esa-sceva/satcom-chunk-collection huggingface dataset:
# Borehole Condition and Limit Pressure Differential Analysis in Carbonate Reservoirs
## 1 Introduction  
Carbonate reservoirs constitute nearly half of global oil reserves, signifying a pivotal role in hydrocarbon exploration and production endeavors [1, 2]. Western China boasts abundant resources of fractured-vuggy carbonate reservoirs, comprising two-thirds of proven carbonate reserves [3]. Notably, the Tahe and Shunbei oilfields in the Tarim Basin represent ultra-deep fractured-vuggy carbonate reservoirs with promising exploitation prospects [4].  
In carbonate reservoir development, open-hole completions enhance productivity but are more susceptible to wellbore collapse during testing or production operations, leading to subsurface accidents. From a rock mechanics standpoint, borehole collapse predominantly arises from severe stress concentrations on the borehole wall, surpassing the compressive strength of the reservoir rock.  
Currently, wellbore stability studies largely concentrate on drilling operations, with limited attention given to well testing and production phases. Drilling-focused wellbore stability analyses primarily consider the impacts of geomechanical parameters and drilling fluid-induced mass, heat, and pressure transfers on borehole wall stresses and rock strength, with a predominant focus on shale formations [5, 6]. Conversely, well-test instability investigations target reservoir rocks, incorporating distinct factors compared to drilling scenarios. Besides in situ stresses and rock strength, drilling-related factors--such as drilling fluid leakage, tool rotation/vibration, and pressure variations from pipe movements--also contribute to borehole collapse, though quantifying these engineering aspects for collapsepressure calculations proves challenging, rendering drilling collapse pressure estimations inherently conservative. The underlying principle assumes that any breach of the elastic limit at the borehole wall may induce instability.  
During well completion tests, reservoir fluids infiltrate the wellbore, and once a negative pressure differential is established, the formation-hydrostatic equilibrium suggests a stable condition, minimizing borehole perturbations. Hence, the presence of plastic deformation zones or minor failure areas on the borehole wall does not necessarily compromise overall wellbore stability under these circumstances.  
Therefore, by investigating the stress distribution around the borehole and the effect of completion fluids on reservoir strength during open-hole completion tests in carbonate reservoirs, complemented with the application of strength criteria, we evaluate the borehole wall condition and adjacent formation stability. This process facilitates the establishment of a comprehensive wellbore stability assessment criterion, ultimately leading to the determination of the permissible maximum pressure differential.
# In the phase of testing
## 4 Implementation and Results  
To test our proposed framework and algorithm, we tested it on real data that we have collected from the National Center of Meteorology and the Jeddah Municipality. We obtain daily weather data from Jeddah city for ten years (from 1 January 2009, to 31 December 2013) and (from 1 January 2018, to 31 December 2022). The dataset contains 3654 rows with eleven features are:  
* DT (Date);
* Temperature (deg. c);
* Dry rate;
* Wet rate;
* Vapor rate;
* Humidity rate (%);
* Wind speed KTS/Deg;
* Pressure degree (hPa);
* Mean of sea level;
* Total rain amount (mm);
* Station level.  
We received data as a PDF file and then we converted it to CSV to deal with it in python. Moreover, we discussed with experts and consultants of weather about the factorsthat have an effect on the flood, then selected thresholds for some features, such as rain amount and temperature degree, in addition to classifying data. In addition, we excluded some unimportant features, such as dry rate, pressure, and sea level, which did not change during dates for the same city. Table 2 shows a subset (10 rows) of the sample data based on the features after initial processing.
## 2 Materials and Methods
### Data Processing  
A fourth-order low-pass Butterworth filter with a cut-off frequency of 50 Hz was applied to the raw GRF signal [27]. Maxima of the GRF signal relating to the forefoot and rearfoot impact peaks were extracted using a self-developed custom MATLAB code (v2017b, MathWorks, Natick, MA, US), as participants were reported to adopt a toe-to-heel landing style [12; 20; 28]. The mean loading rate for forefoot and rearfoot impacts was calculated between 20%-80% of the respective slope preceding each peak [3], with the first slope corresponding to the forefoot loading rate and the second slope corresponding to the rearfoot loading rate. GRF variables were normalized to bodyweight (BW) [12; 28; 29].  
Sagittal plane kinematic and kinetic variables of the right lower limb during the DVJs were extracted using Visual3D (v6.01.18, C-Motion, Germantown, MD, USA). Initial contact was identified when the unfiltered vertical GRF first exceeded 10 N [5; 19; 29]. The landing phase was defined as the period from initial contact of the right foot to maximum knee flexion of the right leg [3; 4; 30]. Marker  
Figure 1: (**a**) Sequence of maneuvers for the double-legged, drop vertical jump task on two adjacent force platforms which measured the vertical ground reaction force (vGRF). (**b**) Linear envelope of a representative electromyography (EMG) signal (solid line) with accompanying vGRF (dashed line) versus time. Shaded area indicates the 100 ms-precontact period before initial contact.  
trajectories were filtered using a fourth-order low-pass Butterworth filter with a cut-off frequency of 10 Hz. Joint angles were derived through a Cardan XYZ rotation sequence [28] of the distal segment relative to the proximal segment. The ankle angle was normalized to the static standing position [28; 31]. Positive joint angles (hip/knee/ankle) represent flexion/dorsiflexion; negative joint angles (hip/knee/ankle) represent extension/plantarflexion. Peak external flexion moments were determined during the landing phase [4] for ankle, knee, and hip joints through an inverse dynamics approach [4; 32]. The integral of the negative joint power curve defined the total eccentric joint work performed by each joint. Joint kinetics were normalized to body mass.  
Mean muscle activations of each muscle were analyzed during the precontact phase of landing, which was considered to be the 100 ms period before initial contact [5; 21; 33]. A band-pass filter (20 to 450 Hz) [34] was applied to raw EMG signals. A linear envelope was formed by full-wave rectification of the EMG signal, then a low-pass fourth-order Butterworth filter cut off at 10 Hz was applied. Mean muscle activation was calculated by dividing the mathematical integral of the linear envelope across the precontact period by 100 ms (Figure 1b).
# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Localization Performance
We show the CDFs of the localization errors in the first scenario shown in Figure 17. The experiments demonstrate our proposed method achieves sufficient accuracy compared to the CHAN, PDR, and the improved PDR estimation in different devices and different pedestrians. This result occurs because the generation of weight values can accurately calculate the importance between the CHAN and improved PDR estimation, and the localization scheme can detect invalid estimation. Figure 16: Mean localization errors among CHAN, PDR, improved PDR, and the proposed algorithm with different step counts. (**a**) Volunteer #2 with a VivoY85a cell phone (scene 1). (**b**) Volunteer #2 with a VivoY85a cell phone (scene 2). (**c**) Volunteer #1 with a VivoY85a cell phone (scene 2). (**d**) Volunteer #1 with an Honor60 cell phone (scene 2). Figure 17: The CDFs of the localization errors among the CHAN algorithm, PDR algorithm, improved PDR algorithm, and the proposed algorithm in the first scene. (**a**) Volunteer #1 with a VivoY85a cell phone (scene 1). (**b**) Volunteer #2 with a VivoY85a cell phone (scene 1). (**c**) Volunteer #1 with an Honor60 cell phone (scene 1). (**d**) Volunteer #1 with an Honor60 cell phone (scene 1). We compare the CDFs of the localization error in the second scenario in Figure 18. The experiments show that our proposed algorithm in a larger environment also achieves higher accuracy than the CHAN, PDR, and improved PDR estimation in different devices and different pedestrians. These results occur because the generation of weight values can obtain the importance in the CHAN and the improved PDR estimations. Tables 5 and 6 show the localization errors of the 90 th percentile in the two scenes for different pedestrians and different devices. The experiments show that the localization accuracy of the proposed method has greater improvement than the CHAN, PDR, and the improved PDR method. Our method thus achieves good generality and flexibility in different pedestrians, devices, and scenes. In addition, we compare the calculation complexity between our proposed algorithm and CHAN algorithm in two scenes. In the first scene, totaled 48 times, it ran 13.0944 s using the CHAN algorithm and 13.8225 s using the proposed method, respectively. In the second scene, totaled 60 times, it consumed 14.0062 s using the CHAN algorithm and 15.0488 s using the proposed method. The experiments show that our method is comparable with the CHAN method and has better localization performance.
## 4 Experiment
### Data  
The experiment (see Figure 1 for experimental protocol) uses a seasonal time series from the M forecasting competition [68] to generate a synthetic sales dataset by first decomposing it (into trend-cycle, seasonal, and remainder components) and subsequently constructing samples with similar demand characteristics.  
Regarding the returns time series, the assumption is that Acme has three classes of customers with distinct returns characteristics or profiles (where a profile is a specific combination of the mean and standard deviation of returns that follows a normal distribution). A discrete event-simulation model built in AnyLogic [69] on this assumption generates the requisite returns data. It first spawns customer \"agents\" (based on the sales data) and  
Figure 1: Experimental protocol consists of three stages: generation of synthetic data, creation of the RNN model for predictions, and incorporation of the RNN model in the system dynamics causal model for simulating scenarios for heuristics (human)–ML collaboration.  
sorts them randomly into three groups, assigning them their corresponding returns profiles. After a specified time offset, the model simulates an agent generating a product return per its profile--that is, a sample value, which stands for the number of units returned, is drawn from a normal distribution with the profile's mean and standard deviation (see Figure 2).  
The synthetic data generation process outputs a file consisting of 480 months of monthly sales (broken down by customer group) and returns data. Before model generation, the data undergo a further important processing step designed to tackle the problem of poor generalization common in ML.  
Poor generalization performance, or the phenomenon of overfitting, is when model performance deteriorates severely on unseen data. It typically happens when the model learns spurious correlations or memorizes inputs [70] to return good training performance that does not translate to good performance in the real world. The usual recommendation to avoid overfitting is to split the data into train, test, and validation sets [71], which the experiment adopts by splitting the data according to a 60/10/30 ratio. Although several specific techniques exist to perform the split, the experiment takes a simple holdout validation approach. In this variant, the training set determines model parameters, the validation set helps fine-tune those parameters, and the test set forms the basis for the final performance evaluation. Given the relatively simple nature of the original seasonal sales data with a limited number of possible features and the synthetic data generation approach (affording finer control over the noise, thus placing modest demands on sample size), there are no grounds for more complex treatments such as K-fold and iterated K-fold validation more suited to feature-rich/data-sparse contexts [70].
# Image Analysis Applications for Building Inter-Story Drift Monitoring
###### Abstract  
Structural health monitoring techniques have been applied to several important structures and infrastructure facilities, such as buildings, bridges, and power plants. For buildings, accelerometers are commonly used for monitoring the accelerations induced by ambient vibration to analyze the structural natural frequencies for further system identification and damage detection. However, due to the relatively high cost of the accelerometers and data acquisition systems, accelerometer-based structural health monitoring systems are challenging to deploy in general buildings. This study proposed an image analysis-based building deformation monitoring method that integrates a small single-board computer, computer vision techniques, and a single-camera multiple degree-of-freedom algorithm. In contrast to other vision-based systems that use multiple expensive cameras, this method is designed for a single camera configuration to simplify the installation and maintenance procedures for practical applications. It is designed to monitor the inter-story drifts and torsional responses between the ceiling and floor of a story that is being monitored in a building, aiming to maximize the monitored structural responses. A series of 1:10 reduced scale static and dynamic structural experiments demonstrated that the proposed method and the device prototype are capable of analyzing images and structural responses with an accuracy of 0.07 and 0.3 mm from the results of the static and dynamic experiments, respectively. As digital imaging technology has been developing dramatically, the accuracy and the sampling rates of this method can be improved accordingly with the development of the required hardware, making this method practically feasible for an increasing number of applications for building structural monitoring.  
Keywords:structural health monitoring; inter-story drift; image measurement +
Footnote †: journal: Applied Sciences
## 3 Experimental Validation
### Rounded Cylinder Modeling  
The experimental results involving the rounded cylinder modeling are reported in this subsection.  
The geometrical parameters of the rounded cylinder considered to model the antenna are \(h_{c}=10\)\(\lambda\) and \(a_{c}=0.85\)\(\lambda\). The probe voltages have again been acquired on a spherical surface of radius \(d=20\)\(\lambda\), and the shown reconstructions have still been attained by choosing \(\chi^{\prime}=1.30\), \(\chi=1.20\), and \(p=q=7\). Also, in such a case, the factor \(\chi^{\prime}\) has been suitably augmented in the region of the spiral wherein the 20 samples close to each of the poles are located.  
The amplitudes and the phases of the recovered voltage \(V_{r}\) on the meridians at \(\varphi=0^{\circ}\) and \(\varphi=90^{\circ}\) are compared with those of the directly measured one on the same meridians in Figures 8 and 9. The same comments made on the reconstructions reported in Figures 4  
Figure 5: Voltage amplitude (a) and phase (b) on the meridian at \(\varphi=90^{\circ}\). — measured. ++++ obtained from the NR data.  
Figure 6: FF cuts at \(\varphi=0^{\circ}\) (a) and \(\vartheta=90^{\circ}\) (b). — reference. ++++ obtained from the NR data.  
The number of employed NR NF data in this case is 983 (comprehensive of the 160 extra samples nearby the poles), much lower than those 1860 and 7320 necessary for the traditional NF /FFT [15] in the onset and offset mounting cases, respectively.  
For reader's convenience, the bar graph comparing the numbers of samples required by the various approaches is shown in Figure 12.
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 3 Materials and Methods
### Performance Indexes  
Three performance indexes are considered:  
* Index of fitting (\(FIT\)): normalized index that indicates how much the prediction matches the real data. For a perfect prediction, it is equal to 100%, and it can also be negative. It is expressed as: \[FIT=100\bigg{(}1-\frac{\sum_{k=1}^{S}\lVert\hat{y}_{k}-y_{k}\rVert}{\sum_{k=1}^{S }\lVert y_{k}-\overline{y}_{k}\rVert}\bigg{)}\] (8)
* Pearson correlation coefficient (\(\rho\)): measures the linear correlation between two variables and has a value between \(-1\) (total negative correlation) and \(+1\) (total positive correlation). \[\rho=\frac{\sum_{k=1}^{S}(y_{k}-\overline{y})(\hat{y}_{k}-\overline{y})}{ \lVert y_{k}-\overline{y}\rVert\lVert\hat{y}_{k}-\overline{y}\rVert}\] (9)
* Root Mean Squared Error (\(RMSE\)): shows the Euclidean distance of the predictions from the real data using \[RMSE=\sqrt{\frac{1}{S}\sum_{k=1}^{S}(y_{k}-\hat{y}_{k})^{2}}\] (10) where \(y\) is the vector of the real data, \(\hat{y}\) of the predicted ones, containing \(S\) elements each, and \(\overline{y}\) and \(\overline{y}\) are their respective mean values.  
The training and validation of the models are performed on five datasets (Dataset 2-Dataset 6) for a total amount of thirty days, and tested on the remaining one, Dataset 1.
# Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism
###### Abstract  
The research on highly inclined mean motion resonances (MMRs), even retrograde resonances, has drawn more attention in recent years. However, the dynamics of polar resonance with inclination \(i\approx 90^{\circ}\) have received much less attention. This paper systematically studies the dynamics of polar resonance and their effects on the Kozai-Lidov mechanism in the circular restricted three-body problem (CRTBP). The maps of dynamics are obtained through the numerical method and semi-analytical method, by mutual authenticating. We investigate the secular dynamics inside polar resonance. The phase-space portraits on the \(e-\omega\) plane are plotted under exact polar resonance and considering libration amplitude of critical angle \(\sigma\). Simultaneously, we investigate the evolution of 5000 particles in polar resonance by numerical integrations. We confirm that the \(e-\omega\) portraits can entirely explain the results of numerical experiments, which demonstrate that the phase-space portraits on the \(e-\omega\) plane obtained through the semi-analytical method can represent the real Kozai-Lidov dynamics inside polar resonance. The resonant secular dynamical maps can provide meaningful guidance for predicting the long-term evolution of polar resonant particles. As a supplement, in the polar 2/1 case, we analyze the pure secular dynamics outside resonance, and confirm that the effect of polar resonance on secular dynamics is pronounced and cannot be ignored. Our work is a meaningful supplement to the general inclined cases and can help us understand the evolution of asteroids in polar resonance with the planet.  
celestial mechanics; Earth; minor planets; asteroids; planets and satellites; dynamical evolution and stability  
[PERSON], [PERSON], S. Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism. _Appl. Sci._**2022**, 12, 6530. [[https://doi.org/10.3390/app12136530](https://doi.org/10.3390/app12136530)]([https://doi.org/10.3390/app12136530](https://doi.org/10.3390/app12136530))  
Academic Editor: [PERSON]
###### Abstract  
In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.  
Keywords:rotating machine; rolling element bearing; indirect self-tuning observer; machine learning-based classification technique; autoregressive with external uncertainties; machine learning-based regression; proportional multi-integral technique; variable structure-Lyapunov technique; self-tuning network-fuzzy system; fault detection; fault classification +
Footnote †: journal: Applied Sciences
## 6 Thermodynamics of Inclusion Complexes
### Stability of Inclusion Complexes  
The structural modifications induced by methylation result in increased stability of the inclusion complexes formed between M-\(\gamma\)-CD and heavy metals. The enhanced interactions  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Properties** & \(\alpha\)**-CD** & \(\beta\)**-CD** & \(\gamma\)**-CD** \\ \hline Glucose unit and & 6 & 7 & 8 \\ Chemical formula & C\({}_{36}\)H\({}_{60}\)O\({}_{30}\) & C\({}_{42}\)H\({}_{70}\)O\({}_{35}\) & C\({}_{48}\)H\({}_{80}\)O\({}_{40}\) \\ \hline Molecular weight (g/mol) & 972 & 1135 & 1297 \\ \hline Water solubility (g/L) & 145 & 18.5 & 232 \\ \hline Inside diameter (Å) & 4.7–5.3 & 6.0–6.5 & 7.5–8.3 \\ \hline Outside diameter (Å) & 14.6 \(\pm\) 0.4 & 15.4 \(\pm\) 0.4 & 17.5 \(\pm\) 0.4 \\ \hline Cavity volume (Å\({}^{3}\)) & 174 & 262 & 427 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of important characteristics of \(\alpha\)-CD, \(\beta\)-CD, and \(\gamma\)-CD [9,11,43].  
arising from increased hydrophobicity and improved accessibility of binding sites ensure that once metal ions are encapsulated within the cyclodextrin cavity, they remain securely bound throughout the removal process, thereby reducing the risks of desorption [17]. Methylation significantly enhances the stability of the inclusion complexes established between M-\(\gamma\)-CD and heavy metals. The strengthened interactions due to hydrophobic encapsulation contribute to elevated complex stability, which is essential for the effective removal of metals during environmental remediation efforts [19; 33].