esa-sceva/satcom-chunk-collection huggingface dataset:
# DAB Signal Preprocessing for Passive Coherent Location
## 3 Results
### Clearing of PR and FIC Symbols  
After removing the periodical parts of the DAB frames (i.e., the PR and FIC symbols) by clearing their sample values, the periodical artifacts are still visible in the bistatic velocity/range (\(v/R\)) plane, as shown in Figure 14. What is essential, is that these artifacts are even intensified by ca. 15 dB when compared to Figure 1. One moving object is visible at \(R\) = 1.2 km, \(v\) = 50 m/s, but its echo will be obscured by the artifacts at bistatic velocities being multiplies of \(v_{\mathrm{a}}\) (1). This effect can cause false detections, and, in general, it degrades the detection and tracking the performance of the considered passive radar. The artifacts stay visible when the clutter-removal filter is bypassed (see Figure 15).
# Numerical Analysis of the Take-Off Performance of a Seaplane in Calm Water
###### Abstract  
Nowadays, with the escalating tensions in maritime dispute and the development of marine economy, there has been renewed interest in seaplanes for their special capacity of taking off and landing on water. Prediction of take-off performance, involving aerodynamic analysis and hydrodynamic analysis, is a main challenge in seaplane design, while the prediction methods have been little improved since the 1960s. This paper aims to investigate the attitude and resistance characteristics of a seaplane at different speeds during the take-off by numerically modeling the air-water flow field using RANS equations with VOF method. The trim and heave motion of seaplane in response to aerodynamic forces, hydrodynamic forces, hydrostatic forces, and propeller thrust was realized by solving rigid body dynamics equations and adopting dynamic overset mesh technique. The variations in heave, trim angle, and resistance characteristics during the takeoff were investigated, and their inherent relationships with the aerodynamic, hydrodynamic, and hydrostatic performance were revealed. Particular investigation on the hydrodynamic resistance indicates that the stagnation line located at the convex bow would contribute a considerable increase of pressure resistance at the first hump, and the trim angel of a seaplane should be operated in an optimum trim range, typical between 4-6 deg, to minimize the hydrodynamic resistance at the second hump. Additionally, the dynamic motion convergence study proves that the utilization of damping terms was an effective way to accelerate the convergence of the dynamic motion ending with a quasi-static state.  
CFD; seaplane; hydrodynamics; aerodynamics; resistance; overset mesh; VOF +
Footnote †: journal: Applied Sciences
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
###### Abstract  
As one of the most important processes in steel wire rope inspection, defect signal processing is of great significance in guaranteeing safety and precision measurement. Aiming at the weak signal detection of steel wire rope with mixed strands and noise, a combined signal processing method based on magnetic flux leakage testing and multi-step filtering techniques are proposed in this paper. The experiments are first introduced and performed on three typical types of steel wire rope with diameters of 28 mm, 32 mm, 45 mm, and different wire broken defects detected under liftoff distances of 13 mm and 20 mm; the acquired signals are then analyzed both in time and frequency domain. According to the weak signal characterizations, the principle of the proposed methods and algorithm are given concretely. Afterwards, comparison of signal processing results between the traditional lowpass filtering, wavelet denoising, median filtering, and the proposed method are presented. Finally, the influence factors of smoothing types and moving average span of the proposed methods are investigated. The processing results of the proposed methods are shown through short-time Fourier transform and signal-to-noise ratio analysis, which not only demonstrates the validity and feasibility of the combined methods with the highest signal to noise ratio of 90.37 dB, but also exhibits a great potential of precision defect detection and practical application in steel wire rope inspection.  
weak signal; steel wire rope; improved Hilbert-Huang Transform (HHT); signal processing +
Footnote †: journal: _sciences_
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 1 Introduction
### Principle of 3D Position Measurement Using Ultrasonic Sensors: 3D Ultrasonic Sensor Module  
Figure 4 is the configuration diagram of ultrasonic sensors, called the \"3D ultrasonic sensor module\" in this paper, for measuring a position of an object in a three-dimensional space. Let \(d_{1}\), \(d_{2}\), \(d_{3}\), and \(d_{4}\) be the measured distances from the sensors located at (\(a\), 0, 0), (0, 0), (0, \(b\), 0), and (0, \(-b\), 0) in the coordinate system (\(x\), \(y\), \(z\)) to an object, respectively. The distance \(d\)- from the ultrasonic sensor with the notation number * is measured as follows  
\[d_{*}=\frac{c_{t}\Delta t_{*}}{2},\ c_{t}=331.3+0.606T\ [m/s] \tag{1}\]  
where \(c_{t}\) is the speed of sound, \(\Delta t_{*}\) is the ultrasonic propagation time, called Time-of-Flight (TOF), from the sensor with the designation number * to the object, and \(T\) is the ambient temperature. Using Equation (1) and geometric information, the three-dimensional position (\(p_{x}\), \(p_{y}\), \(p_{z}\)) of the object using the ultrasonic sensor can be derived as follows  
\[\begin{array}{l}p_{x}=\frac{d_{2}^{2}-d_{1}^{2}}{2a}+\frac{g}{2}\\ p_{y}=\frac{d_{2}^{2}-d_{2}^{2}}{2b}+\frac{b}{2}\\ p_{z}=\sqrt{d_{2}^{2}-p_{x}^{2}-p_{y}^{2}}\end{array} \tag{2}\]
## 2 Materials and Methods
### Cyclist Helmet Regulations  
In recent years, environmental awareness has gained strength and popularity especially in high-income countries [6]. Not only hybrid or electric vehicles are frequently used but also bicycles more often replace cars as the main mean of transport. Except for its undisputed ecological value, the economical aspect comes to the fore [28]. Nowadays, due to traffic jams in cities, a bicycle as a mean of transport allows its user to save time. As the ecological, economical, time saving and health improving form of transport, the tendency with all likelihood will increase the participation of cyclists in traffic. In low-income countries, the key aspect for cycling is the availability, ease of access and low expense of vehicle maintenance. Usually, there is no need for dedicated infrastructure and this contributes to the convenience of using this form of transport, especially in places that are hard to reach by other means of transport.  
Nevertheless, the main burden for a bicyclist comes from the high probability of injury, especially in cases of impacts with motor vehicles. The most exposed body regions in the case of cyclists during road accidents are the upper extremities and head, which is injured in 42% of cases [29]. The frequency of injuries to the cyclist's body region is presented in Table 1. It was also reported that cyclists who died as a result of an impact usually did not wear helmets [30].  
Figure 4: The test rig for the guided free fall impact test in accordance with the EN 1078 and using EN 960 magnesium headform.  
To protect the cyclist's head from potential injuries the EN 1078 standard was introduced 1997 in the European Committee for Standardization (CEN) member states. EN 1078 applies to helmets for adults and children. However, to prevent young children being trapped by a helmet during some activities (e.g., play in the playground), which may lead to strangulation, the EN 1080 was put forward. Although EN 1078 and EN 1080 requires from a helmet the same shock absorption performance (\(<\)250 g), the EN 1080 requires from a helmet manufacturer to provide a retention system with a self-release mechanism--which opens by a force of 90-160 N--to minimize the risk of child strangulation. The author of this paper highlights that these two standards cannot be applied interchangeably, when it comes to young cyclists, as some literature may indicate [31].  
According to EN 1078 a helmet should protect the user in the way that the peak acceleration shall not exceed 250 g for the velocity of 5.42 m/s (drop height 1497 mm) on a flat anvil and 4.57 m/s on the kerbstone anvil (drop height of 1064 mm). The test rig and the magnesium headform (size 575--type J), which complies with EN 960, for helmet testing are depicted in Figure 4. The measured peak linear acceleration according to EN 1078 shall not exceed 250 g.
## 4 Result
### Evaluation of Predictive Performance
#### 4.1.1 Hyperparameter Optimization Using Bayesian Optimization  
The optimized Hyperparameter settings and their corresponding R\({}^{2}\) values are summarized in Table 3.  
Figure 3: Pearson correlation matrix. The asterisk (\({}^{\bullet}\)) indicates a statistically significant correlation.  
During iteration 6, the R\({}^{2}\) value was the highest at 0.7422, with the max_depth set at 9.961 and the learning rate at 0.1349. In iteration 9, the max_depth was set at 9.859 and the learning rate at 0.1908, achieving an R\({}^{2}\) value of 0.74. For iteration 7, the max_depth was 9.562, the learning rate was 0.1003, and the R\({}^{2}\) value was 0.7356.  
In iteration 18, the max_depth was significantly lower at 3.174, resulting in a decreased R\({}^{2}\) value of 0.5116. Iteration 17 featured a max_depth of 9.8 and a learning rate of 0.05436, with an R\({}^{2}\) value of 0.7289. In iteration 19, the max_depth was set at 5.263 and the learning rate at 0.1361, yielding an R\({}^{2}\) value of 0.6658.  
The colsample_bytes were set within a range of 0.5 to 0.9, while the subsample was optimized within a range of 0.7 to 0.9. The n_estimators values ranged from 140 to 320, and it was observed that using more than 200 trees tended to maintain an R\({}^{2}\) value above 0.74.  
The derived hyperparameter values are presented in Table 4.  
To regulate the learning speed and convergence, the learning rate was set at 0.1349, and the max_depth was adjusted to 9. Additionally, the subsample value was set at 0.8033, and the colsample_bytes was set at 0.5895, allowing the model to utilize only a portion of the training data.  
Analysis of the model performance changes due to hyperparameter settings indicated an enhancement in the predictive performance of the XGBoost model. A stable R\({}^{2}\) value was maintained when the max_depth was set to 9 or higher. Adjusting the subsample and colsample_bytes values enabled the learning of diverse data patterns. This adjustment of optimized hyperparameters has been confirmed to contribute to the improvement of the model's performance.
# Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers
## 2 Background  
### NEON Engine and ASIMD Instructions  
From the ARMv7 architecture, NEON engine and parallel processing unit have been supported to maximize performance. NEON engine in ARMv7 supports 16 vector registers (\(q0\)-\(q15\)) with a 128-bit width for parallel processing, but that in ARMv8, the target device of this paper, supports 32 128-bit vector registers (\(v0\)-\(v31\)). In addition, in the NEON engine, parallel processing is possible for 64, 32, 16 and 8-bit wise within a 128-bit vector register. Table 1 shows Advance Single Instruction Multiple Data (ASIMD) instructions and clock cycles of each instructions. Since the target block ciphers are based on ARX structure, ASIMD instructions for processing addition, rotate, and _XOR_ are required. _ADD_ instruction processes the addition in parallel by lane. _SRI_ and _SHL_ instructions process rotate shift operations in parallel, and it is commonly used to implement rotate shift operations in the NEON engine. _REV16_ is an instruction that can process _ROL_s operation more efficiently than _SRI_ and _SHL_ when parallel processing _ROL_s operation in 16-bit units. _EOR_ instruction processes _XOR_ operations in parallel depending on the lane. _TRN1_ and _TRN2_ instructions efficiently transpose the vector registers. The transpose operations of _TRN1_ and _TRN2_ instructions are as shown in Figure 1. _ST4_ and _LD4_ are instructions to store data from 4 vector registers to memory and load data from memory to 4 vector registers, and, at this time, transpose operation is automatically applied. Using these properties, we present a method to apply data parallelism without additional costs by using _ST4_ and _LD4_ instructions in ARX-based ciphers (LEA and revised CHAM) except HIGHT. From Table 1, it can be seen that more cycles are required for memory access operations than simple register operations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instructions** & **Operands** & **Description** & **Cycles** \\ \hline ADD & \(V_{d},V_{m},V_{n}\) & Vector addition \(V_{d}=V_{m}\)+\(V_{n}\) & \(1\) \\ \hline SHL & \(V_{d},V_{m},V_{n}\) & Vector left shift \(V_{d}\gets V_{m}(\#n)\) & \(1\) \\ \hline SRI & \(V_{d},V_{m},\#immediate\) & Vector right shift and insert \(V_{d}\gets V_{m}(\#n)\oplus V_{m}(insert)\) & \(2\) \\ \hline TRN1 & \(V_{d},V_{m},V_{n}\) & Vector transpose (primary) & \(1\) \\ \hline TRN2 & \(V_{d},V_{m},V_{n}\) & Vector transpose (secondary) & \(1\) \\ \hline REV16 & \(V_{d},V_{m}\) & Vector reverse elements in 16-bit halfwords \(V_{d}\gets V_{m}\)(reverse) & \(1\) \\ \hline LD4 & \(\{V_{n}-V_{m}\},[\ ]\) & Loading data from memory to 4 vector registers by applying the transpose operation & \(4\) \\ \hline ST4 & \(\{V_{n}-V_{m}\},[\ ]\) & Storing data from 4 vector registers to memory by applying the transpose operation & \(4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of ASIMD instructions used in this paper [13].
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## 4 Algorithmic Aspects
### Order of Magnitude of the Coefficients and Unknowns
#### 4.1.2. Extensional vs. Shear Torsional Effects  
A second, and more important, topic concerns _coupling_ existing between the GBT equations. They are a set of two fourth-order differential equations, where coupling is mainly due to higher-order terms (the larger ones being produced by warping, as seen before). It is interesting to investigate if, and under which conditions, such a coupling can be neglected. To this end, the box-girder is assumed to be loaded by sinusoidal loads\(\mathbf{p}=\left(\hat{\epsilon}_{t},\hat{\epsilon}_{d}\right)^{T}\sin(\frac{n\pi 2}{\ell})\), which trigger sinusoidal deflections \(\mathbf{a}=\left(\hat{\theta},\hat{\varphi}\right)^{T}\sin(\frac{n\pi 2}{\ell})\). The amplitudes must satisfy the following algebraic equations:  
\[\left[\left(\begin{array}{cc}C_{11}^{e}&C_{12}^{e}\\ C_{21}^{e}&C_{22}^{e}\end{array}\right)\left(\frac{n\pi}{\ell}\right)^{4}+ \left(\begin{array}{cc}D_{11}^{s}&0\\ 0&0\end{array}\right)\left(\frac{n\pi}{\ell}\right)^{2}+\left(\begin{array}{ cc}0&0\\ 0&B_{22}^{f}\end{array}\right)\right]\left(\begin{array}{cc}\hat{\theta}\\ \hat{\varphi}\end{array}\right)=\hat{c}\left(\begin{array}{cc}1\\ 1\end{array}\right) \tag{28}\]  
where \(\hat{\epsilon}_{t}=\hat{\epsilon}_{d}=\hat{c}\) has been taken and \(C_{ij}^{f}\) is neglected together with \(D_{ij}^{t}\) (small thickness). The task is understanding if the \(C_{ij}^{e}\)'s bring a significant contribution to the equilibrium when, e.g., they are compared to \(D_{11}^{s}=GJ\) or \(B_{22}^{f}\). Referring to a rectangular box-girder with uniform thickness \(t_{1}=t_{2}=:t\), the nondimensional ratios \(r_{ij}^{s}:=\frac{C_{ij}^{e}}{D_{11}^{s}}\left(\frac{n\pi}{\ell}\right)^{2}\) and \(r_{ij}^{f}:=\frac{C_{ij}^{e}}{B_{22}^{f}}\left(\frac{n\pi}{\ell}\right)^{4}\) among the coefficients are plotted in Figure 10 vs. the squateness ratio \(\frac{nh}{\ell}\) for different \(\frac{h}{\ell}\). It is seen that the \(r_{ij}^{s}\) are less than 0.1 if \(\frac{nh}{\ell}<0.2\) (e.g., \(n<4\) if \(\ell=20\,h\)). Moreover, \(r_{ij}^{s}\) is much smaller as \(\frac{h}{h}\) decreases, tending towards a square shape. Concerning \(r_{ij}^{f}\), similar consideration hold.  
The investigation therefore leads to the following conclusions:  
1. Warping, as expected, plays a minor but not negligible role in torsion of closed TWB. In particular, it is fundamental in describing boundary layers close the constraints (or lumped forces), which, in a Fourier perspective, call for higher-order harmonics (\(n\) large), which make the fourth-order derivatives comparable with second- or zero-order derivatives.
2. Due to the small but not negligible coupling terms due to warping, the torsion-distortion mechanical problem cannot, in principle, be uncoupled, as already observed, e.g., in Reference [8]. A measure of the error made in splitting the problem will be discussed with reference to the numerical results.
## 3 The Proposed CNN-SNN Conversion Method
### Inference of the Converted SNN  
The converted SNNs are supposed to receive as the input either the spike train or the constant current, which can be the intensity values of the input images. There are some output decoding techniques for SNNs such as the selection of the maximum spike frequency node and the selection of the maximum membrane potential node. The proposed threshold balancing technique sets the threshold of the final layer to the infinity value \(\infty\). Hence, the node with the maximum membrane potential is selected as the target output node. The SNN inference in the proposed method is shown in Algorithm 3.  
```
Input:\(T_{infer}\): desire inference latency Output:\(acc\): Accuracy in % Notations:\(n_{samples}\): number of test samples; \(n_{correct}\): number of correct predicted samples; \(n_{output}\): number of output neurons begin  Initialize : \(n_{sample}=0\) ; \(n_{correct}=0\) foreachtest-sampledo \(n_{sample}+=1\) fortime step \(t\gets 1\) to \(T_{infer}\)do # Feed to SNN to generate the output potentials in the last layer \(V^{n_{layer}}[t]=snn(test-sample)\)  end for \(label_{predicted}=max\{V^{n_{layer}}_{i}[T_{infer}],i=0,1,\ldots,n_{output}-1\}\) if\(label_{predicted}=label_{ground-true}\)then \(n_{correct}+=1\)  end if  end for # Calculate the accuracy of the converted SNN \(acc=\frac{n_{output}}{n_{samples}}*100\) ; // \(acc\): accuracy of the SNN model  end for
```  
**Algorithm 3**Inference of the converted SNN.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 2 Intelligent Inspection Planning
### Hardware Equipment Requirements  
The standard road inspection process is divided into three main parts. For reported cases, simple manual square cutting and patching is carried out upon receipt of reports. Cases reported via the 1999 hotline must be patched within four hours. The first stage involves simple manual square cutting, followed by an evaluation to proceed to the second stage, which includes partial square milling and paving for improvement. For self-inspected  
Figure 1: Inspection and Maintenance Scope.  
profiles, the method of repair or follow-up is chosen, and subsequent paving improvement methods are scheduled.  
The choice of pavement improvement method is based on the Pavement Condition Index (PCI) values. Preventive crack filling and patching methods are applied for PCI values above 71. Routine partial milling and paving methods are used for PCI values greater than 40, while large-area milling and paving methods are selected for PCI values below 40. The equipment used for simple repair vehicles and AI intelligent inspection vehicles is detailed in Tables 1 and 2.
# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 3 Methodology
### Community Detection Approach Based on Deep Learning
#### 3.2.4 Fully Connected Layer  
The last layer of the proposed network is the fully connected layer, which consists of K neurons, whose number is equal to the number of communities to be analyzed. Summarizing, the output of this layer represents the probability distribution over \(K\) community classes.  
Each neuron assigns each input node to the relative community by setting to 1 the output value of the relative neuron, while the others are set to zero. Note that each value in each feature map is connected to all \(K\) neurons in the _fully connected_ layer.  
Formally, the value of the \(k\)-th neuron can be calculated by the following formula:  
\[\sigma_{n}^{k}=relu(b_{k}^{f}+W_{k}^{f}q_{n}^{c_{1}c_{2}}) \tag{2}\]  
where \(f\) denotes the last _fully connected_ layer, while \(q_{n}^{c_{1}c_{2}}\), \(W_{k}^{f}\), and \(b_{k}^{f}\) are the output of the convolutional layer, the weights, and the bias of the \(k\)-th neuron in the layer under consideration, respectively.  
In conclusion, the learning phase iteratively updates the convolutional layer to improve the accuracy of the proposed system by optimizing the system parameters (\(P=(W,W_{f},b,b_{f})\)). Then, a back-propagation step is performed in order to optimize the values of the vector \(P\). Formally, we assume that there is a set \(T\) of _training sample_\(\{(s_{n},l_{n})1\geq n\geq T\}\), where \(s_{n}\) is the adjacency relation of node \(n\) and \(l_{n}^{k}\) expresses whether or not the node belongs to the particular community. The cost function can be expressed as follows:  
\[J(P)=\frac{1}{2}\sum_{n=1}^{T}\|\sigma_{n}-l_{n}\|_{2}^{2}=\frac{1}{2}\sum_{n= 1}^{T}\sum_{k=1}^{k}(\sigma_{n}^{k}-l_{n}^{k})^{2} \tag{3}\]  
where \(\sigma_{n}^{k}\) is the value of the \(k\)-th neuron and \(l_{n}^{k}\) the \(k\)-th size of the corresponding label vector (ground truth).  
This iterative process continues until Equation (3) converges by optimizing the parameters of the vector \(P\) via the back-propagation operation. The back-propagation criterion used was the _sparse softmax cross-entropy_, suitably modified to be optimized to work with sparse matrices.  
Our approach relies on the semi-supervised strategy, which requires a small sample of training data, on which we tuned the optimal parameters in order to identify communities in an MSN.
## 2 Results and Discussion
### UV-Vis and Photoluminescence Spectroscopy
Emission spectra were acquired as a function of temperature, wavelength excitation of 282 nm, and the evolution of the integrated emission is depicted in Figure 8B. At room temperature, CeNR material does not emit. Several reasons have been put forward to explain the quenching of luminescence including the thermally-excited interconfigurational system crossing, energy transfer from _4f_ level to nearby centres, electron transfer or photoionization [36]. Between -262 and -173 \({}^{\circ}\)C the CeNR emission intensity remains constant and then it decreases markedly, almost disappearing at _ca._ -53 \({}^{\circ}\)C.  
Figure 8: **(A)** 11 K emission spectra of CeNR (red), CeNR-PhEt-BuOOH (green) and CeNR-H2O2 (magenta) excited at 316 nm: the dotted lines represent spectral deconvolution using two Gaussian functions attributed to the _5d_-2_F5/2,7/2_ transitions. The inset shows the emission spectra of CeNR (black) and CeNR600 (cyan) excited at 332 nm. **(B)** Emission intensity as a function of temperature of CeNR (red squares) and the Mott-Seitz fitting (solid line) using Equation. (8). This could be the result of defects (including oxygen vacancies site in the crystal) with electronic energy levels between the O_2p_ and _4f_ bands. The number of these defects is temperature dependent [37; 38; 39]. This behaviour can be described by a thermally-activated non-radioactive mechanism modelled by the Mott Seitz Equation [40; 41]:  
\[I=\frac{I_{0}}{1+a\exp^{(-\Delta E/k_{aT})}} \tag{8}\]  
where \(a\) is a constant and _AE_ is the activation energy that corresponds to the energy gap between the _4f_ level and that of the de-excitation states. A fit of the experimental values by Equation (8) (Figure 8B) gives an activation energy of _ca._ 651 cm-1 (81 meV). This value is consistent with a previous report where the defect levels are located in the range of 1 eV below the Ce _4f_ band [38; 39].