esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Their equations, and the 3D approach employed in them, provide estimates of wave characteristics while accounting for variable propagation angles and travel distances, thus covering a wide range of water-basin geometries. The main inputs into their set of equations are landslide properties and a representative lake depth. Their workflow provides the first estimate of impulse wave celerity and waves run-up. [PERSON] et al. [18] suggest a classification of mountain lakes based on their impulse wave potential, which they derived from both subaerial and subaqueous mass movements at Swiss perialpine lakes >1 km\({}^{2}\). Their method relies on parameters calculated from digital elevation data (using geospatial software), for instance, topographic surfaces and bathymetry, together with seismologic data (e.g., the local acceleration), considered as an external factor for landslide initiation. They argue that lakes in the Alps have a high potential for impact by subaerial or subaqueous mass movements due to the surrounding steep slopes and fjord-like morphology of the lake basins compared to perialpine lakes. The latter lakes have a lower potential for subaerial mass movement, but a larger potential to inundate surrounding areas because of the typically lower relief surrounding them. Despite the simplicity of diverse approaches available in the literature, there are some limitations, such as the technical knowledge required to properly use these tools and limited availability of high-resolution geospatial data--particularly bathymetry--which may necessitate the use of lower-quality digital elevation data. In this paper, a simple alternative method, based on lake shape and extent, for estimating impulse wave propagation in mountain lakes (Figure 1) is presented. The method has a short workflow based on analytical equations derived from the analysis of wave characteristics and estimated using hydrodynamic numerical models in Flow-3D for theoretical mountain lakes. A fixed impact volume is used to induce the impulse wave. In comparison to other approaches, the equations are easy to apply. The variables required are the geometric characteristic of lakes; no specifics of the impacting landslide are needed. We describe the near-shore impulse-wave magnitude in terms of flow depth at the shoreline. This novel approach enables rapid, resource-efficient, preliminary investigations that may be required to identify basins of particular concern or high risk and to justify more detailed data collection and analysis. An Excel spreadsheet, as a Computational tool, comprising the presented workflow is available in the Supplementary Materials.
## 2 Materials and Methods
### Analysis of VOCs Using Gas Chromatography Time-of-Flight Mass Spectrometry (GC-TOF-MS)  
Solid-phase microextraction (SPME) analysis was performed following an established protocol [42] using GC coupled with TOF-MS (LECO Pegasus GC HRT, Leco Corporation, St. Joseph, MI, USA). A freeze-dried sample (10 mg) was mixed with 2 mL of 30% NaCl as the saturated solution, and 2 \(\mu\)L of 0.2 mg/mL 3-pentanol was used as the internal standard. The SPME holder equipped with a 50/30 \(\upmu\)m fiber (DVB/CAR/PDMS, model 57348-U, Supelco, Bellefonte, PA, USA) was used for sampling. Before use, the fiber was conditioned at 250 \({}^{\circ}\)C for 5 min. Aromatic compounds were extracted through SPME fiber at 70 \({}^{\circ}\)C and a stirring rate of 500 rpm for 20 min. After stirring, GC analysis was performed using a capillary column (Rtx-5 MS column, 30 m length, 0.25 mm diameter, 0.25 \(\upmu\)m thickness, 5% diphenyl, 95% dimethyl polysiloxane, Restek, Bellefonte, PA, USA) under split mode (30:1). The front inlet temperature was set at 240 \({}^{\circ}\)C and the transfer line temperature was maintained at 250 \({}^{\circ}\)C. The mass range was from 36 to 450. The ion source temperature was kept at 250 \({}^{\circ}\)C with an ionization voltage of 70 eV and the helium was used as carrier gas at a flow rate of 1 mL/min. The oven temperature program was 60-150 \({}^{\circ}\)C at the rate of 13 \({}^{\circ}\)C/min and then 150-180 \({}^{\circ}\)C at the rate of 8 \({}^{\circ}\)C/min, and it was then programmed to 180-200 \({}^{\circ}\)C at the rate of 10 \({}^{\circ}\)C/min and finally reached 245 \({}^{\circ}\)C at the rate of 30 \({}^{\circ}\)C/min. It was then held there for 3 min.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Network Topology  
The network topology for performance evaluation is presented in Figure 3. The proposed tunnel-free protocol with DMM was assessed with other protocols. Since for MIPv6 and PMIPv6, there exists no benchmark deployment model [25], we assumed that BMA was in the similar point as the AR and MAG in MIPv6 and PMIPv6, respectively. In this situation, distance \(\mathrm{d_{HA-AR}}\) was considered to be larger than \(\mathrm{d_{MAG-LMA}}\).  
Figure 2: Handover operation of the proposed approach. BA: binding acknowledgment, BU: binding update, NBAck: new binding acknowledgment, NBU: new binding update, RA: router advertisement, RS: router solicitation.  
Parameters and notations used,
* \(d_{L2}\): handover delay of link layer depending on the chipset implementation for a wireless interface.
* \(d_{A}\): link layer authentication latency.
* \(d_{MD}\): movement detection process delay.
* \(d_{DAD}\): duplicate address detection delay.
* \(d_{N}\): new binding update and acknowledgement process delay.
* \(d_{R}\): delay of proposed protocol's handover registration.
* \(d_{S}\): arrival delay of first packet of previous session from current BMA to the MN.
* \(m_{RS}\), \(m_{RA}\): RS/RA message size = 52, 80 bytes.
* \(m_{BU}\), \(m_{BA}\): BU/BA message size = 56, 56 bytes.
* \(m_{NBU}\), \(m_{NBAck}\): NBU/NBAck message size = 56, 56 bytes.
* \(m_{D}\): data packet size = 1 kbytes.
* \(m_{M}\): mobility option for an address bound to the MN = 20 bytes.
* \(b_{WL}\): bandwidth of wireless link.
* \(t_{WL}\): propagation time for the wireless link.
* \(b_{WD}\): bandwidth of wired link.
* \(t_{WD}\): propagation time for the wired link.
* \(p_{f}\): link failure probability over wireless link.
* \(d\): the number of hops between the CN and current BMA.
* \(S(L)\): average session length in data packet.
* \(i\): inter-packet time.
## 4 Numerical Results
### Effects of the Speed-Dependent Characteristic of Journal Bearings
The parameter \(\alpha_{k}\) of both cases equals 1.0, and the parameter \(\beta_{k}\) of the fixed case is 0.2. Then, the effects of the speed-dependent characteristic on the eigenvalues are discussed. Figure 12 illustrates the evolution of the natural frequencies versus the rotating speed, where both the fixed and the speed-dependent cases have been included. As is shown in the figure, the evolution lines of natural frequencies of the speed-dependent case are remarkably different from that of the fixed case, except the first natural frequency. The reason of this phenomenon is similar to the result shown in Figure 8. Besides, the most conspicuous feature in the diagram is that the lines of the fixed case are all straight, whereas that of the speed-dependent case become curved lines. This is caused by the variation of the stiffness and damping coefficients. In the range of low speed, the coefficients decrease rapidly but with decreased gradients. As a result, the second and higher order natural frequencies are also decreased with decreased gradients. In the range of higher speed, the variations of the coefficients tend to be slow and smooth, so that the evolution lines gradually get straight. As the differences between the stiffness coefficients of the two cases are almost invariant in this range, the evolution lines are almost parallel. Nevertheless, the torsional natural frequencies are not affected by the speed-dependent characteristic. Figure 13 shows the Campbell diagrams of the speed-dependent cases with \(\alpha_{k}=0.5\) and \(\alpha_{k}=1.0\), respectively. This figure is illustrated to show the effects of the anisotropy in the stiffness coefficient. It can be seen that the anisotropy of bearings leads to relatively obvious changes in the natural frequencies of higher orders. Consequently, the 2\(\times\), 3\(\times\) and higher order critical speeds will be affected. Therefore, it can be concluded that the speed-dependent characteristic of bearings can also significantly affect the dynamic response of a cracked rotor-bearing system, and it should be carefully taken into consideration to accurately investigate the vibration of cracked rotor-bearing systems. Figure 11: The evolution of the whirling orbits of the central node of the anisotropic rotor-bearing system with a 20-mm crack. and denote the results of the fixed and speed-dependent cases, respectively. Figure 12: The Campbell diagram of the rotor-bearings system with a 20-mm crack. \(\blacktriangledown\) and \(\blacklozenge\) denote the results of the fixed and speed-dependent cases, respectively. The parameter \(\alpha_{k}\) of both cases equals 1.0, and the parameter \(\beta_{k}\) of the fixed case is 0.2. Figure 13: The Campbell diagram of the rotor-bearings system with a 20-mm crack. \(\blacktriangledown\) and \(\blacklozenge\) denote the results of the speed-dependent cases with \(\alpha_{k}=0.5\) and \(\alpha_{k}=1.0\), respectively.
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 2 Qualitative Studies  
### Professional User Questionnaire  
An online questionnaire was created using the survey tool Qualtrics [22], which asked experts to describe the sound quality of the 1176 when compressing vocals, drum shells (bass and snare drums), room mics (ambient recordings of the drums in a room) and a bass guitar. Judgement sampling (as opposed to random sampling) was used to select experienced engineers and academics to complete the questionnaire. For an expert to be included they had to be knowledgeable in music production and familiar with the 1176. Judgement sampling does, however, have its limitations and is prone to bias [23]. Thirty-five respondents completed the questionnaire.  
The results in Table 1 presents the ten most frequently used descriptors to describe the sound quality of the 1176 compressor for all four sound sources. Aggressive is the most popular word and is investigated in the current study. The other descriptors are likely to refer to amplitude modulation effects (pumping), transient reshaping (punch), colouration (forward, full, midrange and presence) and distortion (dirty and gritty). One would expect fast to be a description of the time constant speed, and not necessarily a description of sound quality.  
Table 2 shows the most common words used to describe the sound quality of the 1176 when compressing a vocal, which is the main focus of this article. To reduce the number of words in the table, only those recorded more than once have been included. As can be seen, the descriptor aggressive is, again, the most popular, followed by the word gritty, which, as stated previously, is arguably a synonym for distorted. Other descriptors appear to refer to coloration (forward, midrange, presence, full, upfront and sparkly), amplitude modulation (pumping) and potentially the perceptual effect of the attack and release curve (smooth).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Descriptor** & **Frequency of Occurrence** \\ \hline Aggressive & 21 \\ Pumping & 11 \\ Forward & 10 \\ Punch & 8 \\ Full & 8 \\ Midrange & 8 \\ Fast & 7 \\ Presence & 7 \\ Dirty & 6 \\ Gritty & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The top ten most frequently used descriptors to describe the sound of the 1176 compression.
## 2 Nonlinear Damage Theory  
At first, [PERSON] and [PERSON] proposed the nonlinear damage accumulation theory based on the damage curve method. [PERSON] and [PERSON] established the fatigue cumulative damage model (Manson-Halford model, also known as the M-H model), which was obtained with the damage curve method. In this model, the relationship between crack length and cycle ratio can be defined as below.  
\[a=a_{0}+(0.18-a_{0})\left(\frac{n_{a}}{N_{f}}\right)^{\frac{2}{3}N_{f}^{0.4}} \tag{1}\]  
where \(a\) is the crack length, \(a_{0}\) is the characteristic defect length, \(a_{0}=0\) at the moment of impending cracking, \(n_{a}\) is the number of cycles to reach a certain crack length, and \(N_{f}\) is the fatigue life corresponding to load levels.  
The damage degree can be expressed as the functions of crack length and cycle ratio as follows:  
\[D=\frac{1}{0.18}\cdot\left[a_{0}+(0.18-a_{0})\left(\frac{n_{a}}{N_{f}}\right) ^{\frac{2}{3}N_{f}^{0.4}}\right] \tag{2}\]  
where \(D\) is the damage degree \(D=(1-a_{0}/0.18)\left(\frac{n_{a}}{N_{f}}\right)^{\frac{2}{3}N_{f}^{0.4}}\) and \(D=(1-a_{0}/0.18)\left(\frac{n_{a}}{N_{f}}\right)^{\frac{2}{3}N_{f}^{0.4}}\) at the moment of impending cracking.  
Based on the parameters and damage theories above, the cumulative damage is shown in Figure 1 due to multistage loading. First, the stress level \(\sigma_{1}\) was applied for \(n_{1}\) times. Then, another stress level \(\sigma_{2}\) was applied instead of the stress level \(\sigma_{1}\), followed by \(n_{2}\) times. The total cumulative damage can be deduced as follows.  
\[D=\frac{n_{1}}{N_{f1}}+\frac{n_{2}}{N_{f2}} \tag{3}\]  
According to the damage characteristics of the material, the damage should be equal at points A and B, as shown in Figure 1. Based on Equation (2), it can be calculated that the damage corresponding to the stress level \(\sigma_{1}\) acting on \(n_{1}\) is equivalent to the damage corresponding to the stress level \(\sigma_{2}\) acting on \(n^{\prime}{}_{2}\), which can be expressed as follows:  
\[\frac{n^{\prime}{}_{2}}{N_{f2}}=\left(\frac{n_{1}}{N_{f1}}\right)^{\left(\frac {N_{f1}}{N_{f2}}\right)^{0.4}} \tag{4}\]  
Figure 1: Cumulative damage under multi-stage loading.  
When the stress level \(\sigma_{2}\) acts on \(n_{2}\) times, the total damage amount can be written as  
\[D=\left(\frac{n_{1}}{N_{f1}}\right)^{(\frac{N_{f1}}{N_{f2}})^{0.4}}+\frac{n_{2}}{ N_{f2}} \tag{5}\]  
Similarly, corresponding to the action of load level 3 and above, its cumulative damage can be expressed as follows:  
\[D=\left\{\left[\left(\frac{n_{1}}{N_{f1}}\right)^{(\frac{N_{f1}}{N_{f2}})^{0.4 }}+\frac{n_{2}}{N_{f2}}\right]^{(\frac{N_{f2}}{N_{f2}})^{0.4}}+\cdots+\frac{n_ {i-1}}{N_{fi}}\right\}^{(\frac{N_{f(i-1)}}{N_{fi}})^{0.4}}+\frac{n_{i}}{N_{fi}} \tag{6}\]  
Consequently, when the cumulative damage \(D\) reached the critical value, fatigue damage occurred to the material. When the stress levels at all levels were the same, the fatigue life of the corresponding stress at all levels was also the same, and then Equation (6) degenerated into a linear cumulative damage model.
## 4 Intelligent Controller Design
### Genetic Algorithm  
The genetic algorithm is an evolutionary computing algorithm inspired by the principle of evolution [13]. Figure 5 depicts the procedure in this paper that the genetic algorithm follows. From the beginning, all strings in the first generation are randomly generated in a sample space \([-1,1]^{p}\), where \(p\) is the number of the parameters, using a uniform distribution. The number of strings in the population must be large enough to ensure the algorithm convergence. Thus, this paper uses a population size of 300 strings after some preliminary studies. The main loop includes three steps. First, all strings in the population are subsequently simulated with the landing gear model. Next, the fitness of each string is calculated following Equation (11). The top ten strings, which claim the highest fitness values, are subsequently chosen as potential parents. Then, these parents are compared with the parents in the previous generation to choose the best 10 parents for creating the next generation. The next generation  
Figure 4: Landing gear learning process.  
Figure 3: Neural network structure.  
involves 100 new strings, which are also randomly generated using a uniform distribution, and 200 strings, which have evolved from the best parents, as can be seen in Figure 6. Both crossover and mutation are used in the evolution from the parent strings.
###### Abstract  
Cellular internalization and the spreading of misfolded tau have become increasingly important for elucidating the mechanism of Tau pathology involved in Alzheimer's disease (AD). The low-density lipoprotein-related receptor 1 (LRP1) has been implicated in the internalization of fibrillar tau. In this work, we utilized homology modeling to model the Cluster 2 domain of LRP1 and determined that a 23-amino-acid sequence is involved in binding to paired helical filaments (PHF) of Tau. Fourteen short peptide segments derived from this ectodomain region were then designed and docked with PHF Tau. Molecular dynamics studies of the optimal peptides bound to PHF Tau demonstrated that the peptides formed critical contacts through Lys and Gln residues with Tau. Based on the computational results, flow cytometry, AFM, SPR analysis and CD studies were conducted to examine binding and cellular internalization. The results showed that the peptide sequence TauRP (1-14) (DNSDEENCES) was not only associated with fibrillar Tau but was also able to mitigate its cellular internalization in LRP1-expressed HEK-293 cells. Preliminary docking studies with A\(\beta\) (1-42) revealed that the peptides also bound to A\(\beta\) (1-42). While this study focused on the CCR2 domain of LRP1 to design peptide sequences to mitigate Tau internalization, the work can be extended to other domains of the LRP1 receptor or other receptors to examine if the cellular internalization of fibrillar Tau can be deterred. These findings show that short peptides derived from the LRP1 receptor can alter the internalization of its ligands.  
low-density lipoprotein receptor 1 (LRP1); cellular internalization; molecular dynamics; docking; Tau +
Footnote †: journal: Applied Sciences
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 3 Defect Inspection Based on Generative Models
### Defect Inspection Based on DMs  
Although it has been proven that the generation quality of DMs outperforms that of GANs and VAEs, the manufacturing field seems to pay little attention to DM-based defect inspection and sample generation. [PERSON] et al. [104] presented an unsupervised learning defect detection method on fabric surfaces, which improves the quality of image generation by optimizing the time step and utilizes the simplex noise DDPM (SN-DDPM) to reconstruct defect images, thus realizing accurate segmentation of defects. [PERSON] et al. [105] proposed a hybrid DL framework based on DDPM for data enhancement and a deep residual neural network (DenseNet) for defect classification on composite panel surfaces. [PERSON] et al. [106] generated diverse defective images by training a DDPM model with limited samples for generalized surface defect inspection. In this method, an erasing-inpainting mechanism is proposed to promote the recombination of real features and learned features, thus fully using the limited defect samples. [PERSON] et al. [107] introduced a stabilized diffusion enhancement method based on the improved YOLOv5 model for defect detection in metal stamping parts. This method introduces a lightweight bottleneck transformer structure and a multi-head self-attention mechanism to increase detection speed and accuracy.  
Table 5 summarizes the DM-based studies conducted for surface defect inspection. Although DMs have strong performance and can generate realistic images, the network structure of DMs is too heavy and the sampling time is too long, which requires a significant amount of computation resources and training time. There is room to reduce training difficulty and model complexity for DMs, resulting in applicability for industrial applications in the future.
## 2 Relevant Methods: State of the Art
As per their taxonomy, these methods belong to the space-covering group, which aims at implicitly exploring the whole feasible region. Such approaches could be iterative and, at the same time, non-gradient methods. These methods started with the simplex method [39] and the algorithms in [40; 41] and do not require calculating the gradient; they are still iterative methods. There are several modifications of the Powell method, such as COBYLA [42], which employs linear polynomial approximations to the loss and constraint functions by interpolation at the vertices of simplices. Another one-pass approach to calculating the mapping of two 3D objects is based on training a deep learning network. The trained network calculates the mapping in a non-gradient and non-iterative way. Such approaches could be found in [43; 44; 45; 46; 47], where the neural network is initially trained to extract local spatial features from each of the 3D objects under comparison. Then, the mapping is calculated based on these features. If the number of possible extrema at the object's surface is relatively small, it is not prohibitively expensive to find the matching of the extrema for both objects and check how good these mappings of the objects are. As the possible number of extrema combinations is assumed not high, as an option, the mapping can be found even by exhaustive search without the calculation of gradients in a non-iterative way. This is the idea of the proposed method for solving the identification problem and finding the mapping of the objects. Let us use a spherical transformation to map the 3D object in the feature space (encoding), and for partial reconstruction (decoding), let us define a 3D shortest distance function. The values of this function are treated as random values. The summation of random values gives us several extrema (see Sections 3.9 and 3.10). Calculating the mappings for the corresponding extrema for two 3D objects under comparison allows us to find the required transformation or tells us that the objects are different. In the \"Results and Discussion\" section, a comparison of the proposed method with a deep learning approach (using [43] as an example) is given.