esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### MFNN Model  
For mobility prediction, the Multi-layer Feed Forward Network (MFNN) model is acknowledged. It contains input, hidden, and output layers. The sub pattern is a training data pair with cell number and time spent as input and the next cell location with time as a desired output. In Table 2, for example, the training sub pattern \(x_{1}\), \(x_{2}\),..., \(x_{k}\) represents input, whereas \(x_{k}\) + 1 is the intended output. The prediction order, \(k\), is determined by the UE's movement characteristics as well as the magnitude of the recorded mobility patterns. In other words, the intended output is assigned to the final element of the sequence. Table 2 assumes that the number of node movement examined in the input training data is four: i.e., \(z\) = 4, and \(X_{j}\)--each movement requires 2 quantities for representing User entity's cell number and time.  
In the proposed technique, one neuron contains the cell number and time. As a result, there are four neurons in the input layer and three in the hidden layer. The neuron's no. at hidden layer is equal to the pattern's no. used for training and length of the sub-pattern. At output layer, neuron's no. depends on two parameters: cell number and time.  
On the basis of trial and error, the value of each parameter can be decided. In the case of designed MFNN, the learning parameters are set as 0.8 and 0.008 from input layer to hidden layer and hidden layer to output layer, respectively. Tanh is used as an activation function. The number of iterations is selected as 20,000 to avoid jam for MFNN at local minima.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UED1** & **Cell** & **Dwell** & **Cell** & **Dwell** & **Cell** & **Dwell** \\
**ID\({}_{\mathbf{1}}\)** & **Time\({}_{\mathbf{1}}\)** & **ID\({}_{\mathbf{2}}\)** & **Time\({}_{\mathbf{2}}\)** & **ID\({}_{\mathbf{3}}\)** & **Time\({}_{\mathbf{3}}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Format of Collected Data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub-Pattern** & **Input Training** & **Input Training** & **Input Training** & **Input Training** & **Desired Output** \\
**Data 1** & **Data 2** & **Data 3** & **Data 4** & **Desired Output** \\ \hline
1 & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) \\
2 & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) \\
3 & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\
## 6 Discussion
### Sensitivity of Results to the Pipette-Surface Boundary Condition  
As indicated in Figure 8, the excitation pressure and the generated displacement on the sample surface is highly sensitive to the boundary condition. Since the excitation pressure and the generated displacement are the two fundamental terms of Equation (1), the calculated elastic modulus also has a high dependency on the boundary condition. The sensitivity comes from the strong dependency of the generated displacement amplitude on the sample surface to the contact compression force on one side and to the excitation pressure amplitude on the other side. As discussed earlier, the compression force makes the sample surface harder and causes the measured displacement decline by applying higher contact pressures. Furthermore, when the size of the gap gets larger, the excitation pressure on the surface falls and the generated displacement amplitude also declines in this case. Therefore, controlling the contact compression force or the gap size is necessary for the accuracy of this presented method. The pipette with gap boundary condition is our preferred method because the compression force does not exist to affect the hardness of the surface and controlling of the gap size is much easier than the compression force. By monitoring the excitation pressure amplitude from the microphone input by the Labview automation program continuously, the gap size could be controlled quite well. An excitation pressure value could be measured for the desired gap size and be defined as an index for the acceptable gap size in the automation program. The pressure is expected to be almost constant in the desired gap size while it might increase or decrease instantly if the pipette touched the surface or the gap size became large. During the measurement, if the excitation pressure goes lower or higher than the threshold index, the automation program shows that the gap size in the measurement isnot appropriate and results are not acceptable. Using the connected pipette is not suggested by this study because this method is more sensitive to the exact boundary condition than the pipette with gap method. The compression force has a significant effect on the correctness of the calculated elastic modulus of the sample surface, especially at higher contact pressures. Moreover, controlling the compression pressure and correcting the errors caused by the pressure is much more complicated compared to the pipette with a gap boundary condition method.
## 2 Materials and Methods
### Measurement
#### 3.1.6 BBR Test  
BBR tests were conducted on pre-cut SBS-modified asphalt samples mixed for different amounts of time. The stiffness modulus (S) and m-value results are shown in Figure 9.  
Figure 8: Fatigue life (Nf) values of pre-cut SBS-modified asphalt mixed for different amounts of time.  
The stiffness modulus (S) of the pre-cut SBS-modified asphalt was lower than that of the SBS-modified asphalt prepared through the conventional process, and the m-value was larger. This indicates that the pre-cut SBS-modified asphalt, after being shredded, was softer at low temperatures, had greater ductility, and exhibited better stress relaxation capacity. At the testing temperature, when the SBS structure was filamentous and granular, the stiffness modulus was smaller compared to when the SBS structure was particulate or atomized, while the m-value showed the opposite trend. This suggests that low-temperature crack resistance is better when the SBS structure is filamentous and granular. The lower stiffness modulus after 60 min of mixing compared to 30 min indicates that a narrower filament width and smaller granular diameter resulted in greater asphalt ductility. The larger m-value at 30 min of mixing compared to 60 min indicates that a wider filament width and larger granular diameter led to stronger stress relaxation capacity.  
Effect of Shredded Particle Size on Microscopic Morphology and Performance of Pre-Cut SBS-Modified Asphalt  
In the aforementioned study, it was observed that pre-cut SBS-modified asphalt with a mixing time of over 30 min exhibited superior rheological performance compared to conventionally prepared SBS-modified asphalt. Therefore, to investigate the effects of shredded particle size on the physicochemical characteristics, morphology, and performance of pre-cut SBS-modified asphalt, samples were prepared by low-speed mixing for 30 min using three different particle sizes of pre-cut SBS (10-18 mesh, 18-30 mesh, 30-100 mesh).
## 3 Problem Definition and Formulation
### Problem Formulation and Objective Function
Specifically, the definition of \(\mathbf{X}\) is  
\[\mathbf{X}=\left\{\mathbf{T},\mathbf{R_{i}},\mathbf{P}\mid\sum_{j=1}^{m}t_{ij }\leq R_{i},\sum_{i=1}^{n}t_{ij}=D_{j},d_{j}\leq D_{\max},|t_{ij}-t_{ij}^{ \prime}|\leq C_{\max},P_{j}\in\mathcal{P},\text{Path}(s_{j},t_{j})\in\mathcal{ G}\right\} \tag{11}\]  
Here, \(\mathbf{T}\) represents the task-resource allocation matrix, \(\mathbf{R_{i}}\) represents the resource constraints at each satellite node, and \(\mathbf{P}\) represents the transmission path for each task. For the variables in the objective function, \(t_{ij}\) represents the amount of resources allocated to task \(j\) at satellite node \(i\). By maximizing the sum of resource allocations for all tasks at all nodes, i.e., \(\sum_{i=1}^{n}\sum_{j=1}^{m}t_{ij}\), we aim to increase the overall throughput of the network (i.e., the total amount of data transmitted). The term \(\sum_{i=1}^{n}\sum_{j=1}^{m}|t_{ij}-t_{ij}^{\prime}|\) quantifies the computational complexity due to resource allocation adjustments, while \(\sum_{j=1}^{m}d_{j}\) represents the total transmission latency across all tasks. Theorem 3.1: _The problem defined in Equation (10) is NP-hard._  
Proof. : We prove that the proposed optimization problem is NP-hard by reducing a well-known NP-hard problem, the Multipath Shortest Path Problem (MSPP), to our resource allocation and task scheduling problem. The MSPP seeks to find \(k\) disjoint paths \(\{P_{1},P_{2},\ldots,P_{k}\}\) from source \(s\) to target \(t\) in a given directed graph \(G=(V,E)\), minimizing the total cost, defined as  
\[C_{\text{total}}=\sum_{i=1}^{k}\sum_{(u,v)\in P_{i}}c(u,v), \tag{12}\]  
where \(c(u,v)\) represents the cost associated with each edge \((u,v)\) in path \(P_{i}\). The total cost is the sum of individual edge costs over all selected paths. The optimization problem for the MSPP can then be formulated as  
\[\min_{P_{1},P_{2},\ldots,P_{k}}C_{\text{total}}=\sum_{i=1}^{k}\sum_{(u,v)\in P _{i}}c(u,v), \tag{13}\]  
s.t. \[P_{i}\cap P_{j}=\emptyset,\quad\forall i\
eq j,\quad 1\leq i,j\leq k. \tag{14}\]  
To demonstrate that our resource allocation and task scheduling problem is at least as hard as the MSPP, we map each path cost in the MSPP to the resource allocation cost in our model. We define the resource allocation decision variable \(t_{ij}\) for task \(j\) at node \(i\) and introduce a cost function for resource utilization:  
\[C_{\text{total}}=\sum_{i=1}^{n}\sum_{j=1}^{m}c_{ij}t_{ij}, \tag{15}\]  
where \(c_{ij}\) represents the cost of assigning resources at node \(i\) for task \(j\). This function directly parallels the MSPP cost function, establishing an equivalence. Since the MSPP is known to be NP-hard, and our problem generalizes the MSPP by introducing resource allocation dependencies, our problem is at least NP-hard.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 3 Grounding Network Topology Detection Method
### Current Injection Method  
The current injection method injects a sinusoidal current by selecting a suitable injection node, measures the magnetic field on the surface, and analyzes the magnetic field distribution characteristics to realize the grounding conductor positioning.  
[PERSON] [30] designed and developed a different frequency current supply system, mobile weak magnetic induction intensity measurement system, and corresponding software system. [PERSON] [33] proposed a grounding network topology detection method on the basis of the differential method and established a magnetic field shape function for describing the direction of the ground surface parallel or perpendicular to the grounding grid. This method uses the principal peak characteristic of the even derivative of horizontal component shape function and odd derivative of vertical component shape function to filter and differentiate the measured data of a square magnetic field in the grounding grid after injecting a current, so as to determine the topological structure of the grounding grid. However, the differential method is very sensitive to noise and measurement error. The detection path has a good effect perpendicular to the conductor direction, and the positioning result may have a large error when a large deviation from the vertical direction exists [30; 33]. [PERSON] et al. [51] proposed a grounding network topology detection method on the basis of wavelet edge detection technology. Wavelet edge detection effectively strips the magnetic gradient characteristics of each conductor segment from the background field, which greatly reduces the influence of magnetic field superposition on positioning accuracy. At the same time, the strong anti-interference ability of the wavelet transform suppresses the error introduced in the measurement and magnetic field fitting process and ensures the method's stability.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Time-Domain Sensitivity Functions of Optical Signals
Here, \(G(\textbf{r},\textbf{r}_{s},t^{\prime})\) means the fluence rate at position **r** and at time \(t^{\prime}\) generated by an impulse point source at position \(\textbf{r}_{s}\) and time \(0\), and \(G(\textbf{r}_{d},\textbf{r},t-t^{\prime})\) in the first equality means the fluence rate at position \(\textbf{r}_{d}\) at time \(t-t^{\prime}\) generated by an impulse point source at position **r** and time \(t^{\prime}\) as indicated in Figure 5a. The function \(H(\textbf{r}_{d},\textbf{r}_{s},\)\(\textbf{r},t)\) indicates the probability of photons that exist at position **r** after being injected from the source at position \(\textbf{r}_{s}\) at time \(0\) and finally being detected by the detector at position \(\textbf{r}_{d}\) at time \(t\), and when illustrated in 2D or 3D images at varying times \(t\), they show the temporal evolution of probabilistic light paths in the medium between the source and detector. In case of reflectance from semi-infinite media, the probabilistic light paths exhibit so-called banana shapes, and the banana shapes grow as time evolves. In case of transmittance through slabs, similar formulations are derived as in the case of reflectance, and the probabilistic light paths exhibit so-called spindle-like shapes. The function \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\textbf{\ }t)\) is called various terminology such as the sensitivity to absorption, photon probability distribution [26], photon hitting density [27], photon measurement density function [28], relative photon visit probability [29], photon visiting probability [30], photon sampling function, photon weight function, Jacobian with respect to absorption, etc., with variation in the proportionality constant, \(\alpha\). The sensitivity to scattering changes can also be derived from Equation (18), but its physical meaning is not as clear as \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\). The sensitivities play important roles not only in understanding the propagating path of scattered light but also in the inversion process of reconstructing absorption and scattering images in DOT as explained later. [PERSON] et al. [30] experimentally estimated \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\) by time-gated measurements of the fluence rates through a homogeneous semi-infinite medium. The second equality in Equation (20) means that \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\) is given by the convolution between the Green's function for the input position at the source, \(\textbf{r}_{s}\), and that for the input position at the detector, \(\textbf{r}_{d}\), as shown in Figure 5b. [PERSON] et al. [30] used this characteristic of \(H(\textbf{r}_{d},\textbf{r}_{s},\textbf{r},\,t)\) to acquire the images in Figure 6 showing the banana shapes growing bigger as time evolved. Continuous wave sensitivity is obtained by extending the upper time limit in Equation (20) to infinity.
## 4 Results and Analysis
### Go-Around: Analysis  
#### 4.5.1 Go-Around: Fleiss' Kappa  
Fleiss' kappa analysis was conducted for one question regarding go-around scenarios, as seen in Table 17.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statement** & **Definitions** \\ \hline \multirow{7}{*}{Maximum float point interpretations from the survey, by survey participants.} & \"Maximum point on the runway in the touchdown zone that the aircraft main gear must make contact with the runway.\" \\  & \"Point at which if you continue past, you are not assured of being able to stop within the remaining distance.\" \\  & \"300–500 ft on C172 depends on flap configuration.\" \\  & \"If the aircraft has not touched down by the maximum float point \\  & there isn’t enough runway left to safely land.\" \\  & \"Maximum distance down the runway that it is safe to land. If not \\  & expected to touch down by the automatically go-around.\" \\  & \"The point beyond which I would initiate a go-around from the flare.\" \\  & \"The furthest point of touch-down after a flare is initiated.\" \\  & \"A point on the runway after which a go-around should \\  & be initiated if still floating.\" \\ \hline \hline \end{tabular}
\end{table}
Table 16: Interpretations of the maximum float point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **P\({}_{\mathbf{o}}\)** & **P\({}_{\mathbf{e}}\)** & **Kappa** \\ \hline Out of the following options, what is the first action that you perform when conducting a go-around procedure & 0.909 & 0.228 & 0.883 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Go-around Fleiss’ kappa.  
With 95% of survey participants agreeing that the first action that should be performed is adding power, the kappa value demonstrates strong agreement among the participants. This is consistent with AOPA [7] guidelines for a go-around. Only 5% of participants answered that they would retract the flaps and gear first.
# Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
## 4 Methods and Results
### Visual Analytics Applied to the Bending Signal  
This article presents the adoption of a technique for converting time series into images that could be classified using deep learning. This method provides a way to reconstruct the original data into images and is an application of the methodology used by [PERSON] et al. [16]. For the conversion of time series into images, they propose three techniques, two based on Gramian angular fields (GAFs) and one based on Markov transition fields (MTFs). In this article, the option of transforming the Markov transition fields is adopted, contrary to the approach using the encoding technique based on the sum of Gramian angular fields (GASFs) employed by [PERSON] et al. (2019) [PERSON] et al. (2019).  
Given a time series, in this case, the pressure of the right cylinder (Z_CPD_P), it is possible to convert it into an image, identify the quantiles, and assign them to bins. Then, a weighted adjacency matrix is constructed to count the transitions between quantile bins, forming a first-order Markov chain. The frequency with which a point in one bin is followed by another point in another bin determines the weights of the matrix. By normalizing the matrix, the Markov transition field (MTF) is obtained. The MTF encodes the transition probabilities in different sections of the series, which are assigned to each pixel of the image. To reduce the size of the image, the pixels are averaged. The following Figure 3 shows the 32 \(\times\) 32 pixel images of the pressure signals in bending. From the signals shown in Figure 3, the signals (1, 2, and 6) are those corresponding to the 44 mm plate and the signals (3, 4, 5, and 7) are those corresponding to the smaller 32 mm thick plates.  
In the context of the bending process, MTFs have the potential to provide valuable insights into the process and enable the identification of potential issues. By converting the bending pressure signal into an image using MTFs, it becomes possible to visualize the different phases of the process, such as the initial loading phase, the rolling phase, and the unloading phase. This visual representation can aid in understanding the process and identifying potential areas of concern. Additionally, MTFs can be used as input features for machine learning algorithms to classify different types of bending defects, such as cracks, wrinkles, and necking. This capability can help in identifying and addressing potential quality issues early in the process. Furthermore, by monitoring the MTF of the bending pressure signal over time, it is possible to detect anomalies in the process, such as sudden changes in pressure or unexpected transitions between phases. This anomaly-detection capability can help in preventing process failures and ensuring the production of high-quality products. Overall, MTFs offer a promising approach for analyzing and understanding the bending process, with the potential to improve process efficiency, product quality, and overall production outcomes.
## 1 Introduction
Given the theory of SEA [20], this method could only give a statistical answer, which always has obvious uncertainty, and it is only suitable for high frequencies. [PERSON] and [PERSON] [21] described two analytical solutions based on the Rayleigh integral and based on BEM for the sound radiation of a thick annular disk with a free boundary. [PERSON] et al. [22] proposed an effective approach called acoustic transfer vector (ATV) to quickly evaluate the vibro-acoustic response by saving the ATV in advance in the database. The above methods are all based on establishing the integral relationship between the surface vibration velocity and the sound field to predict the radiated sound. Therefore, they all require a large number of measuring points on the structure surface. [PERSON] and [PERSON] [23] discussed a similar and complete approach for a fluid waveguide and dealt with more complicated situations. [PERSON] et al. [24] discussed the sound wave radiation from the perspective of the convergence of the solution in terms of the satisfaction of the kinematics at the shell-fluid interface, and also dealt with some complex situations. To simplify the procedure of predicting the radiated sound field and efficiently obtain the radiated sound power of the structure, a new method based on the main radiation structural modes is proposed in this paper, and this method is developed from the modal contribution idea in the ARM method. Some numerical calculations indicated that when the underwater cylindrical shell vibrates at low frequencies, a certain structural mode called dominantly radiated structural mode (DRSM) contributes dominantly to the total radiated sound power in a certain frequency bands. On the basis of this characteristic, DRSM is used to efficiently predict the radiated sound power of underwater cylindrical shells. This method is efficient because it inherits the idea of efficient ARM and obtains the radiated sound power by superposition of several DRSMs. In addition, in most frequency bands, the DRSMs contribute significantly to the vibration displacement. Therefore, the solving displacement equations method can be used during modal decomposition, and will effectively reduce the number of required measuring points. Furthermore, structural modal information of the underwater structure can be precomputed and saved in the database for the next forecast and calculation, which makes it easier to meet the real-time requirements of online prediction.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results  
The performance of the proposed method is evaluated on the NR dataset and TVWS dataset. The evaluation includes the reconstruction performance of BEISTA-Net, the sensing performance of BSWSS-Net, and the sensing performance of the joint CSS method.
## 2 Methodology  
The proposed ReCon framework employs multidisciplinary system design optimization (MSDO) derived from systems engineering. The objective of optimizing a ReCon is threefold: To minimize revisit access time and maximize area coverage; to minimize the initial launch mass of the entire satellite constellation (number of orbit planes \(\times\) number of satellites per plane \(\times\) satellite mass); and to minimize reconfiguration time. The ReCon framework optimizes the geometry of an individual orbit and the arrangement of multiple orbits in addition to satellite payload design while satisfying the imagery resolution requirement.