esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Magnetic Fields Induced by the Wake of an Underwater Vehicle
## 1 Introduction
However, previous researchers have used simplified flow models to study wake magnetic fields without considering the actual hydrodynamic conditions of ships or submarines. The extensive use of computational fluid dynamics (CFD) technology to simulate hydrodynamic phenomena has expanded the study method of wake magnetic fields, thus enabling the detailed investigation of the flow field and magnetic field characteristics. Additionally, most previous studies only considered the magnetic field generated by the surface Kelvin wake but neglected the turbulent wake magnetic field generated by the submarine. Although the surface Kelvin wake can exist for a long time, its fluctuation will decrease with an increase in the submarine diving depth. Furthermore, the turbulent wake is usually much stronger than the surface Kelvin wake and only slightly changes with an increase in the diving depth. Therefore, it is necessary to study the turbulent wake magnetic field of submarines. The magnetic field characteristics of the turbulent submarine wake were studied using numerical simulation and were compared with the results of previous studies. The methods and conclusions of the paper can provide an important basis for underwater target detection and recognition. Progress has been made in the following sections. 1. We used a multi-physical field simulation method and combined dynamic overset grid methods to calculate the magnetic field generated by the wake during submarine movement. This method can obtain substantial information about the wake and magnetic fields. 2. Based on the numerical simulation results, we analyzed the space-time distribution of the wake magnetic field and described the time-frequency domain characteristics of the magnetic field at different monitoring points during submarine navigation. 3. We studied the relationship between the velocity, acceleration, and wake magnetic field characteristics in submarine motion and proposed the gravity center frequency and half energy bandwidth to represent them.
## 4 The Proposed Method
### Making Prediction via Similarity  
For the TPD task, we aim to find a set of users who will generate more people to visit the given POI. We consider quantifying two factors to determine whether a user will visit the given POI. One is user preference \(uf(u_{1},u_{2})\), given by:  
\[uf(u_{1},u_{2})=sim(f(u_{1}),f(u_{2})),\]  
where \(u_{1},u_{2}\in U\). The other is POI preference \(pf(u,p)\), given by:  
\[pf(u,p)=sim(f(u),f(p)),\]  
where \(u\in U,p\in P\), \(f(\cdot)\) is the embedding vector, and \(sim(\cdot)\) is the cosine similarity to compute the relation between two input vectors. Two users with similar preferences will have similar embedding vectors. So user preference is devised to depict the tendency of being influential between users. Moreover, if the similarity between the embedding vectors of a user and a POI is high, we consider that the user has a higher chance of visiting the POI. Hence, POI preference is designed to estimate the POI visiting potential/desire of a user.  
To find the future targeted propagators, we develop a _contagion_ scoring measure by combining user preference and POI preference.  
\[contagion(u)=\sum_{u_{i}\in N_{G_{1}}(u)}uf(u,u_{i})\cdot pf(u_{i},p).\]  
For the TPD task, we rank all users' contagion scores, and select the top-\(K\) users as the discovered propagators, i.e., those that can generate the most people to visit the given POI.  
For the TCD task, we devise a _visiting_ scoring measure considering POI preference:  
\[visiting(u)=pf(u_{i},p).\]Then we rank all users' scores, and select the top-\(K\) users as the potential customers for the given POI.  
The novelty of our similarity-based measures lies in its inductive predictability on LBSN. First, measuring the similarity to identify targeted propagators and potential customers is an unsupervised learning approach. Since we do not have any label data about future propagators and customers, the similarity-based unsupervised method can properly generate the results without supervision information. This would be especially useful for location promotion because we cannot have the labeled promotion data before campaigns.
# An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
## 2 Dynamic Model of Horizontal Vibration of High-Speed Elevator
### Multi-Objective Genetic Algorithm  
The vibration suppression problem of elevators is transformed into a multi-objective optimization problem shown in Equation (16), which achieves optimal system performance with minimal control costs. FOPID controllers introduce more parameters, which brings more degrees of freedom, but also increases the difficulty of controller design. TraditionalPID controller design methods are challenging to achieve the desired results for FOPID controllers. Therefore, MOGA is selected to optimize the parameters of FOPID controllers.  
**Definition 1.**_Pareto solution._  
Select the objective function as \(F(X)=\{f_{1}(x),f_{2}(x),f_{3}(x),\cdots\cdots f_{k}(x)\}\), \(k\geq 2\); For two solution vectors \(x_{0}\) and \(x_{1}\) located in the solution space, \(\exists i\in[1,k]\), \(f_{i}(x_{0})>f_{i}(x_{1})\), and \(\forall i\in[1,k]\), \(f_{i}(x_{0})\geq f_{i}(x_{1})\), It can be considered that the solution \(x_{0}\) Dominant Solution \(x_{1}\), or solve \(x_{0}\) dominates solution \(x_{1}\). If there is no other solution in the solution space that is superior to this solution, the solution is called a Pareto solution. A Pareto optimal solution set is represented as follows:  
\[PS=\{u\in\Omega|\
exists v\in\Omega,v\succ\mathrm{u}\} \tag{17}\]  
where \(u\) is the solution vector and \(\Omega\) is the solution space.  
**Definition 2.**_Noninferior solution sorting._  
Noninferior solution ranking is important in MOGA, which determines the search direction of optimization algorithms. The specific process is as follows: 1. Let all solution spaces be S and compare them to find a noninferior solution set \(S_{1}\). 2. Make S = S -\(S_{1}\). Repeat the comparison process to find the non-inferior solution set \(S_{2}\). 3. Repeat process 2 until S is an empty set. \(S_{1}\) is called the first order noninferior solution, \(S_{2}\) is called the second level noninferior solution, and so on. Obviously, the solution in \(S_{1}\) is the best individual.  
The process of MOGA is shown in Figure 4:  
As mentioned in Section 3.1, the vibration suppression problem of elevators can be simplified into two control objectives: system performance and control cost. The parameters of the controller (Kp, Ki, Kd, etc.) are the variables that need to be optimized. Take two control objectives as the objective functions of a multi-objective genetic algorithm, and iteratively optimize all controller parameters as the population of the algorithm, ultimately obtaining the optimal controller parameters.  
Figure 3: Fractional PID controller plane.
## 2 Research Methodology
### Description of Equipment  
Three different types of NDTs were used to accomplish the objective of this study. These were:  
1. GPR: Geophysical Survey Systems, Inc. (GSSI), Nashua, NH, USA, Structure Scan Mini GPR, as shown in Figure 7a, with an antenna frequency of 1.6 GHz and penetration depth of about 50 cm. RADAN software was used to process and analyze the scans obtained from the GPR;
2. Spectrometer: in this study, the Exemplar LS Spectrometer, as illustrated in Figure 7b, which has a wavelength range of 0.2-0.85 \(\upmu\)m, was used. The Exemplar LS Spectrometer displays real-time data and requires the use of specific software for data analysis (BWSpec software); and
3. IR Camera: the camera used in this study was the FLIR T420 depicted in Figure 7c, which has a wavelength range of 7.5-14.0 \(\upmu\)m. The device has a native resolution of 464 \(\times\) 348. The minimum focus distance is 24\({}^{\circ}\) lens: 0.5 m. IFOV in the manufacturing facility is 42\({}^{\circ}\) lens. However, the focal length is not provided in these cameras because of the relationship between the horizontal FOV and focal length.
## 5 Discussion
### Geochemical Characteristics of Garnets in Skarn
#### 5.2.2 The Y/Ho Ratio and Its Water-Rock Ratio Indication  
The Y/Ho ratio usually has important geochemical implications, as Y and Ho have similar ionic radii and valences and thus exhibit similar geochemical behaviors. [PERSON] (1991) suggested that Y and Ho may exhibit significant fractionation in aqueous solutions, but partial melting or fractional crystallization of magma does not cause a significant fractionation of Y and Ho [53]. Therefore, when garnets are of hydrothermal origin, their Y/Ho  
Figure 14: Andradite end member (And) vs. Y/Ho diagram.  
Figure 13: The relationship between total REE and Mg\({}^{2+}\), Fe\({}^{2+}\), Fe\({}^{3+}\), Al\({}^{3+}\), Y in the garnet from TS-10 profile. (**a**)Al\({}^{3+}\) vs. \(\Sigma\)REE diagram, (**b**) Fe\({}^{3+}\) vs. \(\Sigma\)REE diagram, (**c**) Y vs. \(\Sigma\)REE diagram, (**d**) Mg\({}^{2+}\) vs. \(\Sigma\)REE diagram, (**e**) Fe\({}^{2+}\) vs. \(\Sigma\)REE diagram, (**f**) U vs. \(\Sigma\)REE diagram. Atoms per formula unit (apfu) calculated based on 12 oxygen atoms as cation number.  
ratios will deviate significantly from the Y/Ho value of chondritic meteorites (Y/Ho = 28). An imbalance in the chemical composition of the original rock or changes in the fluid composition during the crystallization of garnets can lead to a lack of correlation between Y and REEs. When the chemical composition of the original rock is relatively balanced or when changes in the fluid are small, Y and REEs show a positive correlation [14]. In our study, the Y/Ho ratio of the garnet in sample TS-10-8 from the inner skarn of the meta-igneous rocks is close to the value of chondritic meteorites and varies only slightly around 28 (Figure 14), indicating that the garnets were in a state of chemical equilibrium and were not significantly affected by fluids. The Y/Ho ratios of garnets in TS-10-4,5,6,7 from the outer skarn vary greatly and deviate significantly from the values of those in chondrites, suggesting an increased water-rock ratio during garnet formation. Furthermore, the formation temperature of andradite is lower than that of grossularite, as reported by [PERSON] (1994) [54]. Consequently, as the temperature declines, hydrothermal fluids rich in metal elements separate from the magma, leading to an increased water-rock ratio. This process, in turn, contributes to the formation of clinopyroxene and significant variations in Y/Ho ratios at relatively lower temperatures.
## 3 Results and Discussion
### Hardness Distribution
#### 3.2.3 Optimization of Hardness  
Figure 11 demonstrates the outcomes of the RSM optimization performed on the hardness, alongside the factors that were pertinent to this analysis. The hardness optimization objective was configured to \"in range,\" using \"maximize\" as the solution destination, and the anticipated result of the desirability function was in the format of \"larger-is-better\" attributes. The optimal conditions were found to be P (A) = 875.087 MPa, (B) H/D = 0.87, and (C) Al\({}_{2}\)O\({}_{3}\) % = 9.845%. This led to the highest hardness value possible, which was 121.199 Hv.  
The use of a genetic algorithm technique helped to determine the objective functions for each response. The maximizing of the hardness that is represented by Equation (6) was used as the fitness function, and it was applied to the boundary condition for the AMC. The best Vickers hardness estimate obtained by the GA at 900 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\) was 126.15 Hv, as shown in Figure 12a. Based on the hybrid DOE-GA data, which are displayed in Figure 12b, it was determined that the material with the highest hardness had a value of 126.15 Hv at 900 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\).  
Figure 11: RSM optimization results of hardness for AMC.  
Figure 12: Optimal hardness by (a) GA and hybrid (b) DOE-GA.
## 3 Results and Discussion
### 3.3. Intact Mass Analysis Is Used to Reveal Expected/Unexpected Post-Translational Modifications
The poor high mass performance of MALDI arises from fundamental differences in ion formation in comparison to ESI and how this affects the efficiency of micro channel plate ion detectors common to both types. Large, singly charged MALDI ions acquire much lower velocity during TOF acceleration than the equivalent multiply charged ESI ion. The impact velocity as ions strike the detector determines the efficiency at which secondary electrons are generated and hence the amplitude of the detector current [30]. ESI instruments require calibration only up to \(m/z\) 2700 because all charge states for a denatured protein fall within this range. In contrast, MALDI instruments require calibration up to an \(m/z\) value beyond the mass of the protein being measured. Monoisotopic masses are unresolved and few, if any, good high mass calibrants exist. Typically, BSA or IgG are used, despite the fact that these calibrants are heterogenous and display broad peaks. Moreover, MALDI analysis of proteins requires a non-homogeneous matrix such as ferrulic acid, needing manual laser positioning and is thus not suitable for automation and high throughput.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 4 Test Setup
### Finite Element Model
Figure 7: Example of designed transfer functions wherein **(a)** three different source points are compared and **(b)** the maximum, average, and minimum amplitudes across all of the applied transfer functions used in an individual test are shown. alarm at 4 m. In these tests, since both signals at 3 and 4 m have the same characteristics, they are both considered defect signals even though the second signal is a false detection.
## 6 Real Field Deployment of Systems Based on DAS+PRS  
Almost all the approaches presented in this review regarding to DAS+PRS are evaluated on _controlled_ conditions with respect to data acquisition ([22; 23; 24; 25; 26] are the only exceptions, but the authors of [26] do not provide enough details in its experimental procedure). In this sense, both the training, validation, and testing data were first recorded and then, the system evaluation was carried out in an _off-line_ mode.  
After the _off-line_ system evaluation, and once the system performance has accomplished the required performance level, facing at real field deployment of surveillance systems that employ DAS+PRS is the next step. This implies that all the system modules must run in an _on-line_ mode, and in real time (or close to real time).  
There are several issues dealing with real field deployment that must be addressed, which are presented next.
# Automatic Assembly Technology of Dense Small Screws for Flat Panel Parts
## 2 Methods
### The Visual Servo-Positioning Strategy
#### 2.3.2 The Servo-Positioning Strategy  
First, the photograph of the sub-area is divided according to the camera's field of vision and workpiece size. Then, the manipulator is guided to capture pictures above the center of each area alternately. To ensure that all the threaded holes in each sub-area are within the camera's field of vision, each sub-area is allowed to overlap to some extent, as shown in Figure 11, A-D are examples of sub-areas.  
Figure 11: Schematic of photographed sub-areas of workpiece.  
Figure 10: Establishment of reference coordinate system.  
After obtaining the threaded hole information from the image, a rectangular frame was used to approximate the hole's position. Moreover, the assembly sequence was given. As shown in Figure 12, \"+\" is the image center, and \"\(\times\)\" is at the center of the red rectangle. Approximate positioning is performed according to the rectangular central coordinate information, and the distance between the image center and target threaded hole is calculated. This process is called the approximate positioning of threaded holes. The purpose is to move the screws that must be assembled near the center of the camera's field of view (i.e., move the camera from \"+\" to \"\(\times\)\" to prepare for precise positioning).  
After approximate positioning, the center of the threaded hole appears near the image center. The sources of positioning errors are due to the (1) influence of the filming angle (a small gap exists between the actual and measuring positions of the center of the threaded hole) and the (2) absolute positioning error of the manipulator. However, owing to the small measurement error and short-range motion of the manipulator, the positioning error is negligible. The use of a suitable industrial camera and manipulator ensures that the positioning error is less than the screw radius. When the threaded hole closest to the image center is used as the hole for screw assembly, the correct threaded hole can be found.  
At this point, the center of the threaded hole is precisely positioned, as shown in Figure 13. The distance between the center pixel coordinate of the threaded hole and that of the image is gradually reduced by several iterations until the error becomes less than visual positioning accuracy. This ensures that the screw can be precisely installed to prevent damage to the threaded hole. The number of specific iterations is related to the absolute positioning accuracy of the manipulator. The higher the absolute positioning accuracy, the fewer the iterations.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 4 Experiments and Analysis  
### Description of the Experiment  
The experimental platform utilized Python 3.8 and the Pytorch framework (version 1.7.1 from Facebook), with computations performed on a computer equipped with an AMD Ryzen 9 5900 HX CPU and 16 GB of RAM. Pycharm served as the Integrated Development Environment (IDE).  
To validate the fault prediction model proposed in this paper, the experimental subjects were 10 sets of civil community elevators undergoing trial operations in a project facilitated by the cooperative unit. These elevators are straight lifts with a rated load of 1000 kg and a rated speed of 1.75 m/s, each servicing 11 floors.  
Table 1 presents a synthesized dataset utilized for the diagnosis of elevator faults, which includes measurements such as the current, vibration, and voltage. These are key physical attributes relevant to elevator function. While only three types of measurements are highlighted here, the dataset actually comprises 10 different types of measurements. The dataset comprises the following: the current (A), electrical current readings with a mean of 10 Amperes (A) and variability indicating different operational states or possible faults; the vibration (Gs), vibration levels measured in G-forces (Gs), for which a mean of 0.5 Gs with variations can point to mechanical issues such as misalignments or imbalances; the voltage (V), voltage readings averaging 220 Volts (V), deviations from which might signify electrical problems; and the fault type, categorical data labeling the fault type, including \"None\" for no fault and other values such as \"Motor\", \"Bearing\", \"Brake\", and \"Door\" for specific fault conditions.  
During the experiments, to ensure normal equipment operation, the terminal automatically read and uploaded 15 types of measurement data from the control cabinet and sensors. This dataset encompasses various measurements, including but not limited to the rated voltage, starting current, and traction machine status. The sampling period extended over two months, targeting elevator usage during morning and evening peaks of the trial operation, with a sampling frequency of once every 5 seconds. After abnormal data were filtered out, 21,535 datasets were obtained.  
The operational signals of the elevator, after undergoing data processing for feature extraction, yielded a total of 3000 elevator fault operation samples, as depicted in Table 2. The fault labels were manually annotated by engineers experienced in elevator fault monitoring, with each sample type comprising 500 sets, including 150 fault samples. Out of these, 300 sets per sample type were selected for training (including 100 fault samples), and 200 sets per sample type for testing (including 50 fault samples). Each fault type consisted of 15 samples, as shown in Table 3.
## 2 Experimental Setup and Methodology
### Image and Data Processing  
All experimental images were processed by self-developed programs based on Python-OpenCV to ensure the consistency of the data obtained and to avoid the error of manual measurement. As illustrated in Figure 2, the experiments' original color image (Figure 2a) was first converted to a grayscale image (Figure 2b), and then the grayscale image was converted to a binary map (Figure 2c). The shape and position parameters of the flame were obtained by multiplying the coordinates of the white pixel points in the binary map by a scale calibrated in advance (Figure 2d). Finally, the frequency and amplitude of flame oscillation were taken using Fast Fourier Transform in MATLAB. In this study, each experiment was repeated three times, then the average value of the three results was taken as the analysis object; its standard error is displayed in results in the form of error bars.
## 3 Routing in Satellite Networks
### Routing Protocols in LEOs
#### 3.3.4 Explicit Load-Balancing Routing Protocol (ELB)  
The rate of satellite use in space is increasing rapidly every day. We can predict that it will become one of the key legs of communication in the future. For this reason, traffic management in satellite networks needs to be directed intelligently from now on. This is where the proposed ELB protocol comes into play. The purpose of the ELB protocol is to obtain congestion information of neighboring satellites. In this way, intelligent communication can be achieved between satellites. A satellite that will soon increase congestion may ask its contiguous satellites for data reduction transmission rates. In reply to this request, the contiguous satellites take the action of searching for new, less congested routes and transmitting data over these routes. The purpose of this protocol is to reduce packet loss, prevent congestion, and achieve higher efficiency [107].