esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
###### Abstract
Predictive analytics for downhole equipment failures enable proactive well interventions to reduce failure rates. To our knowledge, the predictive analytics models for equipment failures in ESPs are well-established in the existing literature. However, similar models still need to be improved for SRPs, which are widely used in Kazakhstan. Existing methods for ESPs cannot be directly applied to SRP systems due to differences in operating dynamics and failure modes. Further research is needed to develop and test machine learning algorithms targeting SRP failures. This will help reduce downtime, minimize maintenance costs, and optimize productivity in Kazakhstan's oil fields. Hence, this study proposes scientific support for developing analytics and artificial intelligence to enhance artificial lift productivity and optimize well operation to meet daily oil production targets. Consequently, reducing downhole equipment failures lowers well workover costs and increases production by minimizing downtime. Historical data analytics and artificial intelligence, particularly machine learning, facilitate predictive analytics for downhole equipment failures. The main aim of this paper is to evaluate the application of a predictive model to foresee upcoming failures in SRP systems. In this research, we examine how several standard and penalized machine learning models perform when applied to datasets with imbalanced classes from oil and gas wells. We put forth two hypotheses: we propose that methods used to address class imbalance may result in model calibration distortion by producing excessively high probability estimates. This research aimed to assess how adjustments for class imbalance affect the performance of the machine learning models in regard to their ability to discriminate, calibrate, and classify data.
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 3 Quad-Rotor Quaternion Dynamic Model
### Euler-Lagrange Methodology
#### 3.1.2 Potential Energy  
The potential energy is described by the product of the mass, the gravity \(g\), and the height \(z\) (the position in the vertical axis in the inertial frame)  
\[V=mgz. \tag{12}\]
## 2 Dimensional Synthesis Equations for Six-Bar Slider-Crank Mechanisms
### Procedure for Simplifying the Synthesis Equations of Watt II Six-Bar Slider-Crank
 \tag{16c}\]  
Similarly, substituting Equation (15) into Equation (14) and combining the coefficients of \(\cos\Delta\alpha_{j}\) and \(\sin\Delta\alpha_{j}\) yields  
\[Q_{1j}+Q_{2j}\cos\Delta\alpha_{j}+Q_{3j}\sin\Delta\alpha_{j}=0,j=2,\ldots,n, \tag{17}\]  
where  
\[Q_{1j}=2\left(r_{4x}^{2}+r_{4y}^{2}-r_{4x}r_{5x}-r_{4y}r_{5y}\right)+\Delta p _{j}^{2}+2\Delta p_{j}\left(r_{4y}-r_{5y}\right), \tag{17a}\] \[Q_{2j}=-2\left(r_{4x}^{2}+r_{4y}^{2}-r_{4x}r_{5x}-r_{4y}r_{5y}\right)-2r_{4y} \Delta p_{j},\] (17b) \[Q_{3j}=2\left(r_{4x}r_{5y}-r_{4y}r_{5x}\right)-2r_{4x}\Delta p_{j}. \tag{17c}\]  
In order to eliminate \(\Delta\alpha_{j}\) from Equations (16) and (17), solve the two equations for \(\cos\Delta\alpha_{j}\) and \(\sin\Delta\alpha_{j}\) to obtain  
\[\cos\Delta\alpha_{j}=\frac{-L_{1j}Q_{3j}+L_{3j}Q_{1j}}{L_{2j}Q_{3j}-L_{3j}Q_{2j} },\sin\Delta\alpha_{j}=\frac{L_{1j}Q_{2j}-L_{2j}Q_{1j}}{L_{2j}Q_{3j}-L_{3j}Q_{2j} },j=2,\ldots,n. \tag{18}\]  
Squaring both sides of Equation (18), adding the result, and simplifying using the trigonometric identity \(\cos^{2}(\Delta\alpha_{j})+\sin^{2}(\Delta\alpha_{j})=1\) gives  
\[\left(-L_{1j}Q_{3j}+L_{3j}Q_{1j}\right)^{2}+\left(L_{1j}Q_{2j}-L_{2j}Q_{1j} \right)^{2}-\left(L_{2j}Q_{3j}-L_{3j}Q_{2j}\right)^{2}=0,j=2,\ldots,n. \tag{19}\]Equation (19), the system of synthesis equations for \(n\) prescribed positions, has \(n-1\) eighth-degree polynomials in 10 unknowns, \(r_{ix}\) and \(r_{iy}\), \(i=1,\,\ldots\,5\), given in Equation (15). Hence, for 9 position synthesis, \(n\) is equal to 9 and there are 8 equations in 10 unknowns, and two unknowns can be selected as free choices. Among the 10 unknowns, \(r_{1x}\) and \(r_{1y}\) are chosen as the free choices in this study to reduce the calculation time as explained in Section 3.1. Now, the total degree of the 9 precision point synthesis equations is \(8^{8}=16,777,216\), which is much smaller than that of the synthesis equations derived in Section 2.1. However, the system size of the simplified synthesis equations can be further reduced by using auxiliary equations and applying the multi-homogeneous Bezout theorem. This process is described in Section 3.
## 3 Proposed Methodology
### K-Medoids-Based Clustering Algorithm (KCA)  
A KCA is provided to obtain a universal clustering strategy that consumes less energy and increases the network longevity. In our suggested approach, a base station collects data from all nodes sensing through the cluster head nodes. The proposed approach is divided into two phases: setup and communication phases.  
The setup phase involves grouping all nodes into appropriate clusters and identifying the CH nodes. This stage determines the number of clusters, selects the first CH nodes, and obtains the outcome. The number of clusters, \(K\), is computed using Equation (5).  
\[K=\sqrt{\frac{N}{2}} \tag{5}\]  
where \(N\) is the number of nodes. Let \(O\) be the central location for all nodes, and it can be calculated using the expression below.  
\[O=\frac{\sum_{i=1}^{N}X_{i}}{N} \tag{6}\]  
where \(X_{i}\) is the coordinate of sensor \(i\).  
Figure 1: WSN architecture [25].  
Let \(R\) be the average distance between the central location and all sensor nodes, and this can be calculated using Equation (7).  
\[R=\frac{\sum_{i=1}^{N}|X_{i}-O|}{N} \tag{7}\]  
The central circle is calculated using the values of \(O\) and \(R\). The \(K\) points are selected uniformly as the initial mean point, \(I_{ix}\) and \(I_{iy}\) can be computed using Equation (8).  
\[\begin{cases}I_{ix}=R\times\cos\left(\frac{360}{k}\times(i-1)\times\frac{\pi} {180}\right)+O_{x}\\ I_{iy}=R\times\sin\left(\frac{360}{k}\times(i-1)\times\frac{\pi}{180}\right)+O _{x}\end{cases} \tag{8}\]  
As the starting mean points are chosen randomly in the K-medoids method, they always have worse results when two or more mean points are quite near, which results in a considerable increase in the iteration time. These critical procedures result in the uniform first mean points being calculated. Thus, the partitioning technique reduces the distance between the ordinary and represented nodes. Equation (9) precisely denotes the absolute-error criteria \(E\).  
\[E=\sum_{i=1}^{k}\sum_{x\in C_{i}}dist(x,r_{i}) \tag{9}\]  
The replacement method is given by Equation (10).  
\[I^{(t+1)}=\begin{cases}I^{*},&E^{*}-E^{(t)}<0\\ I^{(t)},&otherwise\end{cases} \tag{10}\]  
Figure 2 illustrates the flow chart for the proposed KCA. The excess energy of the cluster head nodes must be addressed during the iteration time. The cluster controller node needs to be replaced if its remaining energy at the beginning of the iteration is lower than the average residual energy of all sensor nodes. The final clustering result is obtained by periodically repeating Equation (10).
## 5 Conclusions  
This paper proves that machine learning can be applied for coordinate transformations, even when the dataset for training the network consists of only a few (even minimum) points. The paper provides guidance on how to solve this problem and oversample a virtual point cloud for network training. The authors also found appropriate networks that provide better or the same level of accuracy compared to the novel (gradient) and traditionally used conformal transformations in applied scenarios. However, when the scenario is extended beyond the area of the points used for ANN learning (e.g., in the case of extrapolation), no increased accuracy of the results is expected. Therefore, the proposed method can be applied to points that are inside the adjustment points, and only in this case achieve equal or slightly better accuracy for the coordinate transformation.  
This raises the question of whether the use of conformal transformations for lower-scale coordinate systems, especially with the minimum number of common points, makes sense, since it can be carried out using ANN and more precisely at the same time, but in limited scenarios (inside adjusted points). Can such a transformation be justified in every situation? It should be noted that the ANN transformation is \"tailor-made\" and somehow restores the transformation symmetry, and in some cases, such as the study of the deformation of engineering objects, breaks the symmetry rather than restoring it, because this is how we can determine which points have moved and which have not. The proposed method of transformation can be considered closer to symmetry-back transformations. The benefit of its use is when we do not have additional common points to solve the transformation with mean square errors, especially when the measurement system is not a rigid system, e.g., it is exposed to the deformations due to thermal weather conditions. This can be considered in the measurement of floating objects; hence, the method may find application in the offshore industry and, in the authors opinion, is of great potential for applied engineering. The authors intend to focus further research on the potential applications of this transformation method for positioning moving objects on the water in inland navigation, using a non-metric industrial camera as a data source.  
[PERSON], [PERSON], [PERSON] and [PERSON] proposed the original idea for this study; [PERSON], [PERSON] and [PERSON] implemented the experiments for off-shore purposes; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper, with revisions by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant 3/SKG/20, financed from a subsidy of the Ministry of Science and Higher Education for statutory activities.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Comparing the results from the analytical (**a**) and network (**b**) model. Denotations xm-xp—errors for analytic (1) model, xs-xp—errors for the ANN.  
Informed Consent Statement: Not applicable.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 3 Methodology
### Feature Selection Workflow
#### 3.2.2 Defining the Characteristics of the Feature Selector  
Once the backbone model was decided, the hyperparameters of the model were needed. Within the selector, 12 training runs were utilized to lower variances with the CatBoost model itself being run for 500 iterations, and early stopping using a validation set was used to avoid overfitting of the data. Within the implementation, CatBoost gives indices of categorical columns to allow them to be encoded as one-hot encoding utilizing one_hot_max_size. This is based on the number of unique values available for the features considered. This is important for this study as the process can be calibrated based on the number of unique values within the dataset instead of using an assumptive value. An 'one_hot_max_size' of 25 was utilized given the presence of data for 25 neighborhoods (as is the case in Palermo) which have related categorical features. The use of the cat_features parameter allows the user to pass the column indices for the categorical features through the model for preprocessing and use in the one-hot encoding. The model utilized within the setup was defined as follows using the CatBoostRegressor model setup: model = catboost.CatBoostRegressor(eval_metric = 'RMSE', one_hot_max_size = 25, depth = 10, iterations = 500, l2_leaf_reg = 9, learning_rate = 0.05).  
Within the definition of the model, the eval_metric used was the Root Mean Square (RMSE) with an 'l2_leaf_reg' value of 9 used (regularization term used to regularize the objective function and minimize both loss and complexity of the model) and a depth of 10 (depth of tree used) and a learning rate of 0.05. These parameters were used based on iterations of models and were found to be the most appropriate. Early stopping of the model was also carried out with a test set of 20% to validate and ensure overfitting was not done. During the run of the model, the feature importance levels was recorded using the 'feature_importances' function of the Catboost library. These were recorded and sorted in descending order to showcase the features with the most impact on the model. Finally, the values were normalized based on the total contribution to the model result and these results were graphically represented.
## 2 Materials and Methods
### Experimental Heat Treatment Schedules and Property Measurements
#### 2.2.2 Microstructure Analysis and Mechanical Properties Measurement  
After the solid solution and aging processes outlined in Table 2, each specimen was polished and etched with a specific etchant (10 mL acetic acid + 4.2 g picric acid + 10 mL H\({}_{2}\)O + 70 mL ethanol) or with a 4% nitric acid alcohol solution for about 3-5 s. The as-cast, as-compressed, and heat-treated specimens were inlaid with cold-setting epoxy resin. The grain size was measured using the ASTM E112-88 chord length method in polarized light mode with no less than 200 grains. The precipitate phases were observed and analyzed by means of an XRD-6000 X-ray diffractometer (Shimadzu Corporation, Tokyo, Japan), a VHX-600E optical microscope (OM) (KEYENCE Co., Ltd., Osaka, Japan), a JSM-7100F scanning electron microscope (SEM) (JEOL Ltd., Tokyo, Japan) with an Oxford AZtecX-Max20 energy spectrum probe (Oxford Instruments, Oxford, UK) for the energy dispersive spectroscopy (EDS) analysis and with a JEM-2100 Plus transmission electron microscope (TEM) (JEOL Ltd., Tokyo, Japan) after ion thinning. Hardness was measured using the Vickers hardness tester (the load was 50 gf, the dwell time was about 15 s, the spacing between indentations was about 0.1 mm). Moreover, V-notched Charpy-notched specimens that were 5 mm \(\times\) 10 mm \(\times\) 55 mm (GB/T229-1994) in size were tested to measure the toughness. Each specimen was tested three times and then averaged. The hardness, elongation, and impact toughness were determined using the FM-700/SVDM4R microhardness tester (Future Tech Enterprise Inc., New York, USA), the FLFS-105 slow strain rate tensile tester (FULE Instrument Technology Co., Ltd., Shanghai, China), and the C64-305 MTS universal tester (MTS Systems (China) Co., Ltd., Eden Prairie, MN, USA), respectively. The liquidus and solidus temperatures of the Mg-Nd-Zn-Zr alloy during the cooling process were analyzed using a TGA/DSC 3 + differential thermal analyzer (Mettler Toledo, Zurich, Switzerland).
## 3 Numerical Solution: The Temperature Particles Method (TPM)  
### Particles Generation  
Figure 2 illustrates the idea of particles generation during every time step \(\Delta t\). A solid surface is broken into a finite number M of linear elements (or flat panels) of length \(\Delta S_{i}\). On the center of every flat panel is placed the pivotal point; thus, M pivotal points over the solid surface are necessary to impose the problem boundary conditions.  
The imposition of the no-slip condition implies the solution of a linear system of algebraic equations to generate M Lamb discrete vortices of strength \(\Delta\phi=\Delta\Gamma\) and vortex core \(\sigma_{i}=\sigma_{V}\). The global circulation condition is additionally satisfied. A cloud of \(Z\) Lamb discrete vortices induces vorticity on point j located in the fluid domain with a Gaussian basis function given by [40]:  
\[\omega_{j}=\sum_{i=1}^{Z}\frac{\Delta\Gamma}{\pi\sigma_{V,i}^{2}}\exp{\left(- \frac{r_{ji}^{2}}{\sigma_{V,i}^{2}}\right)}. \tag{11}\]  
A direct interpretation of the Fourier Law [11; 22] allows to generate M temperature particles, one for every flat panel, of intensity \(\Delta\phi=\Delta Q\) and temperature core \(\sigma_{i}=\sigma_{T}\) as follows [11]:  
\[\Delta Q_{i}=\frac{1}{RePr}\frac{T_{W}-T_{i}}{T_{W}-T_{\infty}}\frac{\Delta S _{i}\Delta t}{\sigma_{T,i}}. \tag{12}\]  
A cloud of \(Z\) temperature particles similarly induces temperature on point \(j\) located in the fluid domain in the following manner [1; 9]:  
\[T_{j}=\sum_{i=1}^{Z}\frac{\Delta Q_{i}}{\pi\sigma_{T,i}^{2}}\exp{\left(-\frac {r_{ji}^{2}}{\sigma_{T,i}^{2}}\right)}. \tag{13}\]
## 1 Introduction
### Passive Methods (Passive Coatings)
#### 1.1.3 Coatings Based on Metamaterials  
[PERSON] and others [3-8] have provided several very attractive analytical solutions of the problem of transformation of any opaque body into a transparent one. These solutions are obtained with a ray-like representation of the wave fields both for electromagnetic and acoustical waves and are represented by a passive coating. This coating contains metamaterials (where, for instance, the phase and group velocities of a wave have mutually opposite signs). Due to the exotic characteristics of metamaterials, the incident wave avoids the protected body without scattering behind the body (see Figure 1b). For the solution by [PERSON] and others in its acoustical version [5] we need the anisotropic mass density, which is a difficult problem in practice. The drawbacks of this approach are: (a) the energy concentration of rays (and field intensity) in the tangential area of a coating with thickness \(h\) (Figure 1-b) becomes too large if _D\({}_{l}\)/h\(>>\)_1;; (b) the thickness \(h\) of this coating must be much greater than the length \(\lambda\) of an incident wave (like in the passive absorptive coatings mentioned above) because the wave field is represented by rays; (c) this coating does not suppress any radiation from the protected body; (d) any practical realization of these coatings can achieve the desired exotic parameters only for sufficiently narrowband incident waves, due to the effects of the wave dispersion in the coating.  
_1.2. Active Methods_Active methods of sound control present an alternative to the passive ones and require an outside source of energy. The main difference between active and passive methods is the radiation of special waves, which have the same propagation direction and magnitude as the controlled waves, but the opposite wave sign. In the monochromatic case this means radiation of anti phase waves. Active methods can be attractive for the reason that active coatings can be of much thinner than the length of the wave to be damped (or an acoustically thin coating). To realize this coating we need very small sensors and actuators, electrically connected with the control center (computer). From a rigorous mathematical point of view, the methods for active control of waves represent a large branch of automatic control theory (or optimal control with criteria of minimum errors or fastest descent,...etc, see for instance the books by [PERSON] and others [17-19]).
## 3 Methodology  
To overcome the aforementioned limitations of current approaches, in this paper, we propose a multi-task learning based POI recommender system simultaneously considering sequential check-ins learning and geographical influence. Figure 1 illustrates the overview of our proposed POI recommender system. The training and predicting process can be summarized as below: (1) The historical check-in records are grouped by individuals where each personal check-in sequence is described using POI ids; (2) The sliding window is applied to split the individual sequential check-ins as real training samples for the train of GAN; (3) The features (i.e., user ID, temporal check-ins, and geographical location) of each sample are extracted from the original dataset and integrated as an input hub; (4) Based on the features in the input hub, an adversarial game is construed where the LSTM-based generator generates fake check-in samples while the LSTM-based discriminator tries to distinguish the real and fake samples; (5) the converged generator is capable of capturing the actual user preferences and provides POI recommendations. In the following sections, we will introduce the module of data preprocessing and generative adversarial networks in detail.
## 2 VLC System Model
### Receptor
The CPU, equipped with the PSO algorithm, processes the received data to determine the approximate location of the receiver and, consequently, the object or equipment to be located. Utilizing the existing WiFi infrastructure for data transmission eliminates the need for additional physical connections or proprietary networks, facilitating seamless data transfer and enabling real-time processing and analysis. Considering the technical factors widely discussed in the literature, we assume that in optical receivers, in addition to the PD, there are optical filters with a bandwidth of 20 nm and central wavelengths of 520, 540, and 560 nm. This selection ensures an adequate distinction between the wavelengths emitted by each LED, thus minimizing significant overlap and interference between different LED signals. As a result, the optical receiver can differentiate and identify each transmitter based on the received wavelength. Moreover, the selected parameters are practically compatible with existing LED technology and readily available filters, making the implementation of the proposed system highly feasible.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### WMN Node Placement and Routing
#### 2.2.1 Node Placement and Optimisation  
We can consider the work in this paper to be a specific application of the Mobile Gateway (MG)/Mobile Router (MR) placement problem. Many approaches have been considered for this in the past, for which both fixed and dynamic topologies have been taken into account [30], with the former being more relevant given our focus on 5G FWA. Due to the more general nature of the field, this research traditionally attempts to minimise interference between nodes and balance link utilisation to ensure fairness and load distribution [31].  
Research on fixed topologies typically focusses on joint routing/channel allocation strategies as these increase the flexibility and utilisation of links to optimise throughput or aggregate load, while others aim to minimise hop count [32]. No general consensus has been reached on the optimal approach given the wide range of applicable deployment scenarios, but most research favours simplicity based on the complexity of multi-objective problems [33]. Additionally, while FWA topologies are fixed by definition, some research on dynamic topologies is also relevant in our case as we do consider the number of nodes and meshing configuration. These approaches are defined by the constraints imposed on the problem, such as the presence and number of fixed gateways or if the network is completely unfixed [30]. As above, this research aims to minimise the cost of multi-hop deployments or maximise profit.  
In our case, interference can be mitigated effectively through the limited number of links, the 802.11 ad orthogonal channel options, and link transmission characteristics. Accordingly, one of the key metrics that must be specifically considered in our work on MR placement and configuration is reliability, as we aim to establish a realistic threshold between performance and resilience through meshing. Some previous work also exists in this area. [PERSON] et al. [34] defined a reliability cost function that allows for the maximization of the reliability of the whole WMN. However, it is important to note the additional overheads introduced to the optimisation problem when reliability is elevated and the trade-off with respect to cost that this introduces.