esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of MAG Welding Formation Based on Fuzzy Interpolation Reasoning
## 4 Welding Formation Prediction
### Weld Formation Prediction  
In this section, a welding formation prediction process based on a sparse rule base was designed using the method proposed in this article. The main steps of welding formation prediction based on fuzzy interpolation reasoning are as follows:  
Step1 Fuzzification: The same triangular fuzzy set as the input variable antecedent is used for fuzzification. The real value of the input variable can be fuzzified by the same triangular membership function with base width b and height 1. Suppose that the input variable of the welding current is \(x_{1}^{*}\); it can be fuzzified by the same triangular membership function whose base width is b and membership value at \(x_{1}^{*}\) is 1.  
Step2 Inference: First, calculate the distance between the input value and the antecedent of adjacent rule by (26) to (30), then construct intermediate rules. Then according to (33), \(B_{1}^{*}\) and \(B_{2}^{*}\) can be obtained. Finally, the inference consequence \(B^{*}\) can be drawn according to the maximum similarity by (35).  
Step3 Defuzzification: The result derived from the inference is a family of fuzzy sets, so it is necessary to determine a real value of the weld depth that corresponds to these fuzzy values. Here, the maximum membership principle is adopted for defuzzification. Let \(y^{*}\) be a real value of the output \(B^{*}\), and \(y^{*}\) can be obtained by  
\[B^{*}(y^{*})=sup_{y\in Y}B^{*}(y)=1 \tag{47}\]  
By following the above steps, welding penetration prediction can be achieved by inputting welding parameter values. Figure 9 shows the model of the welding formation prediction algorithm, with input parameters including welding current and welding speed. The proposed algorithm is used to predict the output value of welding penetration depth.  
In this example, five samples are employed to calculate the weld depth, as seen in Table 6, and the experimental results are obtained according to the sample parameters, where \(y^{*}\) and \(y\) are respective outputs of the inference value and measured value, respectively.  
Figure 9: The algorithm flow chart of welding forming prediction.  
We find that the average error between the inference result and the measurement result is 2.14%, and the maximum error is 3.52%, respectively, which indicates that the inference values derived from the algorithm are in good agreement with the measured results, as seen in Figure 10.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 3 Power Performance Monitoring Model  
### AdaBoost Methodology  
Since the 1990s, numerous researchers have been devoted to theoretical and algorithmic advancements in integrated learning. [PERSON] and [PERSON] [35] first introduced the concepts of strong learnability and weak learnability in 1988, and later in 1997, [PERSON] and [PERSON] [36] demonstrated that these concepts are equivalently efficient for machine learning. The learning task was shifted from constructing a single strong learner with high accuracy to boosting a series of weak learners with moderate accuracy. This led to the development of the adaptive boosting (AdaBoost) algorithm, which employs an iterative adaptation process for incremental improvement.  
As depicted in Figure 4, the procedure for power assessment using the AdaBoost strong regressor algorithm is outlined as follows:  
Figure 4: Schematic steps of the AdaBoost strong regressor algorithm.  
Figure 3: The CPV curve of principal components.  
Figure 2: Relationship among the principal components.  
Step 1: Initialize sample weights. Select m groups of training data from sample space beforehand. Set the initial weights for each group as follows:  
\[w_{1}(i)=\frac{1}{m}\quad i=1,2,\cdots,m \tag{6}\]  
where \(m\) is the number of weak regressors. Assuming the maximum number of iterations is \(N\), the initial iteration cycle is set as \(k=1\).  
Step 2: Create weak regressors. The weak regressors are created using the CART decision tree model. During the training of the \(k\)'th regressor \(G_{k}(x)\), both the training error \(\varepsilon_{k,\,i}\) and the prediction error rate \(p_{k}\) are calculated as follows:  
\[\varepsilon_{k,i}=|G_{k}(x_{i})-y_{i}| \tag{7}\]  
\[p_{k}=\sum_{i}\omega_{k}(i)|\varepsilon_{k,i}>\theta \tag{8}\]  
where \(\theta\) is the error threshold.  
Step 3: Calculate regressor weights. The weight \(\alpha_{k}\) represents the importance of \(G_{k}(x)\) in the composite regressor and is calculated as follows:  
\[\alpha_{k}=\ln\left(\frac{1}{p_{k}^{2}}\right) \tag{9}\]  
Step 4: Compute regressor weights:  
\[\omega_{k+1}(i)=\frac{\omega_{k}(i)}{\sum\limits_{i=1}^{n}\omega_{k}(i)}\left\{ \begin{matrix}p_{k}^{2}&\varepsilon_{k,i}>\theta\\ 1&\varepsilon_{k,i}\leq\theta\end{matrix}\right.\quad i=1,2,\cdots,m \tag{10}\]  
Step 5: If \(k<N\), proceed to Step 2.  
Step 6: Construct a linear strong regressor, as:  
\[G(x)=\sum\limits_{k=1}^{N}\alpha_{k}G_{k}(x) \tag{11}\]  
The advantages of the AdaBoost algorithm lie in two primary aspects. Firstly, it employs a mechanism that substitutes random training sample selection with the same set of training samples but with varying weight distributions. Secondly, it utilizes a weighted voting mechanism instead of the traditional average voting mechanism, enhancing the accuracy of the algorithm and making it is particularly suitable for solving real-world problems.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 3 Strain Transfer Ratio Experiment
### Strain Transfer Ratio Analysis  
The experimental system included a cantilever beam, polyimide FBGs, and a fiber grating interrogator, as shown in Figure 14. The FBG matrix fiber in the experiments was SMF-28C. It was recoated by polyimide material after being processed with UV laser-inscribing technology. Its effective reflectivity was greater than 90%. The interrogator in the experiments was SM125-500. This interrogator can identify a fiber grating sensor with a wavelength ranging from 1510 nm to 1590 nm. Its wavelength resolution is up to 1 pm and it can achieve synchronous four-channel 2 Hz sampling.  
The experiments were conducted in three groups. Loctite 401 adhesive was used to bond each bare polyimide of the FBGs of different central wavelengths along the longitudinal axis at the central surface of cantilever beam in each group. The bonding length was 25 mm and bonding width was 1 mm. After adhesive had set and was confirmed to be in good condition, one end of the beam was applied stress to produce theoretical strain \(\varepsilon_{S}\); meanwhile, experimental strain \(\varepsilon_{g}\) was measured and recorded through a fiber grating interrogator. The FBG was above the upper surface of beam after bonding, as shown in Figure 15 in which \(L\) represents the length of the cantilever beam, \(r\) is the radius of FBG, \(d\) is the adhesive thickness between fiber and the upper surface of beam, and \(h\) is the thickness of the cantilever beam. \(R\) represents the bend radius of the cantilever beam, but it has no use in the following discussion.  
In this way, the strain measured from fiber grating is definitely not equal to that of the upper surface of the cantilever beam, and the corresponding coefficient \(\alpha_{L}\) is provided by the following Equation (12):  
\[\alpha_{L}=1+2\frac{r+d}{h} \tag{12}\]  
Assuming the strain transfer rate of adhesive to be \(\alpha_{p}\), the relationship between the upper surface strain and measured strain of fiber grating on cantilever beam can be expressed as follows:  
\[\varepsilon_{g}=\varepsilon_{s}\cdot\alpha_{L}\cdot\alpha_{p} \tag{13}\]  
Figure 14: The strain transfer system.  
Figure 15: Diagram of the FBG pasted on the cantilever beam.  
The average strain transfer rate of bare fiber grating can be calculated as follows:  
\[\alpha_{p}=\frac{\varepsilon_{g}}{\varepsilon_{s}\cdot\alpha_{L}} \tag{14}\]
# Mass Processing of Sentinel-1 Images for
## 2 Study Definition and Datasets
### Ship Position Reports  
Terrestrial AIS from the Maritime Safety and Security Information System (MSSIS) network and from the Italian Coast Guard, and satellite AIS from up to three satellites from the Norwegian Coastal Administration/Norwegian Defence Research Establishment (FFI) has been received during the study. Overall, areas around the European countries are well covered by the terrestrial AIS data, while the coverage around African countries is poor. The satellite AIS data cover the entire Mediterranean Sea; however, on account of radio interference from land and message collisions due to the high intensity of the ship traffic, the satellite AIS detection rate is not high over the Mediterranean, especially over the northern part [25]. In addition, the satellite AIS data were only available for the first 13 months of the study. Long range AIS messages, designed to better cope with message collisions, were not used.
###### Abstract  
To meet growing energy demands, offshore wind turbines (OWTs) with higher energy outputs are being developed, presenting increased challenges for their foundation design. Over the past decade, extensive research on the design optimization of OWT support structures has significantly reduced the anticipated costs of offshore wind farm development. Various design methods have been developed and applied in practice, each with its own advantages and limitations. In this study, 3D finite element (FE) modeling, validated against the measured response of a large-scale test monopile, is used to investigate the lateral load response of monopiles with different geometries and slenderness ratios in small and large displacements. The results are compared to the standard p-y method, and specific behavioral and design aspects of large-diameter monopiles, such as the moment contribution ratio from different resisting components and the minimum embedment length criteria, are evaluated and discussed. The results showed that the maximum and minimum differences between the 3D FE modeling and one-dimensional (1D) DNV p-y method are 41% and 11% for large displacements, and 32.5% and 13.3% for small displacements, respectively. As the slenderness ratio increases, the discrepancy between the finite element (FE) modeling results and the 1D DNV p-y method decreases, with an average difference of about 13% across all monopile diameters at an L/D ratio of 10, in both small and large displacements, indicating the reasonable accuracy of the 1D method for slenderness ratios of 10 and above. Among the three minimum embedment length criteria examined, the DNV recommended and vertical-tangent criteria offered shorter embedment lengths. The primary resisting moment across all slenderness ratios comes from the distributed lateral load along the monopile shaft (MCRp-y), which increases as the L/D ratio increases.  
monopiles; offshore foundations; lateral loading; p-y curves +
Footnote †: journal: Applied  
1
# A Method to Reduce Non-Nominal Troposphere Error
## 5 Method to Reduce the Non-Nominal Troposphere Error  
Based on the above analysis, under non-nominal troposphere, once the satellite geometry is nearly optimal, the airborne VPL increases as the number of visible satellites increases. Thus, it can be inferred that satellite selection can achieve a balance between the satellite geometry and the non-nominal troposphere error. Bellow, several selection methods will be analyzed, and then a satellite selection method to reduce the non-nominal troposphere error and minimize the VPL will be proposed.
## 2 Materials and Methods
### Methodology of Numerical Simulation
#### 2.2.3 Design of Tool Path  
One of the critical process parameters associated with deposit quality is a tool path, because it guides the motion of the laser head [27; 28; 2]. Characteristic features of a tool path, such as discontinuous deposition, sharp turns at the edges of a given geometry, and overlap ratio can lead to defects, non-uniform stress distribution and part failure. Figure 7 illustrates three different tool path designs commonly used in LMD-w. The letter n indicates the current layer and n+1 represents the subsequent layer. The deposition repeats the sequence of 1\(\rightarrow\)2\(\rightarrow\)3\(\rightarrow\)4 until the end of the process. A unidirectional path, as in Figure 7a, is the simplest and most common tool path [29]. The print head always moves from the left to the right direction at every layer. A bidirectional path, as in Figure 7b, alternates the direction at every track on a layer. It potentially generates a uniform temperature distribution at the left and right edges, but it may produce overheating at the corner due to sharp turns. The last path strategy is a bidirectional path with 180\({}^{\circ}\) rotation at every layer, as in Figure 7c. It can avoid the accumulation of overheating caused by a repeated starting location. For instance, track 3 is melted at n+1 th layer but is next to track 2 of the previously deposited nth layer.
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 2 Challenges and Solutions of Wind Shaft Wall Inspection  
### The Challenge of Wind Shaft Wall Inspection System  
In view of the fact that there are few investigations on the working conditions of air shafts in coal mines, this paper, based on the on-the-spot investigation of air shafts in coal mines and the experiences of underground workers, knows that the air shaft is different from the main shaft and auxiliary shaft in coal mines, as shown in Figure 1. Similarly, the longitudinal depth of the air shaft is about 500 m, and the wind shaft and its auxiliary equipment have the following characteristics: (1) the humidity at the site is over 100%, the water vapor is large, the lighting facilities are lacking and the visibility is extremely low; (2) the wind speed in the shaft can reach 6-8 m/s, and the particles in the return air lane are obvious; (3) one side of the shaft is provided with a refrigeration grouting pipeline and a ladder room for manual inspection, which occupies a large space; (4) the exhaust air of the wind shaft contains a lot of toxic, harmful, flammable and explosive gases such as carbon monoxide and gas; (5) a long longitudinal distance, many strata crossing and complicated internal environment may easily lead to defects such as cracks, peeling and even deformation of the shaft lining.  
Figure 1: Environment of wind shaft.  
Therefore, the inspection robot should not only meet the needs of underground use, but also adapt to the special working conditions of the wind shaft. Considering the complex internal environment, bad working conditions and extremely low visibility of the shaft, it is difficult to obtain a clear image of the shaft wall, and the long longitudinal distance, small transverse radius and high wind speed of the shaft bring great challenges to the stable operation of the robot. Therefore, the following requirements are put forward for the inspection robot: (1) high adaptability: the inspection robot can not only obtain the image information of the shaft wall in the complex wellbore environment, but also stably complete the inspection operation; (2) high load: on the premise of meeting the inspection work, it has high load capacity; (3) strong endurance: it can meet the inspection requirements of a longitudinal long-distance wind shaft; (4) accuracy: the inspection system should be able to accurately obtain the position of the robot and the observed object in real time, and the detection algorithm should be able to process the borehole wall image completely.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Internet of Things and Smart Cities
#### 3.3.1 IoT and Smart Cities Protocols  
To ensure effective communication among network nodes, efficient message delivery, and overall IoT operation, the following DTN-related protocols are commonly utilized:  
* _Bundle Protocol (BP)_[7]: Enables the transmission of data in challenged environments by encapsulating data into bundles and routing them opportunistically.
* _Spray and Wait (SNW)_[113]: Distributes messages by spraying multiple copies and then waiting for successful delivery, thus storing and forwarding messages opportunistically.
* _Epidemic Routing_[5]: Disseminates data by reproducing and pushing messages to all the nodes it encounters, thus ensuring final delivery through opportunistic encounters in latency-tolerant networks.
* _MaxProp_[114]: Prioritizes message forwarding on the basis of maximum probability for a successful delivery, thus optimizing the efficiency of communication in delay-tolerant networks.
* _Prophet_[115]: Utilizes probabilistic forwarding based on historical encounter information for improving message delivery in intermittent networks.
* _MQTT:_ A widely used TCP-based publish/subscribe protocol within IoT deployments. It is proposed to be combined with the DTN and IBR-DTN for real IoT Sensor Networks (MQTT-SN) [116] and IoT environment cases [117]; to be utilized complementarily to DTN under various disruption patterns [118; 119] utilizing the 5.0 MQTT version ([[https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html)]([https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html)) accessed on 4 March 2024); run over the _QUIC_ protocol (i.e., _MQTT over QUIC_) [120]; or leveraged as the next generation IoT standard protocol, thereby offering substantial performance advantages and resource footprint reduction.
* Licklider Transmission Protocol (LTP) [121].
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors
### Crater Navigation
This may further reduce performance, and is accounted for in the studied orbital propagation and filter scenarios of Section 4.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 3 Experimental Evaluation  
### Experimental Setup  
In this paper, the MOD-Bi-LSTM model is constructed using TensorFlow framework, and the model is trained on NVIDIA Titan XP graphics card. Because the test data in this study contains 5 features, the input size of the MOD-Bi-LSTM model is set to 5. Additionally, the number of hidden units is 128, the dropout rate is 0.5, the batch size is 1000 and the epoch is set to 256. The ReLU activation function and Adam optimizer are used in the model, and the initial learning rate is set to 0.001.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 2 Planar Arrays with Regular Lattices  
For the sake of simplicity, in the following sections we will focus on the radiation of planar arrays of isotropic elements: we will deal with the synthesis of the \"array factor\" (AF) [10]:  
\[F(u,v)=\sum_{n=1}^{N}a(n)e^{i\beta(ux_{n}+vy_{n})} \tag{1}\]  
where \(\beta=2\pi/\lambda\) is the free-space wavenumber, \(N\) is the number of radiating elements, \(a(n)\) is the excitation of the \(n-\)th element, \((x_{n},y_{n})\) are the coordinates of the \(n-\)th radiating element, \(u=\sin\theta\cos\phi\) and \(v=\sin\theta\sin\phi\), with (\(\theta\),\(\phi\)) the spherical coordinates with respect to the broadside direction.  
It must be recalled that the use of the AF for pattern synthesis is convenient also when dealing with \"real\", non-isotropic, radiating elements. In this case, we could apply the approach proposed in [40] to properly modify the power pattern mask guaranteeing the satisfaction of the side-lobe level specification for any direction within the maximum scanning angle.  
Let us now consider the synthesis of a pencil beam with prescribed side-lobe level employing a regular lattice array. If we require that the beam scanning is performed using a linear phase shift of the excitations, the synthesis problem can be written as:  
\[F(0,0) = 1 \tag{2}\] \[F(u,v) \leq SLL\quad for\quad w_{1}\leq w\leq 1+w_{s} \tag{3}\]  
where \(w=\sqrt{u^{2}+v^{2}}\), SLL is the desired side-lobe level, and \(w_{1}\) defines the circular footprint of the pencil beam, which establishes the beam-width, and \(w_{s}=\sin(\theta_{s})\) with \(\theta_{s}\), the angle defining the scanning cone for the beam. Roughly speaking, we are increasing the region of the \((u,v)\) plane where we impose the pattern constraints, to take into account the scanning of the beam.
## 4 Modeling and Prediction Results
### Data Processing and Model Building  
During the construction process of a large-span steel box concrete tie arch bridge, it is susceptible to external environmental factors, material parameters, and construction techniques, leading to discrepancies between the actual state and the design state of the bridge. Variations also exist between the measured values of bridge stress, arch rib deflection, and hanger cable strength compared with their design values. The basic parameters of the hanger before tensioning are presented in Table 1; as the bridge is symmetric in structure, only one side of the hanger data are provided. To predict the hanger cable force at the completion stage, this study utilized a dataset comprising 10 groups of 90 sample data measured after lifting the bridge; D1 and D11 hangers were excluded due to their short length. Input parameters for modeling include hanger length, measurement frequency, line density, and elastic modulus--all significantly impacting hanger cable force. Fuzzy data were cleaned by addressing poor frequency identification resulting from excessively short hangers.  
The measured fundamental parameters of the bridge were integrated with the finite element model to obtain the sample dataset, and the cable force values obtained from the finite element simulation model were utilized as the actual measured values. The modeling environment employed was Windows 10 22H2 operating system with Intel (R) Core (TM) i5-9300 H CPU and 8 G RAM. python 3.8 is used as the environment setting.  
As shown in Figure 2, the measured sample data were partitioned into training and test sets. The first nine sets of 81 sample data were used as training samples, while the remaining one set of nine sample data was utilized as test samples. Prior to model training, feature scaling and polynomial processing were conducted on the dataset. Initially, the original data underwent feature scaling using StandardScaler to normalize the features by calculating their mean and standard deviation, resulting in a mean value of 0 and a standard deviation of 1. Subsequently, the normalized features were transformed using PolynomialFeatures to create quadratic features that include squares of the original features and their products, thereby expanding the feature space and enhancing model fitting for nonlinear relationships. The model was trained by minimizing mean square error on the training set, followed by running the QPSO algorithm to determine optimal parameters (penalty factor \(c\) and kernel width \(\gamma\)) for the SVR model. These optimal parameters were then assigned to the RBF-SVR model for prediction.