esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RUL Prediction Method Based on Hierarchical Clustering and Transformer-GRU
### Process of the Proposed Method  
The flowchart of the proposed method for rolling bearing RUL prediction is illustrated in Figure 7, and the specific steps are as follows:  
Figure 6: Structure diagram of Transformer-GRU combined model.  
Figure 7: Graphical abstract.  
Step 1: Signal acquisition and denoising: Vibration signals of the full life cycle of the bearing are collected using an acceleration sensor. Wavelet denoising is applied to reduce noise in the original signals.  
Step 2: Feature extraction: Time-domain, frequency-domain, and time-frequency-domain features are extracted from the denoised signals to form the original feature set, which is then smoothed and normalized.  
Step 3: Feature selection: Based on a composite index constructed from monotonicity and correlation, features insensitive to bearing degradation are filtered out to form a sensitive feature set.  
Step 4: Feature redundancy reduction: A hierarchical clustering method combined with the elbow method is employed to adaptively reduce redundancy in the sensitive feature set, resulting in the optimal feature set.  
Step 5: Model training: The optimal feature set is used as input data for model training. The ratio of RUL to the total life cycle is used as the label.  
Step 6: Model testing: The test set is fed into the trained model to predict the corresponding RUL labels, and the model performance is evaluated accordingly.
## 4 Results  
### Reservoir Physical Properties and Storage Space Characteristics  
Based on the measured data from the study area samples and collected physical property data, the porosity range of the Xujiaweizi Fault Depression Denglouku Formation reservoir is 2.0-11.2%, with an average porosity of 5.56%. The permeability range is 0.002-0.865 \(\times\) 10\({}^{-3}\) um\({}^{2}\), with an average permeability of 0.054 \(\times\) 10\({}^{-3}\) um\({}^{2}\), classifying it as typical tight sandstone. The relationship between the logarithms of porosity and permeability shows a poor linear relationship (R\({}^{2}\) = 0.43), reflecting the strong heterogeneity of the reservoir pore structure (Figure 2).  
Through the analysis of core samples and scanning electron microscope images from the study area, the Denglouku Formation tight sandstone mainly develops four types of pore structures: residual intergranular pores, intragranular dissolution pores, micropores, and microfractures (Figure 3). The storage space is primarily composed of residual intergranular pores and intragranular dissolution pores, with intragranular dissolution pores mainly including feldspar dissolution pores and lithic dissolution pores. The feldspar particles are completely dissolved to form mold pores. The radii of intergranular pores and intragranular pores are generally less than 10 um, and the pore radii are mainly distributed between 0.2 and 2.3 um, indicating a large number of micropores in the reservoir, which are one of the main contributors to porosity. The throat structure in the study area is mainly composed of sheet-like throats and bundle-like throats. These can serve as both fluid storage space and fluid flow channels. The throat radius is generally less than 0.15 um.
## 3 Correlation and Implementation
### Intelligent Compaction System Measurements
The values also show the same trend as the PLT test; for example, in the event stadium site, compaction spot number 5 has a higher CMV value than spot number 1. This proves that the CMV values are directly proportional to the bearing coefficients. So, CMV can be correlated with the bearing coefficient values, and a relationship can be developed to measure the compaction quality.
# An Interventional Surgical Robot Based on Multi-Data Detection
## 2 System Description  
### Overview of the Surgical Robot System  
In traditional interventional surgery, surgeons make an incision in an artery in the patient's arm or thigh and insert a guide wire and catheter. The surgeon usually inserts the guide wire into the patient's blood vessel first. The guide wire serves as a guide to the catheter, so the catheter can be pushed along the path of the guide wire. The surgeon pushes and rotates the catheter to move it along the blood vessel to the lesion. In summary, the operation of the catheter can be divided into two parts: pushing and rotating. Therefore, the catheter push device of the surgical robot system needs to be designed with two degrees of freedom to achieve catheter push and rotation.  
Figure 1 illustrates the workflow of the interventional surgical robot. The robot system consists of three main parts: a master manipulator, a slave manipulator and a motion controller (IMAC-LX, Delta Tau, CA, USA). The master side consists of a system control terminal, including two operations controller, and is respectively responsible for the control guide wire and tube and rotation. The master side is located outside the operating room to protect the surgeon from radiation damage. The slave is a robotic action system that replicates the surgeon's instructions.  
The structure of the slave is shown in Figure 2. The linear slide platform is provided with a guide wire clamping module and a conduit clamping module. Two DC motors (EC-22, Maxon, Sachseln, Switzerland) are installed at the end of the platform and are responsible for rotating the tracks to propel the modules. Each clamping module is also equipped with a brushless DC motor (EC-22, Maxon, Sachseln, Switzerland), which is responsible for controlling the rotation of the catheter or guide wire through a gear structure. When the surgeon operates the master manipulator, the motion information on the master side is transmitted to the motion controller using the EtherCat communication protocol. The motion controller transmits the acquired signal to the slave and drives the slave motor to push the guide wire and catheter into the patient's blood vessel. In the meantime, the DSA system captures the image information of the catheter and guide wire during the operation, and displays the image to the monitor, enabling the surgeon to ensure the safety of the operation.  
Figure 1: The system structure of the interventional surgical robot.
## 2 Related Work
### Underwater Communications Technologies  
Underwater wireless communications can be performed using three different technologies: acoustic, optical, and RF [17]. Acoustic communications are the main solution for underwater environment [16; 17]. While they enable kilometer ranges, the low propagation speed of sound in water and the kbit/s data rates make acoustic communications unsuitable for applications with high bitrate requirements, e.g., HD video transmission [19]. In [5], the authors provide a comprehensive comparison between current off-the-shelf acoustic modems and some experimental solutions available. In Table 1, we can observe that most of the commercial devices can only provide rates in the order of some kbit/s, being the fastest at 35 kbit/s. We can also observe that the devices that are developed by research groups are in line with the commercial products. From Table 1, we can observe that one modern exceeds these values; however, the 1-10 Mbit/s data rate is achieved due to the combination with optical communications [20]. It is also important to observe from Table 1 that the high power consumption of the commercial acoustic modems, which range from 1.8 to 300 W during transmission, can have a significant impacts on the endurance of autonomous vehicles and underwater observatories, which are typically battery-powered.  
Optical communications, namely underwater optical wireless communications (UOWC) or Underwater Free Space Optical Communication (uFSO), are able to provide throughputs up to Gbit/s, as shown in Figure 3[21; 22; 23]. Moreover, Optical Communication Systems can be compact, flexible and consume less power than acoustic systems. However, when used for medium-range communications, optical communications are severely affected by turbidity and require clear line-of-sight and beam alignment mechanisms, making them unfeasible in many scenarios [24; 25] and limiting its practical usage to tens of meters [26]. Despite these limitations, optical modems have shown bitrates up to 250 Mbit/s and ranges up to 200 m, such as SA Photonic Neptune [27].  
Despite the strong attenuation underwater, Radio Frequency (RF) communications based on the IEEE 802.11 standard can be used for short-range communications [17; 28; 29], matching the performance of UVLC without the need of line-of-sight [24] nor alignment algorithms [17]. Both the theoretical and experimental evaluations carried out found that 802.11 underwater networks at 2.4 GHz achieve few centimeter ranges. By employing sub-GHz frequencies, RF attenuation reduced progressively, increasing the communications range up to 5 m in freshwater and up to 1.8 m in seawater, with throughputs up to 550 kbit/s at 70-100 MHz [30]. These results prove the feasibility of IEEE 802.11 networks for short-range and high bitrate communications using VHF/UHF bands.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
## 2 Theoretical Framework
### Characterization of the Foam
#### 2.1.1 Delany-Bazley-Miki (DBM) Model  
[PERSON] and [PERSON] [44] have proposed, on the basis of large measurements, empirical expressions for the values of the complex wave number \(k\) and characteristic impedance \(Z_{c}\) for forporous materials with porosities close to 1:  
\[Z_{c}=\rho_{0}c_{0}\Bigg{[}1+9.08\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.75 }-j11.9\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.73}\Bigg{]}, \tag{1}\]  
\[k=\frac{\omega}{c_{0}}\Bigg{[}1+10.8\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.7}-j10.3\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.59}\Bigg{]}, \tag{2}\]  
where \(\rho_{0}\) is the density of air, \(c_{0}\) is the speed of sound in air, \(f\) is the frequency, and \(\omega\) is the angular frequency. Boundaries, proposed by the authors, for the validity of these power law expressions are \(0.01<\frac{f}{\sigma}<1.00\). This empirical model, which can provide reasonable estimations of \(k\) and \(Z_{c}\) in the approximate frequency range defined above, is still widely used for its simplicity: Only one parameter, the airflow resistivity \(\sigma\), is needed to describe the acoustic behavior of a material.  
The model by [PERSON] (DBM) [45] presents revised expressions for the complex wave number \(k\) and characteristic impedance \(Z_{c}\) of the Delany-Bazley model [44]. In the case of multi-layers, [PERSON] noticed that the real part of the surface impedance, when estimated with the classical Delany-Bazley model, may turn negative at low frequencies, thus leading to a non-physical result. In this context, [PERSON] suggests the usage of the following expressions:  
\[Z_{c}=\rho_{0}c_{0}\Bigg{[}1+5.5\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.63 2}-j8.43\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.632}\Bigg{]}, \tag{3}\]  
\[k=\frac{\omega}{c_{0}}\Bigg{[}1+7.81\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.618}-j11.41\bigg{(}10^{3}\frac{f}{\sigma}\bigg{)}^{-0.618}\Bigg{]}. \tag{4}\]  
The validity of [PERSON] model is the same of that related to the original formulation, even if his revised expressions behaves well in a larger frequency range, in particular for \(\frac{f}{\sigma}<0.01\).
## 1 Introduction
Continuous nonlinear equations need to be linearized first when using EKF. The EKF has the advantages of fast convergence and high accuracy in state estimation [68; 69; 70; 71]. This paper proposes an EKF-based method for small leakage detection and location in natural gas pipelines. Most researchers working in this topic approach the leakage detection problem from the research field of control. The main limitation in the extant literature is about the applicability of different control and detection methods. Complementary to the previous efforts, the authors of this paper approach this topic as subject matter experts in pipe leakage detection. We understand that both the NPW method and the fiber-optic-based method are widely used and able to obtain reasonably good leakage detection results. These two methods do not need to establish a pipeline model. They mainly process signals and identify patterns for real-time leakage detection. However, these two methods mainly work well with the common sudden and rapid leakage detection. They perform poorly with the slow and small leakage. EKF-based method is essentially a real-time modeling method. This method requires pipeline models. The computation is complex and converges slowly. Therefore, this method is not suitable for sudden and rapid leakage detection. On the other hand, a real-time model can provide a pipeline's flow characteristics and fluid properties and consider pipeline physical properties as well. In addition, a real-time model considers the effect of system noise and measurement noise. Its working principle determines its advantage in slow and small leakage detection. The Kalman filter has the advantages of fast convergence and high precision in terms of state estimation. This compensates for the original shortcomings in the real-time model method such as slow convergence. Therefore, it is adopted for real-time leakage detection in this paper. The rest of the paper is organized as follows. Section 2 proposes the pipeline transient flow model containing the virtual multi-point leakages. Section 3 elaborates the mathematics for the state estimation by EKF. Section 4 explains the calculation of the actual leakage rate and location. Section 5 verifies the model with a simulation example. In Section 6, a physical experiment is conducted to validate the effectiveness of the proposed method. Section 7 concludes the paper.
## 2 Materials and Methods
### Serum Folate Forms Determination by LC-ESI-MS/MS  
Folate forms in serum samples were determined by liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS/MS). Analysis of serum for MTHF, PGA, tetrahydrofolate (THF), formyl-THF, was performed on a Sciex 5500 QTRAP mass spectrometer (Foster City, CA, USA) coupled to a Shimadzu ultra-high pressure Nexera chromatograph system (Kyoto, Japan). The MS/MS experiments were performed under positive electrospray ionization (+ESI) with multiple-reaction monitoring (MRM) using a Turbolon Spray electrospray source operating at a voltage of 5.5 kV and desolvation temperature of 500 \({}^{\circ}\)C. The selected precursor and fragment ions used for the measurement of unlabeled and labeled folates are summarized in Table S1 (see Supplemental Data, Table S1). The calibration curve was prepared in water containing 2 mg/mL Na ascorbate over a range of 12.5-400 nmol/L for each folate analyte. Sample preparation involved combining 50 \(\upmu\)L of blank, standards (MTHF, PGA, THF and Formyl-THF), control, or sample with 10 \(\upmu\)L of 62.5 mM dithiothreitol, 20 mg/mL Na ascorbate, and 2.5 mM 13-C labeled stable isotope internal standards (13C5-5-MTHF, 13C5-PGA, 13C-Formyl-THF) in 1.5 mL microfuge tubes, mixed, and incubated for 10 min at room temperature in the dark. Next, samples were deproteinized with 5 volumes of methanol containing 2 mg/mL of ascorbate, mixed, and centrifuged at 14,800 rpm (21,100\(\times\)\(g\)) for 10 min. The supernatant was transferred to a 96-well microtiter plate for analysis. The samples were analyzed following injection of 10 mL of extract on a Synergi Hydro 4 um \(\times\) 150 \(\times\) 3 mm with a 4 \(\times\) 2 mm Guard column (Phenomenex, Torrance, CA, USA) maintained at 40 \({}^{\circ}\)C, and eluted in a gradient with buffer A (100% water with 0.1% formic acid) and buffer B (100% Methanol with 0.1% formic acid). The flow rate was 0.5 mL/min, with a gradient over a total run time of 6 min: 0.0-2.0 min, 0-100% B; 2.5 min, 100% B; 2.6-6.0 min, 0% B. Eluent flow from the column was diverted to waste at the beginning and end of each run and was only directed to the source for the period from 1.5-3.5 min. The LC-MS/MS data were acquired and processed using Analyst 1.5.2 software (Sciex, Framingham, MA, USA).  
Folate standards (MTHF, PGA, THF, and Formyl-THF) and 13C-labeled stable isotope internal standards (13C5-5-MTHF, 13C5-PGA, and 13C-Formyl-THF) were obtained from Schircks Laboratories (Bauma, Zurich, Switzerland). Formic acid, ascorbate, and dithiothreitol (DTT) were obtained from Fluka and Optima, and LC-MS grade methanol from Fisher Scientific (Waltham, MA, USA). Stock standard for each folate metabolite and internal standard was prepared as 1 mmol/L solution in water containing 2 mg/mL Na ascorbate and stored at \(-\)80 \({}^{\circ}\)C.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Contact Heat  
Testing insulation against contact heat involves exposing the tested sample, placed on a calorimeter, to contact with a heating cylinder heated to a temperature ranging from 100 to 500 \({}^{\circ}\)C. During research, the threshold time (t\({}_{\rm t}\)) is measured. Threshold time is the time from the moment of first contact with the heating cylinder until the calorimeter temperature increases by 10 \({}^{\circ}\)C compared to the initial value. For protective gloves, the test is carried out on three samples. Then, the arithmetic mean is determined from the three obtained threshold time values, which is the test result. Research on insulation against contact heat is carried out on an OTI-type thermal insulation testing device (OTI Greentech AG, Berlin, Germany). The insulation against contact heat research was performed for contact temperatures of 100 and 250 \({}^{\circ}\)C according to EN ISO 12127-1 [34; 35].  
Based on laboratory tests of insulation against contact heat, protective gloves are classified into one of four levels of protection effectiveness. The first level of protection effectiveness is achieved at a contact temperature of 100 \({}^{\circ}\)C when the threshold time is \(\geq\)15 s. The second level is obtained for a contact temperature of 250 \({}^{\circ}\)C when the threshold time is \(\geq\)15, is \(\geq\)15, the third for a contact temperature of 300 \({}^{\circ}\)C also when the threshold time is \(\geq\)15, and the fourth for a contact temperature of 500 \({}^{\circ}\)C when the threshold time is \(\geq\)15 [35; 36]. Figure 5 shows a scheme for testing the composite's insulation against contact heat.  
Figure 4: Optical microscopy images of the inner surface of tested composites.  
The composite is placed on the substrate and then brought into contact with a heating cylinder heated to a contact temperature of \(T_{\rm c}\) (100 \({}^{\circ}\)C or 250 \({}^{\circ}\)C). In the first stage of the test, the substrate temperature \(T_{\rm s}\) is equal to the ambient temperature \(T_{\rm n}\) (Figure 5a). As a result of heat transfer from the cylinder through the composite, the substrate temperature \(T_{\rm s}\) increases (Figure 5b). The aim of the experiment is to determine the threshold time \(t_{\rm t}\), which is measured from the first contact with the heating cylinder until the calorimeter temperature increases by 10 \({}^{\circ}\)C from the initial value \(T_{\rm s}=T_{\rm n}+10\)\({}^{\circ}\)C (Figure 5c).
## 4 Numerical Examples  
To validate the numerical method's accuracy, two benchmark tests were simulated and compared: a single-edge notched tensile test and a uniaxial compression test on rock with a prefabricated crack. By aligning simulation results with the existing literature, the proposed method demonstrates the precise modeling of crack propagation processes.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 3 Background  
### Internet of Medical Things  
The IoMT can be represented as an ecosystem of the thing layer (sensors), cloud layer (storage), and application layer [7] (user interface), as shown in Figure 2. Remote patient monitoring (RPM) is one of the applications of IoMT, in which medical care and continuous patient monitoring are provided at a remote location. It means the patient is not admitted to the hospital, and instead the patient would be under 24-h continuous monitoring of medical care. The patients would receive immediate medical attention if their health status indicated some emergency.  
Patients are connected through sensors in the thing layer which sends the raw patient's data to the cloud layer through any communication standard (MQTT, CoAP, etc.) [8]. Thereafter, the data are filtered and organized by different users (clinician, administrator, labs, etc.) at the application layer. Responses from different categories of users are processed through the same communication standard from the application layer to the thing layer. Communication technologies, such as 5G/6G, Wi-Fi, Bluetooth, Zigbee, RFID (radio-frequency identification), NFC (near-field communications), etc., and different gateway devices, such as smartphones, are used to transfer the data according to the application demands [9].
# Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques
## 4 Results and Discussion
### Detection of Phrases Related to Fraud
#### 4.2.1 Classification Algorithms  
From the previously described data sets, we built classification models to unveil the trends that would enable us to say whether a new document is related or unrelated to fraud. We tested several classification algorithms to reveal which of them performs better with this particular set of data and, in general, if our approach to detect fraud would be feasible in practice.  
The use and selection of an adequate classification method are directly related to the information's characteristics. Within the spectrum and analysis of classifiers, the distinction between linear and non-linear models was made, taking into account the characteristics of each of these and the nature and quantity of the data. Specific differences between these two concepts can be mentioned. The linear ones are simple and easy to handle, and the fact that they have low computational consumption makes them ideal for use in topics such as automatic text classification. On the other hand, the non-linear ones directly related to neural networks assign data in higher-dimensional spaces [78].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{4}{c}{**Topics**} \\ \hline T1 & T2 & T3 & T4 \\ \hline review & debt & problem & want \\ \hline care & think & economic & know \\ poor & later & make & job \\ steal & fix & big & work \\ temporary & just & people & lose \\ \hline say & tell & abuse & support \\ new & inadequate & fair & deadline \\ man & look & compensation & help \\ really & failure & child & come \\ insufficient & weakness & good & time \\ \hline state & ill & earning & exploitation \\ money & unethical & easily & deserve \\ issue & life & accessible & scarce \\ \hline evacuation & world & country & right \\ leave & try & need & like \\ woman & let & way & day \\ year & talk & pay & use \\ long & old & school & scared \\ change & feel & home & ask \\ period & place & thing & car \\ \hline \hline \end{tabular}
\end{table}
Table 5: Most prevalent words from each topic related to the fraud triangle in our data set. Words are colored orange, blue, and green, representing the vertices pressure, rationalization, and opportunity, respectively.