
In this paper, the native HJ-CCD images and Landsat TM images have been combined to provide more high-quality satellite images (Figure 3) for generating the CLC-SW2010 product. This combination can maximize the advantages of the native HJ-CCD images (wide-covered and short repeat cycle) and the Landsat TM images (pleentiful spectral information and excellent quality). The multi-temporal satellite images were useful for distinguishing the evergreen vegetation and the deciduous vegetation, because they have differences in changes of spectral signature between leaf-on season and leaf-off season [19]. Meanwhile, the temporal signatures were used for identifying the dry lands, paddy fields and other land cover classes, and their spectral signatures frequently varied with season in this study.  
\begin{table}
\begin{tabular}{c c c|c c c|c c c|c c} \hline \hline \multirow{2}{*}{**Code**} & **Area** & **Number of** & **Producer’s** & **User’s** & \multirow{2}{*}{**Code**} & **Area** & **Number of** & **Producer’s** & **User’s** \\  & **Ratio** & **Samples** & **Accuracy** & **Accuracy** & & **Ratio** & **Samples** & **Accuracy** & **Accuracy** \\ \hline
101 & 5.30\% & 347 & 83.29\% & 88.38\% & 35 & 0.14\% & 142 & 95.77\% & 95.77\% \\
102 & 0.80\% & 127 & 79.53\% & 82.11\% & 36 & 0.38\% & 195 & 95.90\% & 95.90\% \\
1  
Figure 12: The comparison of classification results among four land cover products in four typical regions. The regions of (**a–d**) are located in Southeast Tibet, the middle part of Yunnan province, the Karst region of Guizhou province and the upper reaches of Minjiang River, respectively. For the consistency of pixels, the full agreement denotes all four products have a same land cover class, high agreement denotes there are two land cover classes among four products, low agreement denotes there are three land cover classes among four products and no agreement denotes there are a unique class in each product.