esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison Between Titanium and Thermally Activated Prostheses in Stages Surgery
## 2 Materials and Methods
### Statistical Analysis  
Nominal variables were summarised using counts and percentages, while continuous variables were described based on their distribution. For approximately normally distributed variables, results are presented as mean \(\pm\) standard deviation (SD); for skewed distributions, medians and interquartile ranges (IQR; Q1-Q3) were reported. The following variables were compared between the two prosthesis groups (MatriX vs. NiTiBOND): age, air conduction (AC), bone conduction (BC), air-bone gap (ABG), closure and overclosure of the ABG, as well as changes in these measures before and after surgery. Depending on the distribution, comparisons were conducted using either the independent samples \(t\)-test or the non-parametric Mann-Whitney U test. Categorical variables, specifically the distribution of sex and operated ear side across prosthesis groups, were compared using [PERSON]'s chi-squared test. A two-sided \(p\)-value < 0.05 was considered statistically significant.  
To evaluate the effect of prosthesis type on hearing outcomes over time, we fitted separate linear mixed-effects models for each of the following dependent variables: AC, BC, and ABG. Each model included fixed effects for time (preoperative vs. postoperative), prosthesis type, and their interaction (_time \(\times\) prosthesis type_) in order to test whether postoperative changes differed by prosthesis type. A random intercept for participant ID was included to account for repeated measures within individuals:  
\[\text{Outcome}\sim\text{time}\times\text{prosthesis type}+(1|\text{ID})\]  
Models were estimated using restricted maximum likelihood (REML), and degrees of freedom for fixed effects were approximated using the Satterthwaite method, as implemented in the ImerTest package [14]. Model assumptions, including normality and homoscedasticity of residuals, were verified through graphical diagnostics (Q-Q plots and residuals vs. fitted values).  
All analyses were performed in R (version 4.4.3; Foundation for Statistical Computing, Vienna, Austria) [15].
## 6 Simulations Results and Discussions
### Network Stability Time  
Stability time is the time taken by the network from the start till the first SN dies. Stability time indicates the time for which all the SNs are responding, i.e., all the SNs are alive. It depends upon the time period of each round and the number of rounds till the death of first SN. Stability time for all the benchmark and proposed protocol is given in Table 3. It can be seen from the table that EEUCH outperforms EEECA-THWSN, IEE-LEACH, TEEN, SEP, LEACH-C, and LEACH in terms of stability, with factors of 4.82, 4.82, 6.78, 7.32, 3.73, and 8.71, respectively. After round number 183, 21, 49, 25, 27, 38, and 38, the first SN dies in the case of using EEUCH, LEACH, LEACH-C, SEP, TEEN, IEE-LEACH, and EEECA-THWSN routing protocols, respectively, as shown in Figure 16. Furthermore, the time in seconds given in the table represents the time for which the WSN is sensing as well as transmitting the sensed data packets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Network Stability Time** & **Network Lifetime** \\ \hline LEACH [19] & 420 s & \(1.72\times 10^{3}\) s \\ \hline LEACH-C [20] & 980 s & \(17.8\times 10^{3}\) s \\ \hline SEP [21] & 500 s & \(2.26\times 10^{3}\) s \\ \hline TEEN [22] & 540 s & \(3.6\times 10^{3}\) s \\ \hline IEE-LEACH [23] & 760 s & \(2.42\times 10^{3}\) s \\ \hline EEECA-THWSN [24] & 760 s & \(4.58\times 10^{3}\) s \\ \hline EEUCH [Proposed] & \(3.66\times 10^{3}\) s & \(21.6\times 10^{3}\) s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stability and lifetime of the network based on different routing protocols.  
Figure 15: Number of packets collected versus percentage of node deaths.
# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 2 State-of-the-Art Review of Consumer Attitudes Towards Ultra-Processed Foods
### Assessing Ultra-Processed Foods  
Evaluating and comparing UPFs requires a structured approach that considers multiple dimensions of their impact on health, consumer behaviour, and the environment. Various assessment tools can be applied to analyse UPFs, broadly categorised into three main areas: nutritional metrics, composite indices, and theoretical models. These instruments provide valuable insights for industry stakeholders and policymakers in better understanding and regulating UPF consumption.  
Nutritional metrics: UPFs are often evaluated based on their composition, including levels of potentially harmful components, and artificial additives. Standardised scoring systems, such as the NOVA classification [21] help rank foods based on their degree of processing and nutritional quality. These tools enable consumers and health professionals to identify and compare the potential health risks associated with different UPFs. These metrics for food classification can also be applied to evaluate the health implications of UPFs, helping consumers and policymakers make informed dietary choices.  
Composite indices: Several compound indices integrate multiple factors to assess the overall health impact of UPFs such as Nutri-Score [24], Ofcom [25], and Health Star Rating (HSR) [26]. These indices often combine nutritional data with other key elements, such as glycaemic load, caloric density, and the presence of synthetic additives. In addition, consumer perception indices measure factors like taste, affordability, and brand trust, influencing UPF purchasing behaviour [27; 28]. Despite the growing use of these indices, there is currently no universal standard for assessing UPFs. While organisations like the World Health Organization (WHO) and the Food and Agriculture Organization (FAO) provide general dietary guidelines, there is no globally recognised framework for systematically evaluating the health impact of ultra-processed foods. Moreover, emerging research continues to refine these indices, aiming to provide more accurate and actionable insights into food quality and its implications for public health.  
Theoretical models: Conceptual models provide a broader perspective on UPF consumption by incorporating psychological, social, and economic factors. Frameworks such as the Health Belief Model (HBM) [29] and the Theory of Planned Behaviour (TPB) [30] help explain consumer attitudes and decision-making regarding UPFs. Additionally, MCDM techniques and ML models have been applied to analyse the complex interactions between health awareness, convenience, and purchasing preferences. These theoretical models provide structured approaches to understanding UPFs' nutritional and behavioural impact, offering valuable insights for developing public health policies and consumer education strategies.  
By integrating these different assessment tools, researchers and policymakers can develop a more comprehensive understanding of UPFs and their role in modern diets.
## 1 Introduction
### Contribution and Outline  
As a result of scientific studies and technological developments, different scenarios in which drones are included in the delivery processes have been put forward, and different route improvement studies have been carried out for the problems in different scenarios in this field. The truck multi-drone problem is one of these scenarios, and it is the problem focused on in this study. Within the scope of this study, first of all, information about the last mile concept and the use of truck-drones in the field of transportation has been presented, and a literature review has been made. Studies of delivery problems started with applications using only trucks or drones and continued with applications where these vehicles were used together. Apart from these, there have been many studies in the literature in which trucks and drones are used together, and their routes are independent of each other. However, since the routes were independent of each other in these studies, different warehouses were used for both vehicles and different solution calculations were required. In particular, the use of different solution calculations and different solution algorithms has created a disadvantage for these studies. In order to contribute to the literature in line with the research we have conducted, a hybrid truck multi-drone problem has been created in this study, and GA has been proposed as the solution algorithm. Then, the system theory created in this study and the proposed algorithm has been explained in detail. In the created scenarios, there is only one warehouse, one truck, and multiple drones. Trucks and drones leave the warehouse at the same time and come together again in the warehouse after completing the delivery. Also, different scenarios have been created, results have been obtained for these scenarios, and the results have been given in a comparative way. The success of the system and the proposed algorithm has been demonstrated by the results obtained and its ability to handle a real-world problem.
## 2 Methods and Data
### Experimental Design
#### 2.3.2 Experiment on the Landsat Dataset  
As the actual SWIR band image at 30 m resolution was available, the impact of the errors in input data on spatial downscaling was explored by intentionally generating the noise-contaminated coarse resolution SWIR images. More specifically, white noises from Gaussian distributions with zero means and three different standard deviations (5, 10, and 20) were added to the 150 m resolution SWIR band. Thus, four inputs, including one error-free SWIR band and three noise-contaminated SWIR bands, were used as input for the spatial downscaling experiment (Table 3). The original 30 m SWIR band was used as a test image for evaluating predictive performance. Per-pixel comparison of spatial downscaling results with the test image was employed to compute the accuracy.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 4 Solenoid Valve Failure-Mode Recognition
### Input and Output Design  
The SVM input is generally the eigenvector of the sample. In this paper, the research group utilized the signal's time-domain parameters and the information energy of the time-frequency curve as eigenvalues and constructed the eigenvectors on the basis of time-frequency and time-domain analyses. This article classifies the four states of a solenoid valve, namely normal spool, broken spring, stuck spool and slightly stuck. The different states of the solenoid valve are numbered as 0 for normal spool, 1 for spring break, 2 for spool stuck, and 3 for spool slightly stuck.
## 2 Results
### Effect of KHS-Cnd on Adhesion and Invasion of P. aeruginosa to Eukaryotic Cells  
KHS-Cnd action was also evaluated on the _P. aeruginosa_ ability to adhere and invade human cells. Since the bacterial strains studied in this paper were isolated from CF patients with chronic airways infection, adenocarcinoma human alveolar basal epithelial cells A549 were used. For this experiment, the strain 27P was chosen because its mature biofilm was clearly influenced by KHS-Cnd. First, bacterial resistance to gentamicin was evaluated. Obtained data showed that strain 27P was sensible to this antibiotic at a concentration of 300 ug/mL. Taking into account the high adhesion ability of _P. aeruginosa_ to eukaryotic cells, a multiplicity of infection (MOI) of 1:10 was used. The adhesion and invasion efficiency of the KHS-Cnd treated and untreated bacteria are reported in Table 2. Adhesion was defined by the number of bacteria adhering on A549 cells after 1 h of incubation. Invasion represents the number of internalized bacteria in A549 cells that survived after lysis with gentamicin (1 h incubation on cells plus an additional hour for gentamicin treatment). The adhesion efficiency of _P. aeruginosa_ clinical strain 27P corresponded to about 10% of the total used CFU (about 10\({}^{5}\) bacterial cells). Furthermore, our results showed that the adhesion  
Figure 9: Effect of KHS-Cnd on the A549 immortalized cell line. Dose–response plot of cells after 4, 6, and 24 h of incubation with increasing concentrations (5–40 uM) of peptide. Cell viability was assessed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay and expressed as described in the Materials and Methods section. Values are given as the means \(\pm\) SD (\(n=6\)); *** indicates \(p<0.001\), with respect to the control cells.  
efficiency of the _P. aeruginosa_ 27P strain was drastically affected by KHS-Cnd incubation (about one order of magnitude).  
Our data demonstrated that about 1% of total bacteria adhering to A549 cells were able to invade the host cells (about 10\({}^{3}\) bacteria). Interestingly, the invasion efficiency was also reduced after KHS-Cnd treatment (3.0 \(\times\) 10\({}^{3}\)\(\pm\) 0.2 \(\times\) 10\({}^{3}\) vs. 8.06 \(\times\) 10\({}^{3}\)\(\pm\) 0.08 \(\times\) 10\({}^{3}\)). Moreover, the results were also statistically significative (_p_ value = 0.00083).
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
###### Abstract  
Seismic resilience of critical infrastructure, such as liquefied natural gas (LNG) storage tanks, is essential to the safety and economic well-being of the general public. This paper studies the effect of different ground motions on large LNG storage tanks under four different site conditions. Key parameters of structural design and dynamic analysis, including von Mises stress of outer and inner tanks, tip displacement, and base shear, are analyzed to directly evaluate the safety performance of the large LNG tanks. Because the size of an LNG tank is too large to perform any experiments on a physical prototype, Smoothed Particle Hydrodynamics-Finite Element Method (SPH-FEM) simulation is used as a feasible and efficient method to predict its seismic response. First, the accuracy of the SPH-FEM method is verified by comparing sloshing frequencies obtained from theoretical formulation to experimental results and SPH-FEM models. Then, the seismic response of a real-life 160,000 m\({}^{3}\) LNG prestressed storage tank is evaluated with different liquid depths under four site classes. Simulation results show that the tip displacements of the LNG tank at liquid levels of 25% and 50% under site class IV are nearly identical to that of 75% and 100% under site class II. In addition, the maximum von Mises stress of the inner tanks exceeds 500 MPa in all four site classes and jeopardizes the structural integrity of large LNG tanks. As a result, optimization of structural design and the establishment of an early warning system are imperative to the safety of LNG tanks at high liquid levels.  
LNG tank; SPH-FEM; site class; seismic performance +
Footnote †: journal: Applied Sciences
# A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
## 5 Experimental Results and Discussion
### DL-Based Framework's Results and Discussion
#### 5.4.1 Binary Classification Scenario  
Starting of with the binary classification scenario, Table 1 provides a summary of the different metrics for the three DL models considered, as well as two other models from the literature, namely the K-nearest neighbors (KNN) and random forest models from [65]. Several observations can be made. The first is that all three DL models were effective in detecting malicious attacks, with the accuracy, precision, recall, and F1-score all being above 99%, which is comparable to previous models from the literature. More specifically, the dense autoencoder neural network outperformed classical models previously proposed in the literature, such as KNN and random forest [65], in terms of detection accuracy, while the convolutional neural network and the recurrent neural network models only lagged by less than 0.1%. This reiterated the capability of such models for detecting attacks in softwarized 5G networks. The second observation was the relatively high computational complexity associated with training such models, which was highlighted by the high training time that was in the region of hundreds of seconds. This is in contrast to some previous models from the literature, which typically have lower training times, as illustrated by the KNN and random forest models. It was also observed that the dense autoencoder neural network and the convolutional neural network models had fairly similar training times, with only around a 4% difference. In contrast, the recurrent neural network model had a longer training time, which was roughly 11% longer than the dense autoencoder neural network model and 16% longer than the convolutional neural network model. This matched the computational complexity analysis conducted previously, and this was expected. However, it is worth noting that the testing/prediction time was fairly low (in the range of 1.5 min). This highlighted the models' capability to quickly detect the presence of an attack, particularly given that many network attacks can last several hours [72]. Hence, detecting an attack  
Figure 13: Recurrent neural network architecture.  
within a few minutes represents a significant achievement. This again emphasizes the effectiveness of the proposed framework.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 3 Results
### Simulation Results  
According to the path of autonomous vehicles, we design an 800 s vehicle trajectory. As shown in Figure 4, the takeoff point is \(118.8^{\circ}\) N, \(31.95^{\circ}\) E, \(5\) m. The initial velocity is \(0\) m/s, and the initial heading direction is north.  
The update frequency settings are shown in Table 1. The error parameters of the navigation systems are shown in Table 2. The fault simulation scenarios settings are shown in Table 3.  
As Table 3 shows, faults are added to GNSS pseudo-ranges. A step fault with amplitude 1000 m and a ramp fault with a slope of 20 m/s are all typical failure types in GNSS pseudo-ranges.  
\begin{table}
\begin{tabular}{c c} \hline
**Sensors** & **Rates (Hz)** \\ \hline IMU & 50 \\ GNSS & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Sensor rates setting.  
Figure 4: The path of vehicles.  
\begin{table}
\begin{tabular}{c c c} \hline
**NO** & **Sources** & **Fault** & **Duration** \\ \hline \(1\) & GNSS & Add 1000 m step fault to SV-1 & 200–250 s \\
2 & GNSS & Add 20 m/s ramp fault to SV-1 & 200–250 s \\ \hline \end{tabular}
\end{table}
Table 3: Fault scenarios in the navigation system.
## 2 Methods
### Finite Element Method  
In the present work, an eight-noded \(C^{0}\) isoparametric element with 9 degrees of freedom per node \(\left\{U^{(1)},\,U^{(2)},\,U^{(3)},\,U^{(4)},\,V^{(1)},\,V^{(2)},\,V^{(3)},\,V^{ (4)},\,w^{0}\right\}\) is used to develop a finite element model. For any \(k\)th layer (\(k=1,\,2,\,3\)) of the HCS plates, the relationships between physical displacement vector \(\mathbf{u}^{(k)}\), linear in-plane and transverse shear strain vectors \(\mathbf{\varepsilon}_{l}^{(k)}\) and \(\mathbf{\gamma}_{l}^{(k)}\), vector \(\mathbf{g}^{(k)}\) that is related to nonlinear strain with element nodal displacement vector \(\mathbf{\delta}\) are given as  
\[\mathbf{u}^{(k)}=\mathbf{S}^{(k)}\mathbf{\delta}=\mathbf{H}_{D}^{(k)}\mathbf{N}_{ D}^{(k)}\mathbf{\delta} \tag{13}\]  
\[\mathbf{\varepsilon}_{l}^{(k)}=\mathbf{B}_{in}^{(k)}\mathbf{\delta}=\mathbf{H}_{in}^{( k)}\mathbf{N}_{in}^{(k)}\mathbf{\delta},\,\,\mathbf{\gamma}_{l}^{(k)}=\mathbf{B}_{tr}^{(k)} \mathbf{\delta}=\mathbf{H}_{tr}^{(k)}\mathbf{N}_{tr}^{(k)}\mathbf{\delta} \tag{14}\]  
\[\mathbf{g}^{(k)}=\mathbf{G}^{(k)}\mathbf{\delta}=\mathbf{H}_{\mathrm{G}}^{(k)} \mathbf{N}_{\mathrm{G}}^{(k)}\mathbf{\delta} \tag{15}\]  
where \(\mathbf{S}^{(k)}\) is the shape function matrix and \(\mathbf{B}_{in}^{(k)}\), \(\mathbf{B}_{tr}^{(k)}\), \(\mathbf{G}^{(k)}\) are the differential operator matrices of the \(k\)th layer. These matrices can be divided into matrices \(\mathbf{H}_{i}^{(k)}\) that are dependent on the thickness coordinate \(\zeta_{k}\) and matrices \(\mathbf{N}_{i}^{(k)}\) that are independent of \(\zeta_{k}\), where the subscript '\(i\)' donates \(D\), \(in\), \(tr\) or \(G\).
# Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning
## 3 Experiments
### Experimental Data
#### 3.1.2 Explanatory Data Analysis
By identifying and possibly removing highly correlated features, we can reduce multicollinearity and ensure that the remaining features contribute unique, meaningful information to the causal model. This step is essential for accurately attributing the root causes of late delivery risks in the dataset. However, while correlation matrices offer valuable insights into relationships between variables, they also have significant limitations. Correlation analysis highlights associations but does not provide information about directionality or causality. For instance, a strong correlation between shipment delays and certain shipping modes may suggest an association, but it does not clarify whether the shipping mode is causing the delay or if both are influenced by another underlying factor. This limitation underscores the need for our causal discovery approach, which can go beyond correlations to uncover the true directional relationships between variables, leading to more actionable insights for mitigating delivery risks.
## 2 Preliminaries
### Lattice
#### 2.4.1 Discrete Gaussians  
Let \(\sigma\) be any positive real number. Generating a Gaussian distribution \(\mathcal{D}_{\sigma}\) is defined by the probability distribution function (PDF) \(\rho_{\sigma}(\mathbf{x})=\exp\left(-\pi\|\mathbf{x}\|^{2}/\sigma^{2}\right)\). For any discrete set \(\mathcal{L}\in\mathbb{R}^{m}\), we define \(\rho_{\sigma}(\mathcal{L})=\sum_{\mathbf{x}\in\mathcal{L}}\rho_{\sigma}(\mathbf{x})\). The discrete Gaussian distribution \(\mathcal{D}_{\mathcal{L},\sigma}\) over the lattice \(\mathcal{L}\) with the parameter \(\sigma\) is defined by PDF \(\rho_{\mathcal{L},\sigma}(\mathbf{x})\):  
\[\rho_{\mathcal{L},\sigma}(\mathbf{x})=\rho_{\sigma}(\mathbf{x})/\rho_{\sigma}(\mathcal{ L}) \tag{4}\]  
**Lemma 3**.: _If the parameter \(\sigma\) of discrete Gaussian distribution is small, then any vector extracted from this distribution will possibly be short._  
**Lemma 4**.: _Let \(m\),\(n\),\(q\) denote as the positive integers that satisfy \(m>n\) and \(q>2\). We define a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) with parameter \(\sigma=\widetilde{\Omega}(n)\) and \(\mathcal{L}=\lambda_{q}^{\perp}(\mathbf{A})\). Then, there is a negligible function \(\mathbf{negl}(\cdot)\) such that:_  
\[\Pr_{\mathbf{x}\leftarrow\mathcal{D}_{\mathcal{L},\sigma}}\big{[}\|\mathbf{x}\|>\sqrt {m}\sigma\big{]}\leq\mathbf{negl}(n) \tag{5}\]  
_where \(\|\mathbf{x}\|\) is the \(\ell_{2}\) norm of \(\mathbf{x}\)._