esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### High-Definition Mapping  
One of the cornerstones of autonomous driving is an accurate, actual, and seamless high definition map. The basic functionality is to determine the vehicle's position--which road and which lane it is in--but also information about traffic rules like speed limitations, or more dynamic conditions like road closures or construction areas. High-definition (HD) map users expect a continuous availability of the map content, even in cross-border scenarios. Autonomous cars, however, require the map to be constantly up-to-date, and thus when reality changes, the map needs to be updated. Regular map updates by the map provider, typically done a few times a year by driving mapping vans along the roads, are not at all sufficient. To ensure a high reliability of autonomous cars, the map needs to be updated constantly, by as many contributing cars as possible. Broadly speaking, the cars collect information about their surroundings using their on-board sensors, and then use their connectivity to send this sensor data to some backend in the cloud. Here, the received data is compared to the existing map, and if differences are found, the map can be updated. The data might even come from sources other than cars, e.g., road side cameras. The HD map can also be used as the base upon which more dynamic information can be stored, for example accidents. All these procedures have to work seamlessly across borders. For example, map updates from cars on one side of a border have to be distributed also to cars on the other side, served by a different operator with the backend running on a different MEC architecture.  
A schematic overview of the use case is shown in Figure 2: A first vehicle detects a lane that is closed because of construction work. It sends its sensor data to a backend in the cloud, where the new information is incorporated into the map. The updated map is then sent back to all connected vehicles in the vicinity.
# Magnetic Resonance Roadmap in Detecting and Staging Endometiosis: Usual and Unusual Localizations
## 3 Localization
### Superficial Peritoneal Lesions (SUIP)  
Superficial endometriosis is the most common form of endometriosis and is present in at least 8 out of 10 women diagnosed with endometriosis.  
The lesions are located superficially on the peritoneum and across pelvic organs like ovaries and uterine ligaments.  
Compared to deep endometriosis, women have less marked symptoms and medical therapy may be sufficient to control symptoms and ensure a good quality of life [37].  
Flat lesions are not easy to detect with MRIs, except if they present hemorrhagic components detected as hyperintense on fat-suppressed T1 WI or T1 WI. When even an MRI is not adequately sensitive to screen superficial endometriosis, diagnostic laparoscopy followed by histological confirmation remains the gold standard [38].
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 3 Predictive Models for Data Fusion  
### Attention Mechanism  
In this study, the periodic map spectrum feature maps obtained based on SCA-VMD are used for fault recognition, but the low resolution of the images and the small change in the value of the energy under different fault types are factors that lead to the poor effect of direct feature map recognition. For this reason, the classification model needs to judge the importance of the information in the image and its position in the image according to the characteristics of the images of different fault characteristics of the transformer to assign different weights to each part of the input feature map and to provide a more effective feature map of transformer fault types for the subsequent GoogLeNet method. In view of this, the above objectives were achieved through the proposed attention mechanism combining the channel attention mechanism and the spatial attention mechanism.  
The importance of each feature channel is calculated by reducing the loss function in terms of channel attention computation, and the features with higher correlation are weighted accordingly to improve the representation of the features [28]. This module performs global mean pooling and global maximum pooling on the feature map and derives the mean and maximum values. The resulting features are then fed into a weight-sharing MLP that generates features of the same dimensions as the input and is nonlinearly transformed using the ReLU activation function. Finally, the outputs of the two fully connected layers are summed and the results are normalised to a range between 0 and 1 using a sigmoid function. The resulting values are considered as the weights of each channel, the dot product is taken with the feature map and the weighted feature map based on the channel attention mechanism can be obtained through Equations (13)-(15).  
\[T_{channel}=MLP(AvgPool(F)+MaxPool(F)) \tag{13}\]  
\[W_{channel}(F)=Activation(T_{channel}) \tag{14}\]  
\[F_{e-weighted}=F\cdot W_{channel}(F) \tag{15}\]  
F is the input feature; AvgPool(F) and MaxPool(F) denote average and maximum pooling, respectively; and Activation is the sigmoid activation function.  
The spatial attention mechanism allows for weighting between the height and width dimensions of the feature map to improve the representation of features [29]. In this module, the input feature map is first compressed in the spatial direction to obtain the mean and maximum values for each spatial location. Then, the two values are spliced and fed into a convolutional layer to obtain the weights for each location. Finally, a dot product is taken between the resulting weights and the input feature maps, and the spatial-attention-mechanism-based weighted feature maps can be obtained using Equations (16)-(18).  
\[T_{spatial}=Conv(concat(AvgPool(F),MaxPool(F)) \tag{16}\]  
\[W_{spatial}(F)=Activation\Big{(}T_{spatial}\Big{)} \tag{17}\]  
\[F_{s-weighted}=F\cdot W_{spatial}(F) \tag{18}\]
## 2 Theoretical Background
### Optimization Algorithms
#### 2.2.5 Similarities vs. Differences in Optimization Algorithms  
There are many similarities between all the mentioned optimization algorithms. PSO-style algorithms exhibit social behavior by facilitating information sharing among candidates, allowing each solution to learn from its neighbors as they converge. However, many of these algorithms do not incorporate personal bests, thereby neglecting the individual search experience of each candidate [61]. Furthermore, the movement of solutions in all these algorithms is stochastic, and the initial positions are generated randomly. Although the methods for updating positions vary across these algorithms, the underlying rules for position adjustment are fundamentally similar. Many optimization algorithms share common parameters and variables, as illustrated in Figure 1. For example, all PSO-style algorithms utilize the position variable and the global best position. Specifically, the optimizers we employed have candidate positions (denoted by \(\mathbf{x}_{i}\)) and a global best position (\(\mathbf{G}\)). A parameter that transitions between exploration and exploitation phases (e.g., \(a=W=r_{1}\)) is also present.
## 2 Materials and Methods
### Method of Data Analysis
#### 2.3.2 Analysis of Variables Affecting Adaptation to Climate Change
The likelihood that all four components of the present adaptation techniques to climate change and variability were chosen by local pastoralists 'i' is provided as:  
The probabilities of selecting the adaptation of seasonal livestock migration:  
\[\text{Pr}(\text{y}_{\rm{1i}}=1\text{,}y_{\rm{2i}}=1\text{,}y_{\rm{3i}}=1\text {,}y_{\rm{4i}}=1\text{,})=\text{XPr}(\mu_{\rm{1i}}\leq\beta_{\rm{1}}x_{\rm{1i }}\text{,}\mu_{\rm{2i}}\leq\beta_{\rm{2}}x_{\rm{2i}}\text{,}\mu_{\rm{3i}} \leq\beta_{\rm{3i}}x_{\rm{3i}}\text{,}\mu_{\rm{4i}}\leq\beta_{\rm{4}}x_{\rm{4i }}) \tag{16}\]  
The probabilities of selecting the adaptation of livelihood diversification:  
\[\text{Pr}(\text{y}_{\rm{1i}}=1\text{,}y_{\rm{2i}}=1\text{,}y_{\rm{3i}}=1\text {,}y_{\rm{4i}}=1\text{,})=\text{XPr}(\mu_{\rm{2i}}\leq\beta_{\rm{2}}x_{\rm{2i }}\text{,}\mu_{\rm{3i}}\leq\beta_{\rm{3}}x_{\rm{3i}}\text{,}\mu_{\rm{4i}}\leq \beta_{\rm{4}}x_{\rm{4i}}\text{,}\mu_{\rm{1i}}\leq\beta_{\rm{1}}x_{\rm{1i}}) \tag{17}\]  
The probabilities of selecting the adaptation of PSNP: \begin{table}
\begin{tabular}{c c c c} \hline
**SN** & **Strategies** & **Categories** & **Reasoning** \\ \hline \multirow{2}{*}{1} & Seasonal Livestock Migration & Adaptation strategies & Traditional, long-term practice to manage climatic variability sustainably. \\ \hline \multirow{2}{*}{2} & Productive Safety Net & Coping & Short-term relief to address immediate climate shocks (e.g., climate variability such as drought). \\  & Programs (PSNPs) & strategies & \\ \hline \multirow{2}{*}{3} & Livelihood Diversification & Adaptation strategies & Long-term approach to reduce climate risks by diversifying income sources. \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Seedling Planting} & Adaptation strategies & Forward-looking environmental strategy for resilience and land restoration. \\ \cline{3-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Adaptation and coping strategies of study area. \[\Pr(\mathbf{y}_{1_{i}}=1,y_{2_{i}}=1,\mathbf{y}_{3_{i}}=1,y_{4_{i}}=1,)=\mathrm{XPr }(\mu_{3_{i}}\leq\beta_{3}x_{3_{i}},\mu 1_{i}\leq\beta_{1}x_{1_{i}},\mu_{2_{i}}\leq\beta_{2}x_{2_{i}}, \mu 4_{i}\leq\beta_{4}x_{4_{i}}) \tag{18}\]  
The probabilities of selecting adaptation of seedling planting:  
\[\Pr(\mathbf{y}_{1_{i}}=1,y_{2_{i}}=1,\mathbf{y}_{3_{i}}=1,y_{4_{i}}=1,)= \mathrm{XPr}(\mu_{4_{i}}\leq\beta_{4}x_{4_{i}},\mu_{1_{i}}\leq\beta_{1}x_{1_{i }},\mu_{2_{i}}\leq\beta_{2}x_{2_{i}},\mu_{3_{i}}\leq\beta_{3}x_{3_{i}}) \tag{19}\]  
They jointly estimate this system of equations using the maximum likelihood approach. [PERSON] [50] used the Gewek-Hajivassiliour-Keane smooth recursive conditioning simulator to estimate the multivariate normal distribution. The GHK simulator, as demonstrated by [51], possesses favorable characteristics when applied to multivariate, normal, and limited dependent variables. Specifically, the simulated probabilities are unbiased and confined within the (0, 1) range, and the simulator is a continuous and differentiable function of the model's parameters.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Rate of Failure for DRFTSA Using Regular Dataset Using Fixed Number of VMs  
In this section, we precisely present the generated rate of failure for DRFTSA for a regular dataset using a fixed number of VMs. The reason to measure the rate of failure is that it is one of the parameters which is related to trust in the cloud provider and it is also used to identify the fault tolerance of the cloud provider as it impacts the quality of service. Therefore, we chose the rate of failure as one of the parameters and it is calculated by considering a regular dataset using a fixed number of VMs in this scenario. We considered 100, 200, 300, 400, 500, and 600 tasks. For all these tasks, VMs are considered as fixed. Generated rate of failure for PSO with 100, 200, 300, 400, 500, 600 tasks is 49.23, 53.57, 55.23, 57.17, 60.32, 63.98, respectively. Generated rate of failure for GA with 100, 200, 300, 400, 500, 600 tasks is 52.38, 56.18, 58.41, 62.59, 64.36, 67.16, respectively. Generated rate of failure for ACO with 100, 200, 300, 400, 500, 600 tasks is 58.31, 54.18, 49.53, 50.37, 63.18, 65.86, respectively. Generated rate of failure for DRFTSA with 100, 200, 300, 400, 500, 600 tasks is 32.18, 37.58, 34.21, 35.98, 33.27, 36.86, respectively.  
From Table 12 and Figure 12 it is evident that rate of failures of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.
## 4 Experiment Setup and Results  
This section starts with information about the experiment's setup and technical specifications. Next, a discussion of the experimental results, which starts by presenting the final configuration of the drone and the experiment location, is presented. This is followed by introducing the Hata-Davidson empirical propagation model with its tuned results, highlighting the results of the drone's search-and-rescue missions using its components. When reaching the final setup and configuration of the drone with its components, all the components were calibrated and tested for both ground segments and space segments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviations** & **Description** & **Abbreviations** & **Description** \\ \hline \(\mathrm{P_{1}HD}\) & Path loss of Hata-Davidson & \(\mathrm{G(h_{t})}\) & Transmitter antenna height gain \\ \(\mathrm{P_{L}Hata}\) & Path loss of Hata & \(\mathrm{G(h_{t})}\) & Receiver antenna height gain \\ \(\mathrm{A}\ (h_{r},\mathrm{d})\) & Correction factors for extend distance and & L & Connector and cable loss \\  & & transmitter antenna altitude & \\ \(\mathrm{S_{1}(d)}\) & Correction factors for extend distance & SNIR & Signal-to-interference-ratio \\ \(\mathrm{S_{2}(h_{t},d)}\) & Correction factor for transmitter antenna altitude & N & Noise figure \\ \(\mathrm{S_{3}(f)}\), \(\mathrm{S_{4}(f,d)}\) & Correction factors that extend frequency & I & Interference \\ \(\mathrm{\theta}\) & Elevation angle & T & Throughput \\ \(\mathrm{d}\) & Distance of the propagation model’s range & B & Bandwidth \\ \(\mathrm{f}\) & Carrier Frequency & a & Length of GPS area \\ \(\mathrm{E_{r}}\) & Earth radius & FOV (x) & Horizontal field of view of the camera \\ \(\mathrm{h_{r}}\) & Transmitter antenna altitude & B & Width of GPS area \\ \(\mathrm{h_{r}}\) & Receiver antenna altitude & FOV (y) & Vertical field of view of the camera \\ \(\mathrm{a(h_{t})}\) & Correction Factor for mobile antenna height & Scale (x) & Linear relationship between x pixels and distance \\ \(\mathrm{E}\) & Error function & Scale (y) & Linear relationship between y pixels and distance \\ \(\mathrm{Y_{i}}\) & Measured data & \(\mathrm{c}\) & Rotation matrix \\ \(\mathrm{R}\) & \(\mathrm{w}\) & Angle of drone \\ \(\mathrm{PLHD_{i}}\) & Predicted data & \(\psi\) & Angle of drone \\ \(\mathrm{K_{1}}\) & Hata-Davidson model coefficient 1 & P & Position offset in the world frame \\ \(\mathrm{K_{2}}\) & Hata-Davidson model coefficient 2 & \(\mathrm{Px}\) & Position latitude \\ \(\mathrm{RSS}\) & Received Signal Strength & Py & Position longitude \\ \(\mathrm{P_{t}}\) & Transmitter power & fx & GPS cam longitude \\ \(\mathrm{RMSE}\) & Root Mean Square Error & fy & GPS cam latitude \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.  
Mission Planner, MATLAB, ThingSpeak, Blynk, and TCP/IP package were used as the software enablers for the communication, GPS coordination, and live tracking of the drone.
## 6 Results and Discussion  
Three probability distributions, namely 2-parameter Weibull, 3-parameter Weibull, and exponentiated Weibull, were applied to fit the wind speed data from the meteorological station Poprad, located at the Poprad-Tatry airport. Their performance was compared in terms of how well these distributions matched the observed wind speed data. Applying the maximum likelihood method, we obtained the parameter estimates for all discussed probability distributions. The evaluation of each model was done by the GOF tests and the model selection criteria described in Section 5.3. The estimated parameters as well as test statistics of the _KS_ and the _AD_ tests, respectively, and of the other assessing criteria are summarised below in Tables 5-8--for the estimated parameters of the probability distributions see Table 5 (the entire period and the seasons) and Table 7 (respective months); for the GOF tests and model selection criteria see Table 6 (the entire period and the seasons), and Table 8 (respective months). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Distribution**} & **Estimated** & **Entire** & \multirow{2}{*}{**Spring**} & **Summer** & **Autumn** & **Winter** \\  & **Parameters** & **Period** & & & & \\ \hline \multirow{2}{*}{W2} & \(a\) & 1.3893 & 1.5146 & 1.5680 & 1.3650 & 1.2059 \\  & \(b\) & 3.5133 & 3.9596 & 3.3696 & 3.2592 & 3.4510 \\ \hline \multirow{3}{*}{W3} & \(a\) & 1.3263 & 1.4591 & 1.5046 & 1.2978 & 1.1305 \\  & \(b\) & 3.3802 & 3.8383 & 3.2479 & 3.1216 & 3.2817 \\  & \(c\) & 0.0894 & 0.0875 & 0.0914 & 0.0919 & 0.0945 \\ \hline \multirow{3}{*}{EW} & \(a\) & 0.8726 & 1.0340 & 0.8823 & 0.7791 & 0.7642 \\  & \(b\) & 1.6847 & 2.3591 & 1.3894 & 1.2294 & 1.5294 \\ \cline{1-1}  & \(7\) & 2.5451 & 2.1045 & 3.3732 & 3.2179 & 2.4516 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum likelihood estimates of parameters of the modelled distributions—the entire considered period and the seasons. Figures 5-7 illustrate how appropriately the considered probability distributions describe the wind speed data. From the obtained results and their visualizations, we can conclude the following:  
* Respective months:  
For eight out of the twelve months (April-November), the EW distribution achieved the best results in terms of all GOF tests and the model selection criteria. In February and March, W3 performed better, but the EW was the second best. In January and December, the EW and the W3 distribution ranked the same, but the EW performed better in terms of the \(KS\) and the \(AD\) test, as well as in terms of \(R^{2}\), and \(RMSE\). In this case, when some GOF tests and model selection criteria favoured the EW distribution, whereas the others
## 2 Materials and Methods
### Antimicrobial Activity Determination  
The effectiveness of the antimicrobial activity was assessed using the disc diffusion method. The experiment involved testing eight different strains of microorganisms, which included one yeast (_Candida albicans_ CCM 8186), five Gram-negative bacteria (_Haemophilus influenzae_ CCM 4454, _Escherichia coli_ CCM 3954, _Klebsiella pneumoniae_ CCM 2318, _Yersinia enterocolitica_ CCM 5671, _Salmonella enterica_ subs. _enterica_ CCM 3807), and four Gram-positive bacteria (_Staphylococcus aureus_ CCM 2461, _Clostridium perfringens_ CCM 4991, _Bacillus cereus_ CCM 2010, _Streptococcus pneumoniae_ CCM 4501). All strains were obtained from the Czech Collection of microorganisms. Prior to testing, the bacterial and yeast suspensions were cultured in nutrient broth (Imuna, Bratislava, Slovakia) at 37 \({}^{\circ}\)C for 24 h. A suspension of 0.1 mL of the tested microorganism with a density of 10\({}^{5}\) cfu/mL was spread on Mueller Hinton Agar (MHA, Oxoid, Basingstoke, UK). Filter paper discs (diam = 6 mm) impregnated with the oil (15 \(\mu\)L) were placed on the agar. Agras were left at 4 \({}^{\circ}\)C for 2 h and then after incubating at 37 \({}^{\circ}\)C aerobically for 24 h, the inhibition zones were measured in millimetres. The analysis was conducted in triplicate.
# The Effect of Inflatable Pressure on the Strain Deformation of Flexible Wing Skin Film
## 4 Results and Discussion
### Analysis of the Strain Deformation of Flexible Wing Film
The off-axis tensile deformation test of flexible inflatable wing skin film is shown in Figure 10. It can be seen from the test results that the tensile deformation and fracture are closely related to the deformation and failure of the internal components of the flexible film. The tensile deformation and fracture failure in the direction of 0\({}^{\circ}\) and 90\({}^{\circ}\) off axis is mainly controlled by the fiber yarn bundle. The ultimate strain of the fiber yarn bundle directly determines the deformation failure and bearing limit of the flexible inflatable wing skin film, while the matrix deformation has little effect on the deformation failure of the flexible inflatable wing skin film. Therefore, for the tensile deformation in this direction, the tensile deformation of the flexible inflatable wing skin mainly depends on the ultimate deformation value of the fiber yarn bundle. With the increase in tensile deformation, the tensile deformation of the matrix increases significantly, and the matrix cracks first. When the crack extends to a certain range, the crack causes the local fracture of the fiber bundle, resulting in the overall tensile deformation and fracture failure of the flexible inflatable membrane skin. Therefore, the tensile deformation and fracture failure in this direction mainly depends on the matrix with large elongation, and the ultimate strain of the matrix directly determines the deformation failure and bearing limit of the flexible inflatable wing skin film, because the matrix cracking leads to the air leakage failure of the flexible inflatable wing skin film, while the fiber deformation has little effect on the deformation failure of the flexible inflatable wing skin film, so for the tensile deformation  
Figure 5:0 Experimental study on the axial tensile deformation of the membrane skin of the flexible inflatable wing. in this direction, the tensile deformation of the flexible inflatable wing skin mainly depends on the ultimate deformation value of the matrix. Thus, the judgment criterion for the tensile deformation failure of the flexible inflatable wing skin film proposed in this paper is verified. In the design of the flexible inflatable wing film, according to the different load-bearing deformation conditions, the smaller value of the allowable strain of fiber bundle [\(\varepsilon_{f}\)] and the allowable strain of matrix [\(\varepsilon_{m}\)] should be selected for the design strain.
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Stress Responses and Adaptive Resistance  
The capacity of bacteria to endure extreme conditions and resist antimicrobial agents is intricately linked to the stress responses and adaptive resistance strategies demonstrated within biofilms. Forming biofilms exemplifies a complex adaptive mechanism in response to various environmental stressors, such as physical pressures, chemical agents like hypochlorous acid, and antibiotic exposure [126; 137; 138; 139]. The ability to adapt is crucial for biofilms' resilience, allowing them to survive in harsh conditions that would usually hinder or eradicate planktonic bacterial cells.  
Biofilms utilize various strategies to adjust to stress and improve their resilience. These encompass structural modifications, physiological adaptations, and the emergence of genetic resistance that enhance their survival in challenging environments. For example, biofilm-associated bacteria can alter their EPS matrix, modifying its viscoelastic characteristics to reduce the impact of mechanical and chemical stresses [140; 141; 142]. Furthermore,biofilms have the capability to trigger particular stress responses, including oxidative stress pathways and the SOS response, upon exposure to antibiotics. The pathways induced by stress lead to genetic alterations and repair processes that strengthen resistance, thereby improving the survival of biofilm communities [126].  
The occurrence of stress-inducing agents, including triclosan and aminoglycosoides, can initiate the emergence of adaptive resistance within biofilms. This resistance encompasses physiological alterations that enable bacteria to endure concentrations of antibiotics that would typically be lethal, underscoring the adaptive strategies for survival within biofilms [143]. Additionally, biofilms have demonstrated the ability to facilitate the development of resistance by encouraging genetic alterations, such as mutations and horizontal gene transfer, in response to stress conditions. These adaptations allow biofilm-associated bacteria to circumvent therapeutic interventions and establish enduring infections [144].