esa-sceva/satcom-chunk-collection huggingface dataset:
## Case Study
### Deformation Reliability Assessment  
We illustratively chose the monitored data at location QCX04 (c.f. Figure 4a) to perform deformation reliability assessment. The horizontal displacements measured at different depths on each day are shown in Table 2. Note that the depths in Table 2 were calculated from the bottom, which would equal 43.5 m at the top (ground surface).  
The fitting of \(\mathbb{E}(S(x))\) is performed using a polynomial, with which \(\Omega(x)\) and \(Y(x)\) in Equations (13) and (24) can be subsequently determined. The fitting accuracy is evaluated by the determination coefficient \(R^{2}\) in Equation (17). Using the monitoring data from the subset of 1/4-3/4 of the depth, the dependence of \(R^{2}\) on the order of polynomial (c.f. the term \(m\) in Equation (14)) is shown in Figure 5. It can be seen that when \(m\geq 2\), \(R^{2}>0.9\), fairly close to 1. While a greater value of \(m\) will lead to a greater value of \(R^{2}\), the [PERSON]'s phenomenon could occur as \(m\) is sufficiently large. Thus, \(m\) was chosen as 4 for the following analyses. Furthermore, assuming that the displacement limit \(R\left(x\right)\) is 73.5 mm, \(\Omega\left(x\right)\) can be determined according to Equation (4). The derivative of \(\Omega(x)\) gives \(\dot{\Omega}(x)\). The variances of both \(Y(x)\) and \(\dot{Y}(x)\) were computed with Equations (15) and (16). With this, the deformation reliability was found to be \(\mathbb{L}_{\text{mme}}(43.5)=0.956\) according to Equation (13). That is, the probability that the maximum lateral displacement of the retaining wall at location QCX04 does not exceed the predefined limit is 95.6%.  
Next, the impact of ME on the deformation reliability is discussed. As introduced in Table 1, the inclinometer CX-06B used in this project has a measurement accuracy of \(A=4\) mm\(/15\) m \(=2.667\times 10^{-4}\). With this, applying Equation (23) yields \(\mathbb{L}_{\text{pc}}(43.5)=0.518\). Furthermore, with Equation (24), the deformation reliability taking into account the impact of ME is estimated as \(\mathbb{L}_{\text{me}}(43.5)=0.956\times 0.518=0.495\). That is, in the presence of ME, the probability that the maximum lateral displacement of the retaining wall at location QCX04 does not exceed the predefined limit decreases to 49.5%. This implies that ignoring the impact of ME could significantly overestimate the deformation reliability of retaining structures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Depth (m)** & **CD (mm)** & **Depth (m)** & **CD (mm)** & **Depth (m)** & **CD (mm)** & **Depth (m)** & **CD (mm)** \\ \hline
1.0 & \(-4.62\) & 12.0 & 37.48 & 23.0 & 58.03 & 34.0 & 4.97 \\
1.5 & \(-3.63\) & 12.5 & 41.22 & 23.5 & 54.29 & 34.5 & 4.46 \\
2.0 & \(-2.
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 2 Materials & Methods
### Dataset Preparation  
In this paper, we used the Chest X-ray Dataset for the detection of COVID-19 cases that was recently made public by [PERSON] et al. [21]. This dataset was made by a team of researchers from Qatar University, Doha, and the University of Dhaka, Bangladesh, along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors. The inclusion exclusion criteria is common for all COVID-19 dataset.  
The dataset is composed of chest X-ray images that merely present the anterior-posterior views because based on board-certified radiologist advice. Only anterior-posterior images are kept for COVID-19 prediction since the other type of images is not suitable for this purpose [33].  
It contains chest X-ray images for COVID-19 positive cases, along with Normal and Viral Pneumonia images. It consists of a total of 2992 chest radiography images with three classes: 306 COVID-19 positive images, 1341 normal images, and 1345 viral pneumonia images.  
To improve the Chest X-ray Dataset, we removed duplicate images found in the original dataset. We found that it contains 77 duplicate images. The new dataset contains 2911 images divided into 237 COVID-19 positive images, 1338 normal images, and 1336 viral pneumonia images. As observed, the COVID-19 cases are significantly lower than other classes, making it an imbalanced classification problem. Figure 1 presents some images of the chest X-ray dataset.  
For the evaluation of our deep learning model, we split our dataset into 2328 images for the training, 291 images for validation (dev), and 292 images for testing. For the training, we performed data augmentation on the dataset and fixed the input size of the image to 224 \(\times\) 224 \(\times\) 3.
# Progress in the Development of Electrodeposited Catalysts for Direct Liquid Fuel Cell Applications
## 5 Fuel Cell Polarisation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fuel Cell** & **No of** & **Fuel Concentration** & **Anode** & \begin{tabular}{c} **Catalyst Loading** \\ **mg cm\({}^{-2}\)** \\ \end{tabular} & **Cathode** & \begin{tabular}{c} **Catalyst Loading** \\ **mg cm\({}^{-2}\)** \\ \end{tabular} & **Cathode** & \begin{tabular}{c} **Catalyst Loading** \\ **mg m\({}^{-2}\)** \\ \end{tabular} & \begin{tabular}{c} **Total Active** \\ **Area, cm\({}^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **OCP**, \\ **V** \\ \end{tabular} & \begin{tabular}{c} **Power**, \\ **mW** \\ \end{tabular} & **Reference** \\ \hline AB-DMFC & 6 & 2 & PrRa/Ti & 4 & \begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 2 & 36 & 2.9 & 60 & [71] \\ \hline AB-DMFC & 1 & 2 & Pr/Ti & 4 & \begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 2 & 6 & 0.373 & 7.3 & [71] \\ \hline AB-DMFC & 1 & 2 & PrRa/Ti & 4 & \begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 2 & 6 & 0.496 & 16.1 & [71] \\ \hline AB-DMFC & 2 & 2 & PrRa/Ti & 4 & \begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 2 & 1 & 1 & 30 & [71] \\ \hline DFAPC & 1 & 3 & \begin{tabular}{c} Dendritic Pr/C \\ sequentially deposited \\ \end{tabular} & 2 & \begin{tabular}{c} Commercial \\ **4** \\ \end{tabular} & 2 & 4 & 0.571 & 168 & [98] \\ \hline DFAPC & 1 & 2 & Pr-Pd nanodel/C & 2 & \begin{tabular}{c} Commercial \\ **4** \\ \end{tabular} & 2 & 4 & 0.739 & 196 & [99] \\ \hline AB-DMFC & 6 & 5 & PrRa/C & 8 & \
\begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 8 & 24 & 3 & 500 & [122] \\ \hline AB-DMFC & 1 & 5 & PrRa/C & 8 &
\begin{tabular}{c} Commercial \\ **P/C** \\ \end{tabular} & 8 & 4 & 0.6 & 70 & [122] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the fuel cell tests conducted utilizing the electrodeposited catalysts with different liquid fuels: methanol and formic acid.
## 3 Smart Inverter Control Architecture
### DC-Link Voltage Control  
Since the insolation and the module temperature significantly affect the PVMA, the relationship between the output voltage and output current is nonlinear. Therefore, the characteristic curve also differs under different insolation and module temperature. For the photovoltaic system to reach maximum power output, the duty cycle for the power switch of the boost converter was controlled via MPPT, which kept the PVMA working at the MPP. Therefore, the output voltage of the boost converter was the unstable DC voltage, meaning the inverter input DC voltage was unstable. Therefore, to stabilize the inverter input DC voltage, the DC-link voltage at the inverter input must be controlled. Firstly, after acquiring the error between the DC-link \(V_{dcbus}\) voltage of the actual inverter input and the command voltage \(V_{dcbus}^{*}\) (400 V) that was set, the DC current command \(I_{dcbus}^{*}\) was then generated via the P-I controller. The control architecture of DC-link voltage is shown in Figure 6. When \(V_{dcbus}>V_{dcbus}^{*}\), in order to stabilize the DC-link voltage at the reference command value (400 V), the DC-link voltage controller would raise the current command and increase the real power of inverter output; furthermore, the DC-link voltage was reduced for stabilization at the reference command value of DC-link voltage. When \(V_{dcbus}<V_{dcbus}^{*}\), the DC-link voltage controller would reduce the current command value, so the DC-link voltage could raise to the voltage command value.  
From Figure 6, it is known that \(I_{dcbus}^{*}\) is expressed by the following formula:  
\[I_{dcbus}^{*}=K_{Pdc}(V_{dcbus}^{*}-V_{dcbus})+K_{Idc}\int{(V_{dcbus}^{*}-V_{ dcbus})dt} \tag{15}\]  
Among them, \(K_{Pdc}\) = 0.15 and \(K_{Idc}\) = 1 are the constants of the DC-link voltage P-I controller in this paper, respectively.
## 2 Space Segment  
From the modest radio transmitter onboard Sputnik 1 in the late 1950s to currently developed Very High Throughput Satellite (VHTS) systems, there has been a great deal of space technology developments and innovations, driven by new applications with communication satellites at the forefront of the commercial use of space. The turn of the century marked a major paradigm shift with increasing involvement and leadership from the private sector, often referred to as New Space, taking over a field previously driven by
## 1 Introduction
### Multi-Dimensionality of Spatial Attributes
Semantic data mining and clustering revealed 50 attributes which can be used to describe different aspects of spatial impression. Whilst the aforementioned research is valuable in recognising a wide range of perceptual descriptors for different concert halls, there is a lack of understanding in the perception of auditory attributes depending on the listener position and head orientation. Research conducted by [PERSON] et al. [19] highlighted the perceptual effects caused by the head-rotation-dependent variations in inter-aural cross correlation coefficient (IACC). In their study, two experiments were carried out testing how variations in IACC affect different perceptual attributes depending on different head movements of the listener's head. Synthesised IACC variations were tested in the first experiment and variations caused by different loudspeaker configurations were used during the second experiment. Results showed that the width and depth of the reverberant environment, as well as the perceived width and distance of the source, were strongly influenced by the variations in IACC. In a later study, [PERSON] [20] investigated into the perception of ASW and LEV at different source-listener distances in a reverberant concert hall. Perceived ASW was found to statistically decrease almost linearly as the distance from the source was doubled. Similarly, LEV was found to decrease upon doubling the distance, but with a lower magnitude. However, only three listening positions were tested, all on the central axis of the room and all facing towards the source.
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 1 Introduction
The main goal of this work is to pursue a joint theoretical-computational study on the dynamical evolution of localized pulses under the influence of gain/loss. Following our earlier work [21], we term a linear/cubic gain applied locally as a 'hot spot'. To obtain localized modes, energy dissipation should occur in the bulk of the waveguide through a linear loss. The objective is to trace the evolution of a rogue wave (or a localized pulse) in the presence of these external energy inputs/losses. On extending another work [22], two hot spots in a symmetric configuration might also be considered. Asymmetric patterns generally tend to be unstable. Theoretically, we consider the complex Ginzburg-Landau equation,  
\[iA_{t}+A_{xx}+\sigma|A|^{2}A=i\Big{[}\beta+\gamma(x,t)+\alpha(x,t)|A|^{2}\Big{]}A \tag{1}\]  
where \(\sigma\) and \(\beta\) are real constants representing the coefficient of cubic nonlinearity and the constant background linear gain (or loss, if \(\beta<0\)), respectively. The real functions \(\gamma(x,t)\) and \(\alpha(x,t)\) model the variable _linear gain_ and _cubic gain_ respectively. These gain functions may depend on space and time. Before proceeding further, a remark on the roles of the coordinates is in order. If the right hand side vanishes, Equation (1) will reduce to the conventional NLSE. In fluid mechanics, \(t\) and \(x\) will stand for slow time and group velocity coordinate, respectively [23; 24; 25; 26]. In optics, they represent propagation distance and retarded time for optical fibers [3] and may both be spatial variables in the case of diffraction [27]. In plasma physics and Bose-Einstein condensates, \(t\) and \(x\) also have a connection in terms of time and space [28; 29]. In this paper, we shall take \(t\) and \(x\) in the sense of temporal and spatial coordinates. Given these diverse fields of applications, the interpretation in different physical disciplines must be exercised with proper caution. The goal of this work is to examine computationally how various kinds of gain or loss may affect the generation and dynamics of rogue waves. The Peregrine breather  
\[u_{PB}=\rho\exp\Bigl{(}i\sigma\rho^{2}t\Bigr{)}\left[1-\frac{2\bigl{(}1+2\sigma \rho^{2}ti\bigr{)}}{\sigma\rho^{2}\Bigl{(}x^{2}+2\sigma\rho^{2}t^{2}+\frac{1}{2 \sigma\rho^{2}}\Bigr{)}}\right] \tag{2}\]  
of the nonlinear Schrodinger equation (NLSE)  
\[iu_{t}+u_{xx}+\sigma|u|^{2}u=0 \tag{3}\]  
is employed as the initial condition and the evolution in the dissipative medium described by Equation (1) is studied. More precisely, the waveform at a negative time instant (\(t<0\)), where the rogue wave is undergoing the growing phase, is taken as the initial condition. The structure of this paper can now be explained. The effect of background gain on the dynamics of rogue waves is discussed in Section 2. In Section 3, a Gaussian gain localized in space is considered. An extension to a gain localized in space and time, i.e., a 'rogue gain', is pursued in Section 4. Conclusions are drawn in Section 5.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 6 Evaluation and Discussion of the Results  
In this section, simulation and experiments are conducted to evaluate the performance of the optimization model as well as practical issues regarding time-slotted communications in LoRa networks. Due to the high complexity of the model, the LP solver was not able to solve solutions with more than 50 nodes. As a consequence, we divided the simulation results in two sections. Section 6.2 is dedicated to the comparison with the optimal with 10-50 nodes, and Section 6.3 is dedicated to scenarios with a higher number of nodes (100-1000).
## 3 Results and Discussion
### Fracture Self-Healing Rate  
The next step was to quantitatively analyze the relevant parameters. According to the method in Section 2.5.3, image software is used for threshold processing, the crack shape is extracted, and the crack area and healing rate are calculated. Table 5 lists the eight groups of specimens: Control-1, Control-2, N4.5, M27, N4.5-M27, N2.25-M13.5, N1.125-M6.75, and N3.375-M20.25. The curing periods for the crack repair area are 3 days, 7 days, and 28 days, respectively. As shown in Figure 6, on the 7 th day, the crack area healing rate of the Control-1 group was only 5%, the Control-2 group was 70.5%, the N4.5 group was 84%, and the other five groups were completely healed. After 28 days, except for the Control-1 group, the other seven groups were completely healed. This is because the self-healing agent was added to the other groups except the control group. When cracks appeared in the specimen, the self-healing agent was activated, and the microorganisms mineralized inside the specimen to produce calcium carbonate precipitates to fill the cracks. It can be observed from Figure 6 that the self-healing rate of cracks increases with the increase in silica content. This is because silica reacts with calcium hydroxide in cement mortar to form C-S-H gel and promotes the generation of more calcium carbonate to fill the cracks.
## 6 Description of the Developed Design
### Energy Conversion  
Even if liquid and gaseous hydrogen both rank best in the evaluation covered in Section 3, liquid hydrogen is considered more suitable for the following reasons not yet considered: First of all, the logistical effort of transporting gaseous hydrogen is much greater than that of liquid hydrogen.--In a calculation based on the developed supply chain models, it is found that four liquid hydrogen trucks transport the same amount of energy as 20 gaseous hydrogen trucks. Thus, a society choosing liquid hydrogen benefits, for example, from less traffic, fatigue of streets or other infrastructure, and noise pollution.--Furthermore, the manufacturing costs for a gas tank (not considered in the supply chain models) are expected to be higher than for a liquid tank due to the higher complexity of producing a carbon fibre tank compared to a steel tank.  
The implementation would happen with PEM electrolysers. The required number of PEM modules would be housed in commercially available containers. This enables modularity as well as easy manufacturing and maintenance. The wind turbine powers the electrolysers and the liquefaction plants. Assuming an electrolysis efficiency of 75% and a liquefaction efficiency of 70%, based on the trade study from Section 3, results in an electrolysis capacity of about 1.93 MW and a liquefaction capacity of 2.07 MW--all other power consumers are neglected at this early stage of the system design. Based on the data  
Figure 11: Concept sketch (not proportionally scaled). (**a**) View from behind. (**b**) View from the left.  
provided by a manufacturer of electrolyser containers [45], it is calculated that five 20-ft containers weighing 16 t (based on data for an alkaline electrolyser container due to missing information on the weight of a PEM container) each, for a total of 80 t, are necessary.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Influence of Excitation Parameters on Nonlinear Vibration Characteristics of Transmission System
#### 3.2.4 Influence of Tooth Backlash on Vibration  
In the process of producing gears, flaws such as base bitch and gear tooth profile are unavoidable. To decrease friction heat and improve transmission performance, a particular lubrication backlash must be left. Moreover, the presence of tooth backlash affects the transmission accuracy of the gear, resulting in impact and noise, which has a significant influence on the vibration characteristics of the gear pair.  
When the input speed \(n=1000\) r/min, the excitation \(T_{at}=10\) N \(\cdot\) m, the transmission error \(E=20\) um, the torque fluctuation is \(T_{f}=0\) N \(\cdot\) m, and the bearing backlash \(b_{b}=20\) um, the backlash's general value range is considered. Take the tooth backlash \(2\)\(b=0\), \(10\), \(100\) um to produce the time\(-\)domain curve comparison diagram of the external gear vibration, as well as the radial comparison Figure 8 and the axial comparison Figure 9.  
When the tooth backlash \(2\)\(b=0\), \(10\), \(100\) um, the radial vibration amplitude is \(29.208\) um, \(29.738\) um, and \(30.698\) um, respectively, and the axial vibration amplitude is \(6.706\) um, \(6.754\) um, and \(6.981\) um, respectively. When the backlash changes, the radial vibration curve of the gear changes under the coupling action of bearing backlash, but it is difficult to  
Figure 8: Time domain diagram of radial vibration of external gear under different tooth backlashes.  
Figure 9: Time domain diagram of axial vibration of external gear under different tooth backlashes.  
analyze. The vibration amplitude increases to a certain degree, the axial vibration trough rises little, and the peak value rises significantly. The global amplitude increases.
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy
### Performance Measures  
There are four main performance measures for routing in FANETs.  
* Higher QoS performance increases throughput and packet delivery rate (or reduces packet loss rate) as well as reduces end-to-end delay of delay-sensitive packets.
* Lower energy consumption increases route lifetime.
* Lower number of clusters increases the cluster size and, hence, the coverage of each cluster, which is a logical group of nodes comprised of the leader of a cluster (or cluster head, CH) and cluster members (CMs). Larger cluster size reduces intra-cluster communication (i.e., between CH and CMs from the same cluster) and inter-cluster communication (i.e., between CH and CMs from different clusters) [106; 107].
* Higher route setup success rate increases with the capability of setting up a route between a source node and a destination node.
# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 4 Experiments and Results
### Classification Results  
After the execution of our algorithm, each of the candidate regions was characterized by the feature vector that was described in Section 3.5. In order to determine if this candidate region is a logo or not, a bi-classification was added to the system. The set was divided into a training set, with 65% of the images, and a test set, containing the remaining 35%. For the training and test set images, the class of each sample (logo, non-logo) was manually annotated along with its feature vector.  
The training and classification were performed through the Bayesian classifier, the SVM bi-class classifier, and the C4.5 decision tree. For the analysis of the results in this section, only the C4.5 classifier has been taken into account because of its better results.  
The computed decision tree is depicted in Figure 9. The numbers placed on the tree branches represent the ranges of values for a particular feature, which are represented in circles in Figure 9. Therefore, we branched left or right depending on the attribute value computed for that region. Regarding the Euler number as a classification feature, setting the expected class to non-logo after testing that the Euler number of the sample was less than or equal to 26 yielded 77.57% correctly classified instances.  
Figure 8: Example of logotype detection in an image from the corpus with a high level of noise. (**a**) Original image. (**b**) Image with 5% salt and pepper noise. (**c**) Image with 15% salt and pepper noise.
###### Abstract  
One of the primary challenges in the railway industry revolves around achieving a comprehensive and insightful understanding of track conditions. The geometric parameters and stiffness of railway tracks play a crucial role in condition monitoring as well as maintenance work. Hence, this study investigated the relationship between vertical ballast stiffness and the track longitudinal level. Initially, the ballast stiffness and track longitudinal level data were acquired through a series of experimental measurements conducted on a reference test track along the Tehran-Mashhad railway line, utilizing recording cars for geometric track and stiffness recordings. Subsequently, the correlation between the track longitudinal level and ballast stiffness was surveyed using both frequency-based techniques and machine learning (ML) algorithms. The power spectrum density (PSD) as a frequency-based technique was employed, alongside ML algorithms, including linear regression, decision trees, and random forests, for correlation mining analyses. The results showed a robust and statistically significant relationship between the vertical ballast stiffness and longitudinal levels of railway tracks. Specifically, the PSD data exhibited a considerable correlation, especially within the 1-4 rad/m wave number range. Furthermore, the data analyses conducted using ML methods indicated that the values of the root mean square error (RMSE) were about 0.05, 0.07, and 0.06 for the linear regression, decision tree, and random forest algorithms, respectively, demonstrating the adequate accuracy of ML-based approaches.  
machine learning; railway track maintenance; longitudinal level; data mining; ballast stiffness +
Footnote †: journal: _Algorithms_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Correlation Analysis of Railway Track Alignment and Ballast Stiffness: Comparing Frequency-Based and Machine Learning Algorithms  
**Algorithms**