esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Health Monitoring of 2D Plane Structures
## 3 Example of the Application
### Definition of the Analyzed Structure  
The analyzed structure was chosen from [PERSON]'s book [45]. This structure corresponds with the discretization of a clamped plate simulated with the plane strain theory. For the finite element model, two quadratic 6-noded LST elements (see Figure 2a) were considered, leading to a system with 9 nodes and 18 degrees of freedom. The boundary and loading conditions as well as the node and element numbering are presented in Figure 2b. The uniform force at the right edge of the structure (corresponding to a distributed load of W = 3000 kN/m\({}^{2}\)) has been simulated as concentrated forces at the element nodes (nodes 3, 6, and 9), according to the tributary area of each node. As illustrated in this figure, the clamp conditions are modeled by fixing the horizontal and vertical deflections of nodes 1, 4, and 7. The horizontal and vertical deflections of the rest of the nodes in the structure remain free.  
The analyzed structure has a length of 0.5 m, a height of 0.25 m, and the thickness of 1 (plane strain assumption). The Young's modulus and Poisson's ratio are E = 210 GPa and \(\theta\) = 0.3, respectively. The first part of the developed package was used to calculate the horizontal and vertical deflections at the free nodes as well as the reactions at the boundary conditions (direct analysis). The obtained deflections and reactions are summarized in Table 4. The results obtained from the direct analysis are considered in good agreement with those in [45] (in fact, both results present the same number of decimal numbers).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Node** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(u_{i}\)(m) & & & 0.080 & 0.1580 & & 0.0739 & 0.1568 & & 0.0716 & 0.1580 \\ \(v_{i}\)(m) & & & & & & & & \\ e-6 & & & & & & & & \\ \(H_{i}\)(KN) & \(-3.541\) & & & \(-11.67\) & & & \(-3.541\) & & \\ \(V_{i}\)(KN) & \(-2.863\) & & & & 0.012 & & & 2.851 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Deflections and reactions at the example from the direct analysis.  
Figure 2: Example: (**a**) 6-noded triangular element (LST), and (**b**) node and element numbering together with the boundary and loading conditions [45].
## 4 Test Results and Insights
### Post-Test Insights: Unstructured Interviews  
After the test, unstructured interviews were conducted to gather qualitative impressions from users regarding their experience. The issues raised by the users can be summarised as follows:  
1. Lack of feedback: Users requested more precise feedback from the interface as they often had a perception gap regarding whether they had acted correctly. Particularly for gestures, users emphasized the need for more timely feedback, such as sound or visual references, to help them be more efficient during interaction with the interface;
2. UI/UX issues: Several users needed clarification on the menu icons, mistaking the POI icon for the destination icon. Inconsistencies in the interface, such as visual indicators and transparency effects for scrolling, also confused and disrupted users' familiarity with the interface;
3. Responsiveness and complexity of gestures: Certain gestures were more accessible to execute than others. The \"grid interaction\" proved to be the most challenging and unnatural to master compared to the \"swipe interaction\", which is more intuitive and aligns with users' familiarity with digital devices. On the other hand, the \"pinch interaction\" for music control received positive feedback, mainly due to the immediate feedback that follows the gesture;
4. Hand position and finger usage: Some users needed clarification on whether they had to maintain a specific hand position for the device to detect their hand or if they could relax. Additionally, prolonged haptic feedback was found to be bothersome by some users. Moreover, gestures that required all five fingers posed an accessibility challenge. Regarding the positive aspects they can be summarised in the following way:  
1. Appreciation for the look and feel: Users found the interface unobtrusive, taking up minimal space and conveniently placed on the windshield, allowing them to focus on the road without distraction. They also appreciated the minimalist design, color scheme, and overall sense of calm it provided;
2. Perception of innovation: The proposed interaction system has received positive feedback from users, particularly the gestures that have been implemented. The novelty effect was also apparent with the trackpad, even though it is a more commonly used device;
3. Haptic feedback: Users appreciated the haptic feedback, which often guided them during gesture performance, providing a sense of touch and confirming that they had successfully executed the requested actions.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### Comparative Study
#### 5.3.3 Comparison with other Clipping Techniques Using HPA  
In order to simulate the near real case, we adopt the nonlinear Solid-State Power Amplifier (SSPA) as a High-Power Amplifier (HPA) model. The BER performances of the proposed method are compared to the other clipping techniques presented in [36] with the same conditions. The output signal is mobilized in SSPA amplifier by  
\[g[r(t)]=\frac{r(t)}{\left[1+\left(\frac{r(t)}{r_{sat}}\right)^{2p}\right]^{ \frac{1}{2p}}}\;;\phi(r)=0\;\;\text{no change in phase}, \tag{13}\]  
where the nonlinear gain \(g[r(t)]\) refers to the input amplitude/output amplitude (AM/AM) conversion characteristics, \(r(t)\) is the amplitude of the input signal, \(r_{sat}\) is the maximum output amplitude, and the parameter \(p\) controls the smoothness of the transition from the linear region to the saturation region. The saturation region of an amplifier depending on the nonlinear distortion of the signal by SSPA is measured using the Input Back-Off (IBO), which is defined as follows:  
\[IBO=10 log_{10}\frac{r_{sat}^{2}}{P_{avg,input}}, \tag{14}\]  
where \(r_{sat}^{2}\) is the input saturation and \(P_{avg,input}\) is the average power of the input signal.  
It is assumed that the PC-OFDM signal is passed through the SSPA amplifier and then through a channel corrupted by Additive White Gaussian Noise (AWGN). The BER performances of the proposed method with other clipping methods are depicted in Figure 22. For all the simulations carried out, \(p\) was set to 2, IBO was set to 5 dB, and CR = 10 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PAPR Value [dB]** & **PAP Gain [dB]** & **BER Loss [dB]** \\ \hline CMSLM [8] & 6.90 & 3.77 & 2.50 \\ SLM [8] & 6.60 & 4.07 & 0 \\ GA-SLM [8] & 6.50 & 4.17 & 2.5 \\ PC (CR = 7 dB) & 6.40 & 4.27 & 0.48 \\ PC (CR = 6 dB) & 5.83 & 4.84 & 1.33 \\ PC (CR = 5 dB) & 5 & 5.67 & 3.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative peak-to-average power ratio (PAPR) gain at CCDF = \(10^{-3}\) and BER loss at \(10^{-3}\) with the PC method and Selective Mapping Technique (SLM) variants. CMSLM: conversion matrices Selective Mapping Technique, GA-SLM: Genetic Algorithm Selective Mapping Technique.  
From this comparison, we refer to the work of [PERSON] el al. [36] in which they drew the curves of BER versus SNR for a value of CR equal to 10 dB and IBO = 5 dB. The first remark to draw our attention states that that the CR rate equal to 10 dB brings no gain in terms of PAPR; see Figure 4 in [36]. As a comparison with the results shown in this paper, we present the curves of the BER with the same parameters (i.e., IBO = 5 dB, CR = 10 dB, with the 16-QAM modulation over the AWGN channel). According to the results depicted in Figure 22, our method presents a good performance compared with the four clipping methods.
## 3 Results and Discussion
### Effect of Modifier Mass Ratio and Dosage of FeBC on Cr(VI) Removal  
The removal efficiency of Cr(VI) by BC was only 4.57%, while the Cr(VI) removal capacity significantly increased due to the Fe(NO\({}_{3}\))\({}_{3}\) modification. The effects of modifier mass ratio on the Cr(VI) removal efficiency are shown in Figure 5a. FeBC, with a mass ratio (Fe(NO\({}_{3}\))\({}_{3}\)/BC) of 0.4:1, showed the best adsorption effects with a removal efficiency of 99.5%. This implied that the modification of Fe(NO\({}_{3}\))\({}_{3}\) promoted the microstructure of BC and increased its adsorption sites. However, when the modifier mass ratio exceeded 0.4:1, the removal efficiency of Cr(VI) did not increase. This may be due to the fact that excess  
Figure 4: FT-IR spectra of BC and FeBC.  
Figure 3: XRD patterns of BC and FeBC.  
metal ions blocked the formed micropore, indicating that the modifier mass ratio had the best modification effect within a certain range.  
A key factor affecting the Cr(VI), removal by FeBC was the dosage. As shown in Figure 5b, the removal efficiency increased from 23.9% to 99.9% with the FeBC dosage increasing from 1 to 10 g\(\cdot\)L\({}^{-1}\). The increase of FeBC dosage provided more active sites, accelerating the removal rate and improving the removal efficiency [22].
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
###### Abstract  
A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by [PERSON] which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.  
multi-target tracking; random finite set; labeled multi-Bernoulli; track before detect +
Footnote †: journal: Applied Sciences
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 2 Circuit Design  
Based on the 0.5 \(\upmu\)m GaAs E/D pHEMT process, a multi-function chip (MFC) is designed; the diagram is shown in Figure 1. The MFC comprises two channels (CH); each one consists of a 6-bit digital phase shifter (PHS), a 6-bit digital attenuator (ATT), and a single pole single throw (SPST) switch. The phase and the amplitude of RF signals are controlled by the PHS and ATT. The on-off state of each channel is chosen by an SPST switch. The 26-bit serial-to-parallel converter (SPC) is needed to control RF Field Effect Transistors (FETs) of the PHS, ATT, and SPST switch in two channels, and another bit is used to output differential voltages to control an off-chip switch. In total, a 27-bit serial-to-parallel converter is designed to decrease the control pads of the chip.  
The summary of the link budget analysis is also shown in Figure 1. Based on the demand of the actual application, no additional driver amplifies need to be designed in the MFC. The total gain of the receiving channel is sufficient with the introduction of the Low Noise Amplifer (LNA) at the RF frontend. On the other hand, based on the 0.5 \(\upmu\)m GaAs E/D pHEMT process, the noise performance of LNA is no longer competitive, so that the LNA at the RF frontend is not integrated in the MFC and is designed in another process.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 2 Related Work  
### Single Image Dehazing  
Most of the existing single image dehazing methods are based on the atmospheric scattering model. The most commonly used estimation methods can be roughly divided into two categories, namely, a priori information-based methods and learning-based methods.  
A priori-based dehazing: [PERSON] et al. [4] observed haze-free images and found that there was always a channel in each image phase with a low gray value that approached 0, therefore, they proposed a dark-channel a priori knowledge method. [PERSON] et al. [10] converted the regularization of the atmospheric scattering model into the problem of maximum posterior probability and then regularized the probability model to obtain image depth and the transmission map. In [11], [PERSON] et al. restored the depth information using color attenuation a priori information. In [12], the inherent boundary constraint of the transfer function was used in the estimation process, which is called context regularization. In nonlocal image dehazing as described in [13], which is based on a priori knowledge, the number of different colors in a haze-free image is much lower than the number of pixels in the image. All of these methods use some prior knowledge to estimate A and t in the atmospheric scattering model of Equation (1), so the final effect of the dehazing depends on the accuracy of the estimations of A and t based on a priori knowledge. In this way, physical models that are more in-line with reality have great potential in cases involving heavy haze, but generally require more time to accomplish dehazing.  
Learning-based dehazing: Inspired by the successful application of deep learning technology in image-style transfer, super resolution and image denoising, increasing numbers of researchers proposed new learning-based algorithms to solve the problem of image dehazing. In [14], [PERSON] et al. used a multi-scale convolution neural network (CNN) to estimate the transmission map. [PERSON] et al. [15] proposed an end-to-end dehazing model based on CNN called DehazeNet, alongside a nonlinear activation function called bilateral rectified linear unit (BReLU). [PERSON] et al. [16] proposed unification of A and t through a K(x) estimation module and then completed the reconstruction of clear images based on K(x). In [17], a method of haze image restoration based on a threshold fusion network was proposed, consisting of an encoding and a decoding network. By means of significant training, these methods can directly estimate A and t without a priori information.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 4 Depth Distribution Algorithm Based on Linear Block Codes
### Depth Distribution Algorithm Identifies Bit Rate and Generation Matrix
#### 4.3.1 The Recognition of Rate \(t\)  
For the case of no error codes, on the premise that the code length and the starting point of the code word are known, if there is only a \(k\) value in the depth spectrum of \((n,k)\) linear codes, the code rate is:  
\[t=\frac{k}{n} \tag{20}\]  
Under the same conditions, according to Theorem 1, the distribution of code words corresponding to the non-zero depth value of linear block codes is characterized by a two-times increase in the number of code words corresponding to the depth value with the increase in the depth value. This characteristic is used to verify the situation of error codes. Due to the existence of the error, if there appears in the depth of a non-zero value and its previous adjacent depth of non-zero values a code word number that is not about two times bigger, which shows that the depth value of the corresponding code words exist in error the ones when the ratio is far greater than or less than two times, it shows that the depth value of the corresponding code word code element is mostly an error and we then reduce the error. The remaining types of non-zero depth values are \(k\) and the bit rate is \(t=\frac{k}{n}\).
## 3 Congestion Prediction Model
### Traffic Generation  
Cross-Classification Analysis, also known as Category Analysis, is a way to predict future network traffic by analyzing the incidence of average network traffic, the results of which will be different depending on various characteristics related to the occurrence of network traffic. The total traffic production of some areas in cross-classification analysis are calculated as the sum of the per-node traffic generation, and can be expressed as in Equation (6) below:  
\[O_{i}=\sum_{h}C_{h}R_{h} \tag{6}\]  
* \(O_{i}=\) The total traffic generation of area \(i\)
* \(C_{h}=\) The number of nodes belonging to the category \(h\) of the characteristics that are classified
* \(R_{h}=\) The average traffic generation of nodes belonging to the category \(h\)  
Figure 4: 4 Steps of traffic demands analysis.
## 1 Introduction
The simulation results have revealed the fast transient unsteady flame kernel formation and propagation with a cavity-stabilized mechanism, while the chemical reaction and the heat transfer characteristics are also discussed. Noise disturbance exists in the combustion chambers of aircraft engines, gas turbines and other devices, which may cause serious combustion instability and lead to safety accidents [18; 19; 20]. Intensive investigations have been conducted to reveal the complex interactions between acoustic perturbation and flame dynamics [21; 22]. With acoustic perturbation, the flame may show dramatic changes in both structure and color [23; 24]. The large-scale vortex structure in the flow field is a common phenomenon under the acoustic effect, which will enhance the mixing of fuel and air, thus affecting the heat release rate as well as the flame dynamics [25; 26; 27]. [PERSON] et al. [28] compared the effects of different acoustic amplitudes on flame transfer function and flame dynamics. The results indicate that an acoustic wave would not only have a linear effect on flame, but also have a nonlinear effect. [PERSON] et al. [29] studied the influence of longitudinal acoustic disturbances of different frequencies and amplitudes applied to the fuel side on a jet methane flame. It was found that under an acoustic disturbance of a certain frequency, an ethylene flame would produce a half-frequency phenomenon; that is, it would oscillate at half the frequency of an external acoustic excitation. Other than half-frequency, more nonlinear interactions in non-premixed jet methane flames have been reported by [PERSON] et al. [30], including sum and difference between the natural oscillating and external acoustic excitation frequencies. [PERSON] et al. [22] studied the dynamic response and blowoff characteristics of a premixed methane-air flame to acoustic disturbances in a longitudinal combustor. The results indicated that the strong velocity fluctuation caused by the acoustic wave is the main reason for blowoff. As aforementioned, the study of the ignition process has been intensively studied in the static environment and in jet flames. However, the acoustic effect on the transient ignition process in jet flames is still rarely reported. Since the ignition process occurs in a very short time, it requires high frequency diagnostics to reveal the flame kernel formation and propagation process. In the present study, the ignition process under different frequencies of acoustic excitation was visualized and quantitatively analyzed based on high-speed color flame images and schlieren images. Quantitative velocity information has been estimated from schlieren images. Both the successful and the failed ignition processes are presented and compared. The important role that acoustic excitation plays in the ignition process has been revealed by the experimental evidence.
## 4 Discussion  
Neutron Bragg-edge imaging allows us to film phase changes during the welding of martensitic steel and to film the spatial spread of the heat-affected zone during gas tungsten arc welding. The use of a polychromatic beam results in a four times higher temporal resolution and in an order of magnitude higher signal-to-noise ratio. This leads to a significantly better image quality than for monochromatic imaging. Further, imaging with a polychromatic beam allows us to film the spreading of the heat-affected zone that corresponds to the moving welding torch. The use of monochromatic neutrons allows for phase-specific imaging, but neutron scattering by thermally induced lattice vibrations may influence the image quality.  
In this investigation, the lattice vibrations at high temperatures disturbed the monochromatic imaging. From this, it follows that it should, in principle, be possible to measure the temperature of a sample by energy-selective neutron radiography [19] using the temperature dependence of the Debye-Waller factor. A prerequisite for such measurements is the knowledge of these temperature dependence data for all involved crystallographic phases. However, systematic studies of the temperature dependence of such data are not known to the authors up to now.  
Generally, NBEI is a useful tool to study in situ phase changes in metals. This might help in clarifying material-related problems, e.g., in components that show strain-induced phase transformations [20] during service. For selected cases, NBEI is a useful non-destructive testing method that might help in damage prevention and damage handling.  
Conceptualization, A.G. and N.K.; methodology, N.K.; software, B.P.; validation, B.P., N.K. and A.G.; formal analysis, B.P.; investigation, B.P., U.A.T. and A.G.; resources, A.G.; data curation, B.P.; writing--original draft preparation, A.G.; writing--review and editing, N.K. and A.G.; visualization, B.P.; supervision, A.G.; project administration, A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The BAM authors thank the Helmholtz-Zentrum Berlin fur Materialien und Energie for the allocation of neutron radiation beamtime.  
The authors declare no conflict of interest.