esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 1 Introduction
The results demonstrated that the rotation action of the beam around the pin at the top T-stub had a promising energy concentration as a result of the detachable friction damper, as well as negligible plastic deformation in other components; however, the disadvantage of occupying available height cannot be overlooked. Motivated by the above considerations, a new T-stub connection in which two steel plates are inserted between the T-stub connection at the bottom of the beam and the column flange was presented in [24], as shown in Figure 1. Such a connection offers advantages to both seismic performance and rehabilitation. First of all, the space formed by two steel plates provides deformation and yield conditions for the bottom T-stub connection of the beam end under negative bending. The new T-stub connection has outperformed the traditional one (in which yielding is only possible under positive bending), thus effectively increasing the plastic deformation and energy dissipation capacity of joints. Secondly, the failure of beam-to-column joints is centered only on new T-stub connections with inserted plates at the lower flange of the beam, while most of the middle and upper sections of the beam are still in an elastic working state, rendering large-scale repair unnecessary. By only replacing the damaged bottom T-stub connection, convenient and cost-saving restoration can be ensured. Thirdly, the new T-stub connection with inserted plates does not reduce the height of space below, and can be manufactured and installed with ease. Along with the introduction of the new T-stub connection with inserted plates, other vital factors are worth exploring. For instance, it is important to determine which kind of T-stub connection shows the better performance, whether the connection it is produced by welding or cut from a steel section, to what extent the inserted plates will improve the energy dissipation capacity of the T-stub connection and what is the optimum width of inserted plates is that can maximize mechanical behavior. This study takes as its research object T-stub connections that use inserted plates from a steel beam-to-column joint. Specifically, it focuses on the component formed by bolting one T-stub flange and two steel plates with two high-strength bolts. In order to fully investigate the mechanical properties of the new connections, this paper first establishes the calculation model of load bearing capacity of the innovative connection based on two distinctive failure types. Next, uniaxial tensile, compressive and cyclic experiments are conducted on T-stub connections with or without inserted plates to verify the accuracy of the model and probe the influence of fabrication method, length of T-stub flange and width of plate on the strengths, stiffness and energy dissipation capacity of connections.
## 5 Scenarios and States: Representation and Characterization for Decision Path Making
### FEE--Fundamental Equilibrium Scenarios  
In the following section, we describe five equilibrium scenarios.  
Scenario N = 1 represents a situation of neutral equilibrium among the optimal equilibria; it is the medium-energy equilibrium scenario, characterized by CSFs which, while having their dynamics, do not produce significant alterations. In this sub-scenario, E = 21, and S = 0.  
Scenario N = 2 is a low-energy equilibrium, meaning that the system has slow dynamics. In this sub-scenario, E = 14, and S = 0.  
Scenario N = 3 corresponds to the frozen scenario, meaning the system has almost no dynamics. It is substantially different from N = 1, where there are some dynamics but is still at equilibrium. In this case, there are extremely slow dynamics, as if the scenario were frozen. Therefore, if there were alterations, they would be very slow or of a transient nature. In this sub-scenario, E = 7, and S = 0.  
Scenario N = 4 corresponds to a significantly energetic scenario, which can become unstable because small fluctuations can lead to alterations or imbalances. In this sub-scenario, E = 28, and S = 0.  
Scenario N = 5 corresponds to an equilibrium scenario, but at very high energy; therefore, small unbalanced oscillations can lead to chaos. In this sub-scenario, E = 35, and S = 0.  
The five FEE sub-scenarios have the characteristics of having E \(\in\)[7; 35] and S = 0, from which it can be seen that they fall into Areas I (blue), II (light blue), and III (green). In the DSS (decision support system) model, just like in chaos and complexity theory, the FEE represent five fundamental attractors. The maximum equilibrium attractor is FEE_5, while the minimum is FEE_3. We could add the lower and upper extremes as well. The lower one would represent the beginning of the hibernation space, and the upper one the chaos. In that case, we would have seven attractors, and a match could be created between the proposed model and [PERSON]'s catastrophe theory. In mathematical terms, a catastrophe is a degenerate (or non-regular) critical point of a smooth (everywhere differentiable) surface defined in an n-dimensional Euclidean space, as these points correspond to radical bifurcations in the system's behavior.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
###### Abstract  
Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time.  
artificial intelligence; machine learning; fault detection; support vector machine (SVM); feature extraction; decision tree; rotating machines; dimensionality reduction
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 4 Experiments and Discussion
### Forbidden Virtual Fixture Experiment  
Figure 12 shows the experiment scene of the FRVF. Considering FRVF generally contains multiple VF geometries, the spatial plane-based FRVF and the single-leaf hyperboloid-based FRVF are composites to be used in this experiment. While evaluating the constraint performance of the single FRVF, the constraint performance of the composite FRVF is also evaluated. In the experiment, the robot works in the admittance-based drag control mode. The endoscope of the robot is dragged from the starting point to the ending point, without FRVF constraint and with composite FRVF constraint. During the experiment, the trajectory of the endoscope tip is obtained through the navigation system.  
The experimental results are shown in Figure 13. It can be seen that without the FRVF constraint, the trajectory of the endoscope tip fluctuates drastically, and the maximum fluctuation distance is approximately 4.9 mm. To avoid collision, the drag velocity is slowed down when the endoscope tip is close to the nasal entrance (the density of the sampling points is increased). With the composite FRVF constraint, the trajectory of the endoscope tip is basically above the plane and inside the hyperboloid, the maximum constraint surface penetration distance is approximately 1.3 mm. At the intersection of the plane and the hyperboloid, the trajectory of the endoscope tip is smooth, verifying the effectiveness of the composite FRVF constraint.  
Figure 11: Comparison of experimental results of the GVF. (**a**) Without GVF constraint; (**b**) with GVF constraint.  
Figure 12: Experiment scene of the FRVFs.
## 3 Solution Methodology
### Pumping Characteristics  
Peristalsis is characterized by pushing the fluid forward against the pressure rise \(\Delta P_{\lambda}\). The characteristic of pumping can be analyzed in the form of \(\Delta P_{\lambda}\) versus \(\Theta\), shown in Figure 4a-c Pumping action divides the whole region into the four segments: pumping region (adverse pressure gradient \(\Delta P_{\lambda}>0\), Positive pumping \(\Theta>0\), backward/retrograde pumping (\(\Delta P_{\lambda}>0\), \(\Theta<0\)), augmented pumping (favorable pressure gradient \(\Delta P_{\lambda}<0\), Positive pumping \(\Theta>0\)) and free pumping (\(\Delta P_{\lambda}~{}=~{}0\)). Figure 4a portrays the \(\Delta P_{\lambda}\) against \(\Theta\) for different values of \(\Gamma\). It is perceived that pumping increases as \(\Gamma\) increases in pumping region (\(\Delta P_{\lambda}>0\)). But in co-pumping (\(\Delta P_{\lambda}<0\)), pumping decreases as \(\Gamma\) increases. It means that pumping rate is high for third order fluid under parameter \(\Gamma\) in comparison to viscous fluid \(\Gamma~{}=~{}0\). For free pumping (\(\Delta P_{\lambda}~{}=~{}0\)), curves coincide i.e., there is no difference between third-grade fluid and Newtonian fluid within the domain of \(-0.4\) to \(0.4\). Figure 4 bdescribes that \(\Delta P_{\lambda}\) decreases as \(m_{e}\) increases. This means additional pressure is needed to drive the third-grade fluid for maximum thickness of EDL. It physically declares that formation of EDL on charged surface opposes the third-grade fluid flow. Furthermore, Figure 4c illustrates \(\Delta P_{\lambda}\) decreases as \(U_{\text{Is}}\) increases. Here pumping is controlled through an electric field. Note that there is a linear relationship between \(\Delta P_{\lambda}\) against \(\Theta\), because of \(U_{\text{Is}}\).  
The distribution of pressure gradient \(dp/dx\) is plotted through Figure 5a-d for Deborah number \(\Gamma\), electroosmotic parameter \(m_{e}\), maximum electroosmotic velocity \(U_{\text{Is}}\) and volume flow rate \(\Theta\). Figure 5a portrays that by increasing the \(\Gamma\), magnitude of pressure gradient \(dp/dx\) increase. \(\Gamma\) being a physical parameter showing the Non-Newtonian nature, one can observe pressure gradients are higher for third order in comparison to Newtonian fluids. Figure 5b illustrates that by increasing the values of \(\Theta\), \(dp/dx\) decreases. Because there is an inverse relationship between \(dp/dx\) and \(\Theta\). Figure 5c highlights that by raising the values of \(m_{e}\). \(dp/dx\) decreases. It means that EDL presence in charged surface resists the flow of third grade fluid. Similarly, it is important that the characteristics of pumping can be modified by EDL mechanism and process of pumping can be systematized by thinning and thickening EDL width. Also, Figure 5d presents that the magnitude of \(dp/dx\) increases as \(U_{\text{hs}}\) increases.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 5 Galileo Signals  
Within the Galileo framework, two signals are transmitted in the E1 band. The Public Regulated Service (PRS) signal uses a high-order BOC(15,2.5) subcarrier and the E1 Open Service (OS). The former is confidential, thus it is out of scope of this paper. In addition, other GNSS signals of interest are broadcasted at lower bands, namely Galileo E6 and Galileo E5 signals.  
* _Galileo E1 OS Signal:_ The Galileo E1 OS signal is constructed as the combination of two CBOC modulations [30] (Chapter 4), a particular implementation of the MBOC modulation. This signal is separated into two components: the data component which transmits the navigation message, called I/NAV message [24], and the pilot component. The I/NAV navigation message [24] is broadcasted at 250 symbols per second, the data PRN sequence of 4092 chips lasts 4 ms and the pilot PRN (combination with a secondary code of length 25) lasts 100 ms. The subcarriers BOC(1,1) and BOC(6,1) in the data component are added in phase (+). On the other hand, the subcarriers BOC(1,1) and BOC(6,1) in the pilot component are added in antiphase (-). The combination of both sub-carriers is the so-called CBOC modulation. Then, the I/NAV navigation data message is first modulated by the ranging code and then by the two sub-carriers, each with a different weighting coefficient \(\alpha=\sqrt{\frac{10}{\Pi}}\) and \(\beta=\sqrt{\frac{1}{\Pi}}\). In parallel, the pilot component ranging code is also modulated by these sub-carriers.
* _Galileo E6 Signal:_ The signal transmitted in the Galileo E6 band has two components: the data component which transmits the navigation message (with a symbol rate of 1000 symbols/s), called C/NAV message [24], and the pilot component. For the data component, the PRN sequence of 5115 chips lasts 1 ms, and for the pilot (combination with a secondary code of length 100) 100 ms. This signal is QPSK(5) modulated, that is, in-phase and quadrature BSPK(5) signals.
* _Galileo E5 Signal:_ Within the Galileo E5 band, the Galileo System generates and broadcasts the E5 signal. This signal is separated with four signal components and it is allocated in two different frequency sub-bands, denoted as E5a (1176.45 MHz) and E5b (1207.14 MHz). Within each sub-band, one data component (in-phase) and one pilot component (quadrature) are transmitted [24]. The F/NAV (E5a-I) and I/NAV (E5b-I) navigation messages [24] are transmitted at 50 symbols/s and 250 symbols/s. The E5a-I and E5b-I PRN (combination with a secondary code of length 20 and 4, respectively) last 20 and 4 ms. The quadrature components PRN (combination with a secondary code of length 100) lasts 100 ms. The Galileo E5 signal is constructed as an AltBOC(15,10,10), or simply denoted as AltBOC(15,10), modulated signal. Refer to Section 6.1 for details on the AltBOC modulation.  
The ACFs for the different modulations, from the GPS L1 C/A BPSK(1) to the Galileo E5 AltBOC(15,10), are shown in Figure 2.
## 2 Related Theoretical Foundation and Modeling Framework
### Belief Reliability Theory  
The theory of belief reliability follows the three most basic principles of reliability science: Margin reliability principle, Degenerate eternity principle, Uncertainty principle [24]. These three principles can be expressed by the following three equations:  
1. Margin equation: \(E=G(P,P_{th})\).
2. Degradation equation: \(P=F(X,Y,t,T)\).
3. Measurement equation: \(R(t)=\mu(E>0)\).  
The margin equation, which represents the amount of the object performance allowance and the failure criterion, corresponds to the allowance reliability principle among them. The allowance is essentially the distance from the performance characteristic \(P\) to the performance threshold \(P_{th}\). If the margin is greater than 0, the product is reliable. The performance of the object \(F\) and the system of intrinsic properties \(X\), extrinsic properties \(Y\), and physical time \(t\) occur irreversibly along the degradation time vector \(T\), according to the degradation equation, which also corresponds to the principle of degradation eternity. The degradation equation describes the degradation law of product determinism. The metric equation describes the quantification of uncertainties in the allowance equation and relates to the uncertainty principle, providing the product reliability in accordance. As a result, it is possible to assume that the reliability is derived by quantifying the uncertainty using the law of certainty.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 2 Materials and Methods
### Isolation of Hydrocarbon Degrading Bacteria Using Heavy Crude Oil as Carbon Source  
One gram of the homogenized soil sample was mixed with 10 mL of distilled water and vortexed thoroughly; 5 mL of the supernatant served as an inoculum for the first enrichment containing 50 mL of enrichment medium (g/L) (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (1.5); NaH\({}_{2}\)PO\({}_{4}\). H\({}_{2}\)O (1.5); K\({}_{2}\)HPO\({}_{4}\) (4.65); KCl (0.1); MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (0.2); yeast extract (0.5); peptone (0.5); casamino acid (0.5); and trace elements (2.0) (pH = 7.1), and varying Bouri crude oil concentrations of 1.0, 2.0, and 3.0% (\(v/v\)) were added. Cultures were incubated with shaking for 14 d at 37 and 55 \({}^{\circ}\)C for the purpose of isolating hydrocarbon-utilizing bacteria from oil contaminated soil. Three different isolates (_Nocardiic guriacigeorgica, Bacillus_ species, and _Pseudomonas putida_) showing the best growth on heavy crude oil were selected and transferred to a mineral medium containing (g/L) NaNO\({}_{3}\) (4); Na\({}_{2}\)HPO\({}_{4}\) (0.5); KH\({}_{2}\)PO\({}_{4}\) (1.5); KCl (0.1); MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (0.2); CaCl\({}_{2}\) (0.01); FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (0.0011); and yeast extract (0.1). The medium was supplemented with 1 mL of the trace elements solution [13] and optimum concentration 2.0% (\(v/v\)) of crude oil. Then, 50 mL cultures were grown in 250 mL Erlenmeyer flasks and incubated with orbital shaking at 150 rpm for 21 d at 37 and 55 \({}^{\circ}\)C.  
The three bacterial isolates with the ability to grow on target hydrocarbons were assessed further for growth rate, crude oil biotransformation, viscosity reduction, and oil recovery. Growth of the isolated bacterial strains on different concentrations of crude oil was evaluated by measuring culture absorbance at 600 nm using a spectrophotometer (Eppendorf Biophotometer plus, Hamburg, Germany).
# MuIm: Analyzing Music-Image Correlations from an
## 2 Related Work
### State-of-the-Art Methods in Music-Image Correlation
#### 2.1.2 Non-Semantic Relation  
In order to bridge the emotional content of music-visual data, multiple studies [32; 15; 33] have employed end-to-end neural network models. Some of them [15; 33] focused on matching music with images using specific emotional categories. They achieved this by combining features from both audio and visual data and then processing them through a series of fully connected neural layers. Another study [32] took a different approach, using continuous valence-arousal (VA) emotions. It combined deep metric learning and multi-task regression techniques, creating a shared space for making cross-modal and single-modal VA predictions.  
However, these approaches have shortcomings. They often overlook the modalities' semantics and use photorealistic images for affective analysis, and their emotional labeling, whether categorical or dimensional, can be ambiguous for complex data samples, leading to contradictory emotional interpretations.
## 6 Performance Evaluations
### Smart Redirect or Jump Algorithm (SRJ)  
This algorithm overcomes DRN failures by using a combination of the jumping and redirection strategies described in the previous sections. We define as sibling DRNs to a particular DRN x, the DRNs that have the same parent DDN as x. We also define as secondary sibling DRNs to x the DRNs that have as parent DDN the secondary parent DDN (_i.e._, the DDN that is on the opposite side of the parent DDN with respect to x) of x. In this algorithm, each node contains information about the operational status of its sibling and secondary sibling DRNs. Consequently, before dispatching the message, it calculates the total necessary energy it needs to reach its parent DDN Expand the total energy it needs to reach its secondary parent Exsp. It then dispatches the message in the direction which takes the lower total energy to reach either the parent DDN or the secondary parent DDN. Specifically, if \(\text{Exp}\leq\text{Exsp}\) then the message is sent towards the parent DDN. Otherwise, the message is sent towards the secondary parent DDN. This algorithm relies on the information in the node to reduce the total energy consumed by the network for the transmission of the message. This information about failure status of DRNs is cached by the DRNs from participations in previous packet transmissions. More research is being conducted for the most efficient means of gathering such information by the DRNs.
# New Fuzzy Numerical Methods for Solving Cauchy Problems
## 2 Basic Concepts
### Numeric Scheme I: Modified Trapezoidal Rule Based on FT and NIM for Cauchy Problem  
In the present subsection, we will construct a numeric scheme of the more advanced method known as the Trapezoidal Rule. Recall that it is a one-step method with second-order accuracy, which can be considered as a Runge-Kutta method. We propose modification of trapezoidal rule based on FT and NIM for solving Cauchy problem. Modification of the trapezoidal rule can be improved by the FT to solve Cauchy problem (15). We contributed to numeric methods of Cauchy problem (15) by scheme provides formulas for the FT components, \(Y_{k},k=2,\ldots,n-1\), of the unknown function \(y(t)\) with respect to choose some power of the triangular (or raised cosine) generalized uniform fuzzy partition, \(B_{1},\ldots,B_{n}\), of interval \([a,b]\) with parameter \(h\) to approximate solution of Cauchy problem (15). The first, choose the number \(n\geq 2\) and compute \(h=\left(b-a\right)/\left(n-1\right)\), then construct the generalized uniform fuzzy partition of \([a,b]\) using Definition 7. Note that each function \(B_{k}\) spans over three nodes \(t_{k-1}\), \(t_{k}\), \(t_{k+1}\), \(k=2,\ldots,n-1\). Nevertheless, \(B_{k}(t_{k-1})=B_{k}(t_{k+1})=0\) and \(B_{k}(t_{k})=1\). Now, we apply the FT and NIM to Cauchy problem (15) and obtain the numeric Scheme I for \(k=1,\ldots,n-1\) as follows (see [3; 8] for technical details):  
\[\begin{split} Y_{1}&=y_{1},\\ Y_{k+1}^{*}&=Y_{k}+hF_{k}/2,\\ Y_{k+1}^{**}&=Y_{k+1}^{*}+hF_{k+1}^{*}/2,\\ Y_{k+1}&=Y_{k}+h\left(F_{k}+F_{k+1}^{**}\right)/2, \end{split} \tag{16}\]  
where  
\[F_{k} = \frac{\int_{a}^{b}f(t,Y_{k})B_{k}(t)dt}{\int_{a}^{b}B_{k}(t)dt}, \quad F_{k+1}^{*} = \frac{\int_{a}^{b}f(t,Y_{k+1}^{*})B_{k+1}(t)dt}{\int_{a}^{b}B_{k+ 1}(t)dt},\quad F_{k+1}^{**} = \frac{\int_{a}^{b}f(t,Y_{k+1}^{**})B_{k+1}(t)dt}{\int_{a}^{b}B_{k+ 1}(t)dt}. \tag{17}\]  
In the sequel, the approximate solution of Cauchy problem (15) can be obtained using the inverse FT as follows:  
\[y_{n}(t)=\sum_{k=1}^{n}Y_{k}B_{k}(t). \tag{18}\]