esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement: Nonlinear Submerged Arches Design
### Structural Stability: Buckling Analysis of submerged Arches  
Structural stability represents a type of structural analysis which explores the behaviour of the structures subjected to compression loads. In essence, an equilibrium state achieved under compression load is considered unstable when the original equilibrium state is not achieved after removing the compression load.  
In case of plastic behaviour, equilibrium state is assumed to be stable, if a structure even tends to return to the initial equilibrium state [32]. Nevertheless, elastic members subjected to static loads are only considered in this work.  
The minimum force at which the structure no longer returns to the initial state, if all disturbing factors are removed, is the so-called critical force. If we note as \(N\) the internal axial force due to an external load, then the linear buckling load factor (\(\alpha_{crit}\)) can be expressed as  
\[\alpha_{crit}=\frac{N_{crit}}{N} \tag{2}\]  
in which \(N_{crit}\) is the critical axial force. Then, for \(\alpha_{crit}<1\), the buckling phenomenon occurs in the arch structure, while for \(\alpha_{crit}>1\), the stability is guaranteed, as the axial force along the structure is lower than the critical axial force. As a general rule, a loss of stability produces a change in the initial form of equilibrium or buckling, that usually leads to the structural collapse.  
The critical load of the arches may be determined in an analytical way only in the simplest cases, such as uniform circular arches subjected to uniform pressure normal to the axis. Thus, [PERSON] [33] only provided analytical solution of the buckling problem for circular two-hinged arches subjected to a uniform distributed radial pressure. Approximate methods of solving stability problems for arches consist of approximating the arch by a framed structure, and proceeding with analysis by the displacements method (also called the eigenvalue buckling test [34]).  
The importance of considering the nonlinear effects in the submerged arch structure has been shown in [18]. Particularly, the arches corresponding to shallow waters usually present linear buckling factors lower than 1.0, thus becoming unstable under design loads. This leads to modify the slenderness of the arch (i.e., augmenting its thickness), which implies large volume and, consequently, large spans due to self-weight effect in the funicular equilibrium.  
This last problem may be approached in an alternative way through a shape parameterization of the submerged arch. This strategy allows searching the optimal shape and, simultaneously, controlling its second-order behaviour during the optimization process, as explained below.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 4 Discussion
### Landslides  
A landslide on the Marsyangdi River that occurred three to six days after the 2015 Gorkha earthquake partially constricted the channel and formed a landslide-dammed lake [5]. We estimated the volume of this landslide to be 83,100 m\({}^{3}\) from the UAV (April 2018) and HMA (March 2014) DEM of difference (Figure 7). A DEM of difference between two HMA DEMs (May 2015 and March 2014) suggested a mass loss of 68,000 m\({}^{3}\) for the same landslide. The time difference between the DEMs precludes a direct comparison between these volumes; however, it is possible that additional slumping occurred in the additional three years before the UAV survey, which could explain the larger estimated volume loss.  
Figure 9: Simulated landslide-dammed lake outburst floods on the Marsyangdi River. Qp = 936, 468, 234, and 1874 m\({}^{3}\) s\({}^{-1}\) for simulations 1–4 respectively.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 2 Materials and Methods
### Accuracy Assessment
For the uncertainty resulting from the glacier area determination (\(\delta_{A}\)) we take a mean value of 0.6%. This value is justified since previous detailed analysis by [PERSON] and others [35] revealed an error of 3% for alpine catchments. However, their area to perimeter ratio is much larger (5.03) than in our case (0.99) corresponding to the dominating effects of a scattered glacier coverage and long valley glaciers in contrast to a more compact icefield with outlet glaciers in our case. As a consequence, the outline error contributes much less to the overall error in our case. Regarding volume-to-mass conversion, we adopt the density uncertainty of \(\pm\)60 kg\(\cdot\)m\({}^{-3}\) given by [PERSON] [27]. We apply this for each of the density scenarios.
###### Abstract  
In order to study the applicability of Low Vibration Track (LVT) in heavy-haul railway tunnels, this paper carried out research on the dynamic effects of LVT heavy-haul railway wheels and rails and provided a technical reference for the structural design of heavy-haul railway track structures. **Methods**: Based on system dynamics response sensitivity and vehicle-track coupling dynamics, the stability of the upper heavy-haul train, the track deformation tendency, and the dynamic response sensitivity of the vehicle-track system under the influence of random track irregularity and different track structure parameters were calculated, compared and analyzed. **Results**: Larger under-rail lateral and vertical structural stiffness can reduce the dynamic response of the rail system. The vertical and lateral stiffness under the block should be set within a reasonable range to achieve the purpose of reducing the dynamic response of the system, and beyond a certain range, the dynamic response of the rail system will increase significantly, which will affect the safety and stability of train operation. **Conclusions**: Considering the changes of track vehicle body stability coefficients, the change of deformation control coefficients, and the sensitivity indexes of dynamic performance coefficients to track structure stiffness change, the recommended values of the vertical stiffness under rail, the lateral stiffness under block, and the lateral stiffness under block are, respectively 160 kN/mm, 200 kN/mm, 100 kN/mm, and 200 kN/mm.  
track structure parameters; heavy-haul railway; low vibration track; vehicle-track coupled system; dynamic response sensitivity
# Vision-Based System for Black Rubber Roller Surface Inspection
## 2 Visual Inspection System
### Defect Detection Based on Proposed Traditional Method
#### 2.3.2 Laplacian Filters  
A Laplacian filter or a Laplacian edge detector is used to compute the second derivatives of an image, measuring the rate of change of the first derivatives. This determines whether changing adjacent pixel values are from an edge or a continuous progression.  
The Laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian smoothing filter in order to reduce its sensitivity to noise. One  
Figure 6: (**a**) ROI (original image); (**b**) result of applying the power-law transformation; (**c**) gray scale image.  
of the main issues that could affect the results is random noise. All of the images may be affected by random and uniform noise due to the physical characteristics of rubber rollers, which must be removed. By using the Laplacian filter, the photos are smoother, and the sensitivity to noise is also significantly reduced.  
The Laplacian _L(x,y)_ of an image with pixel intensity values _l(x,y)_ is given by [13]:  
\[L(x,y)=\frac{\partial^{2}I}{\partial x^{2}}+\frac{\partial^{2}I}{\partial y^{2}} \tag{2}\]  
This can be calculated using a convolution filter. Since the input image is represented as a set of discrete pixels, we have to find a discrete convolution kernel that can approximate the second derivatives in the definition of the Laplacian. Two commonly used small kernels are shown in Figure 7.  
Using one of these kernels, the Laplacian can be calculated using standard convolution methods. The result of applying the Laplacian is shown in Figure 8 below.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 5 Component and Architecture of a LiFi System  
LiFi, as a potential and strong candidate in communication, is different from traditional Wi-Fi in many aspects, such as its components and architecture. Understanding such aspects will aid in comprehending and utilizing the technology efficiently in the future. Therefore, this section aims to elaborate and discuss the components and architecture of LiFi. A standard LiFi operating system involves propagation in one path, such as DL [89; 158; 159] or UL [34; 155] or where both of them are BID [78; 103; 160]. Power and data may be supplied to each light mixture through various approaches, including Power Over Ethernet (PoE) and Power Line Connectivity (PLC) [14]. An UL is introduced by utilizing a transmitter on the user equipment (UE) or sometimes using an IR source and a receiver near the light fixture. These light fixtures, which simultaneously serve as wireless LiFi APs, generate an incredibly tiny cell called attocell. This tiny cell has a high bandwidth density because of a single light source [161]. The key and typical components for data transmission and reception in DL and UL are specified in this section. The transmitter and receiver also run simultaneously between the UE and the AP. Similar techniques, processes, device architecture, and configurations were suggested by most researchers who worked on improving LiFi systems. Consequently, the most common components used in LiFi systems in DL and UL procedures are introduced in this section.
## 3 Materials and Methods
### EEG Data Preprocessing
#### 3.2.1 Discrete Wavelet Transforms  
Wavelet transforms are particularly valuable for EEG analysis because they capture both localized and global features of the signal at different scales. This capability enables the identification of transient events, such as spikes or artifacts, as well as the detection of rhythmic patterns and frequency changes associated with various mental states or neurological conditions. Classification tasks that utilize feature extraction with discrete wavelet transforms (DWTs) on EEG signals during complex tasks have demonstrated an accuracy of 98% using support vector machines, multi-layer perceptrons, and K-nearest neighbor classifiers [37].  
While DWT is not exclusive to EEG, it has also been shown to be effective for ECG (electrocardiogram) signal processing [38]. The \(Db2\) wavelet is particularly suitable for EEG analysis because it closely resembles the spike-wave patterns observed in EEG data [39].  
Additionally, level 5 decomposition with \(Db2\) provides valuable insights for preprocessing EEG signals, contributing to improved classification accuracy.  
The approximation coefficient (\(cA\)) and detailed coefficient (\(cD[1-5]\)) of the DWT signal of channel TP9 are plotted in Figure 1. The approximation coefficient represents the low frequency of the EEG wave capturing the overall trend or baseline activity. Detailed coefficients (\(cD[1-5]\)) represent the high-frequency components at different levels of decomposition. \(cD1\) captures high-frequency details at the first level, \(cD2\) refines the details further, \(cD3\) provides even finer resolution, and so on up to \(cD5\) (Level 5).  
Let us denote the EEG signal as \(x[k]\). The approximation and detailed coefficient at level 1 are represented in Equations (1) and (2).  
Approximation coefficient:  
\[a_{1}[n]=\sum_{k}x[k]*h[2n-k]. \tag{1}\]  
Detailed coefficient:  
\[d_{1}[n]=\sum_{k}x[k]*g[2n-k], \tag{2}\]  
where \(h\) and \(g\) are the low-pass and high-pass filters associated with the \(Db2\) wavelet, \(a1[n]\) is the approximation coefficient, \(d1[n]\) is the detailed coefficient, \(n\) is the index of the output coefficients at level 1, \(x[k]\) is the original signal at the sample index \(k\), and the factor \(2n-k\) ensures downsampling, as DWT operates on every second element to create the next level. This procedure can be implemented in Python using the library PyWavelet by calling pywt.wavedec.
## 3 Results
### Quantitative Results
#### 3.2.2 Object Detection  
We trained two neural networks with the brake pad dataset to demonstrate that the proposed data augmentation method affects object detection tasks. We tabulated average and standard deviations for all mAPs of trained YOLOv4 networks to determine how mAP changes with the IoU threshold. Each mAP and [EMAIL_ADDRESS]:0.95 by augmented data are shown in Table 10, and corresponding results by the original data are shown in Table 11. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**One-Channel**} & \multicolumn{2}{c}{**Three-Channel Grayscale**} & \multicolumn{2}{c}{**Three-Channel Color**} \\ \cline{2-10}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline [EMAIL_ADDRESS] & 0.894 & 0.044 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.857 & 0.072 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.817 & 0.079 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.727 & 0.092 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.588 & 0.101 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.397 & 0.049 & 0.998 & 0.001 & 1.000 & 0.001 & 1.000 & 0.000 \\ \hline [EMAIL_ADDRESS] & 0.224 & 0.061 & 0.986 & 0.010 & 0.996 & 0.001 & 0.997 & 0.001 \\ \hline [EMAIL_ADDRESS] & 0.077 & 0.033 & 0.963 & 0.016 & 0.989 & 0.001 & 0.988 & 0.001 \\ \hline [EMAIL_ADDRESS] & 0.016 & 0.015 & 0.726 & 0.030 & 0.884 & 0.026 & 0.884 & 0.018 \\ \hline [EMAIL_ADDRESS] & 0.001 & 0.001 & 0.150 & 0.062 & 0.272 & 0.069 & 0.293 & 0.063 \\ \hline [EMAIL_ADDRESS]:0.95 & 0.460 & 0.040 & 0.882 & 0.008 & 0.914 & 0.007 & 0.916 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: YOLOv4 object detection validation results with the augmented dataset. Figure 13: Boxplots of validation accuracy with Resnet18: (**a**) results validate with the augmented dataset; (**b**) results validate with the original dataset. All results show the phenomenon in which the IoU threshold increases and the mAP value decreases similarly. Table 10 shows the high mAPs and [EMAIL_ADDRESS]:0.95 of YOLOv4 networks trained with the three-channel augmented color dataset. Nevertheless, in Table 11, some average mAP of YOLOv4 networks trained with the three-channel augmented grayscale dataset has a higher average mAP than YOLOv4 networks trained with the three-channel augmented color dataset. However, the YOLOv4 networks trained with the three-channel augmented color dataset has the highest [EMAIL_ADDRESS]:0.95. The boxplot of mAP0.5:0.95 obtained from both datasets is shown in Figure 14.  
Figure 14 shows that the YOLOv4 networks can better infer performance when trained with the three-channel augmented color dataset than trained with the other datasets.
# Periodic Multilayer for X-ray Spectroscopy in the Li K Range
## 2 Materials and Methods  
Theoretical simulations of optical properties of multilayer stacks show that the structure Be (12.5 nm)/Si (2 nm)/Al (14.5 nm) has the highest reflection in the 47-57 eV range, at grazing angles around 30\({}^{\circ}\). This structure, where the order of the layers starts from the surface, was the aim. The penetration depth is quite small in this photon energy range, and simulations show that only a few layers are necessary, 30 periods (90 layers), to reach the maximum of reflectance. The reflectance is 99.7% of this maximum value for a stack with 20 periods (60 layers) and 93% for a stack with 10 layers (30 layers). Thus, the Be/Si/Al period was reproduced 20 times.  
The stack was deposited by \(dc\) magnetron sputtering on a smooth (0.1-0.2 nm) silicon substrate. Deposition system and the technological process for the synthesis of beryllium-containing structures are described in detail in [22]. In this experiment, high-purity (99.998%) argon at a pressure of \(\sim\)0.1 Pa was used during the sputtering process, while the base pressure was lower than 10\({}^{-4}\) Pa. The electric power on the magnetrons was 112 W for Be, 152 W for Al and 162 W for Si. In these conditions, the growth rate of the films was 0.06 nm/s for Be, 0.21 nm/s for Al and 0.16 nm/s for Si.  
Following deposition, the hard X-ray reflectivity of the sample was measured by means of a PANalytical X'PertPro four-crystal high-resolution diffractometer at the wavelength of 0.154 nm (Cu K\(\alpha\) radiation). The basic parameters of the structure, such as period, film thickness and interfacial roughness, were extracted from the fit of the experimental reflection curve, obtained in the 0-20 mode, using the Multifitting software for reflectometric reconstruction of multilayer structures [23]. Figure 1 shows the experimental angular dependence of the reflection coefficient and the result of the fit.  
The parameters of the synthesized structure are determined as Be (12.6 nm-1.4 nm)/Si (2.0 nm-0.8 nm)/Al (14.4 nm-1.6 nm), where the first number in parenthesis is the thickness of the layer and the second number its interfacial roughness. The uncertainty of the thickness and roughness is estimated to be 0.1 nm. The deposited thicknesses are closed to the intended ones and result in a period of the stack equal to 29 nm.  
X-ray reflectivity measurements in the Li K spectral range were performed on the BEAR beamline [24] at the Elettra synchrotron radiation facility. Incoming and reflected intensities were measured with a SXUV 100 photodiode with an integration time of 0.5 s. First, measurements as a function of the glancing angle in 0-20 mode were performed for six incident photon energies spanning the spectral range of the Li K emission. Monochromatic incident radiation was \(s\)-polarized. Second, measurements were performed as a function of the incident photon energy at given angles corresponding to the energy of the maximum of the reflectivity curves. Reflectance values are determined with an experimental uncertainty of 1%.
## 4 Design of Algorithms and Decision Variables  
The goal of our experiments was to develop an edge-assisted object detection system that is tailored around the needs of the user to minimize the E2E execution time or to maximize the accuracy of the system. Hence, in this paper, we follow an experimental approach where we use our testbed to evaluate an exhaustive list of possible configurations, i.e., different splitting decisions and compression values, with respect to accuracy and delay criteria. This analysis sheds light on the dependencies and conflicts of these two criteria and allows one to select, offline, a strategy that optimizes the criterion of their preference. To make this clear, we also introduce the respective optimization problems, albeit without providing analytical expressions for the metrics, for the reasons we explained above. The aim of this research was to find the optimal trade-off of the metrics to improve E2E execution time and the accuracy of the system. The key decisions here are how to split and offload the set of images, across the end devices. This decision is taken at the server, based on the solution of our properly defined optimization problems. In the experiments, we demonstrated that the parameters which affect and improve the operation of the system are the encoding rate \(q\) (compression value) and the batch sizes (splitting decisions) of the delivered images to every device. More specifically based on our measurements (Tables 1-4 and Figures 2-10) we argue that the parameters which affect the accuracy and execution time are: the encoding delay \(T_{enc}\), the transmission delay \(T_{tx}\), the inference delay \(T_{inf}\) and \(T_{dl}\) depend on decision variables \(q\) and the percentage of the total amount of images transmitted to each device \(a_{i}\). Therefore, the decision variables of our system are the encoding rate \(q\) and \(a_{1}\), \(a_{2}\), \(a_{3}\).  
It should be noted that we define our functions and our parameters in Table 5; these parameters were used for constructing our optimization problem. In more detail, we define:  
\[\sum_{i=1}^{3}a_{i}=1\qquad with\quad a_{i}\in[0,1] \tag{4}\]  
\[b_{i}=a_{i}*L \tag{5}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Image Set** & **Execution Time (s)** & **Average CPU Utilization (\%)** & **Precision** & **Recall** & **mAP** \\ \hline Image Set1 & 83.3 & 27.1 & 0.71 & 0.53 & 0.6 \\ \hline Image Set2 & 81.1 & 29.3 & 0.77 & 0.43 & 0.56 \\ \hline Image Set3 & 82.1 & 29.2 & 0.73 & 0.50 & 0.54 \\ \hline Image Set4 & 83.6 & 28.1 & 0.6 & 0.49 &  
\[T_{dl}(q,a_{i})=max([T_{enc}(q)+T_{tx}(q)+T_{inf}(a_{1});\] \[T_{enc}(q)+T_{tx}(q)+T_{inf}(a_{2}); \tag{6}\] \[T_{enc}(q)+T_{tx}(q)+T_{inf}(a_{3});)])\]  
In Equation (6), we calculate the three values of time from which we keep the largest value. More specifically offloading to every device in a workload means that we calculate three values of time from which we keep the time value
## 2 Background  
### Social Media, Topics, and Trends  
Social media (or SNS) are web-based services that allow individuals to create a profile, articulate a list of connections to other users, view and traverse such connections, and share content [10]. Although social media are today the primary source of information for many, the spread of information is still irregular, difficult to predict in nature, and incidental. Methods that aim to track and retrieve given conversations are required to be flexible and dynamic enough to be able to follow them with the minimum information loss.  
To be able to accurately track opinion over time has been one of the main concerns of analysts for a long time [11; 12; 13; 14]. With the advent of Twitter, public opinion can be tracked continuously and in real time. In fact, Twitter continuously publishes real-time produced lists of the most popular topics under discussion either locally or globally (also known as trending topics). Many users consider something to be news when it becomes a Twitter trending topic, even though the way Twitter produces these lists of trending topics is unknown, as the algorithm used remains unpublished. It is relevant, however, that although Twitter is assumed to classify trends according to every tweet published during a period, it only provides (via its API) a reduced sample of the entire stream for free. This limitation makes it difficult for those trying to extract trending topics from the whole data stream. However, our work does not aspire to replicate the way Twitter produces its trending topic lists, and assumes working on a subset of the complete data to be reasonable.  
Thus, Twitter produces trending topic lists from all users' tweets. However, the way information spreads in online social networks is more reminiscent of a complex contagion model where information diffusion is affected not only by the number of exposures to a piece of information but also by the exposure to multiple sources and their social influence [15]. Hence, users tend to follow other users on topics of their interest in order to acquire information on those topics. These later users, leaders of opinion or \"authorities\", are sources of information for many individuals in this media environment, and consequently, become more influential, and the information diffused by them more \"viral\" [1].  
As mentioned, our work aims not at detecting global trending topics but instead at obtaining the relevant topics of discussion among a set of opinion leaders that we call \"authorities\", and adapting the tracked keywords accordingly to retrieve the most relevant information throughout the duration of the monitoring. As a result, we will be able to: (a) observe how the topics of interest addressed by a set of authorities evolve; and (b) to extract the conversations linked to those topics, discarding any others, which will tell us how authorities' public discourse has changed over time.