esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation
### Experimental Setup
This approach follows standard industry practice for high-pressure valve testing, where inert gases are used instead of hydrogen to eliminate explosion risks while maintaining equivalent pressure loading characteristics for safety considerations. The test sequence continued until either leakage detection occurred or the maximum test pressure of 87.5 MPa was reached. Post-experimental data analysis involved examining recorded pressure data to identify the precise time point of leakage onset, then correlating this with corresponding video frames to confirm bubble formation timing. Five replicate tests were conducted for each valve design configuration under identical conditions. The experimental protocol maintained consistent environmental conditions, measurement procedures, and data analysis methods across all specimens to ensure statistical reliability of the comparative assessment.
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Classifiers with Embedded Feature Selection
#### 2.1.2 Logistic Regression (LR)  
Logistic Regression [42] is a widely used classifier that models the probability of a binary outcome based on one or more predictor variables. By applying L1 regularization, logistic regression can effectively perform feature selection by shrinking less informative coefficients to zero, therefore eliminating them from the model. The model prediction \(\hat{y}\) is given by a logistic function, with values in the range (0, 1) interpreted as the probability that \(x_{i}\) belongs to the positive class \(y_{i}=1\), while 1-probability represents the likelihood of belonging to the negative class. The negative class, in the case of LR, is usually labeled as 0.  
\[\hat{y}_{i}=\frac{1}{1+e^{-(\mathbf{w}^{T}\mathbf{x}_{i}+\theta)}} \tag{4}\]  
\[error_{i}=-log(\hat{y}_{i})\ if\ y_{i}=1\ else\ -log(1-\hat{y}_{i}) \tag{5}\]
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 3 Existing Equations Used for Two-Way Flat Slabs  
For the design of a two-way flat slab-column connection, the shear stress is usually assumed to be a function of strength of concrete and the geometric parameters of the slab and column. The critical section for checking punching shear in slabs is usually situated between 0.5 and 2 times the effective depth from the edge of the load or reaction. Many empirical equations have been published to estimate the punching shear strength of two-way slabs, such as the equations proposed in ACI 318-14, BS-8110-97, Model-Code-2010, and Euro-Code 2 [6; 7; 8; 9].
## 2 Results
### Mechanism of Antibacterial Activities Against Gram-Nnegative Bacteria  
We investigated the modes of action of the peptides by measuring their abilities to permeate model phospholipid membranes and _Escherichia coli_. The membrane-permeabilizing ability of the peptides was measured by monitoring the release of fluorescent calcein from liposomes of various compositions. We employed negatively-charged large unilamellar vesicles (LUVs) composed of 7:3 (_w/w_) egg yolk L-\(\alpha\)-phosphatidylylethanolamine (EYPE)/egg yolk L-\(\alpha\)-phosphatidylglycerol (EYPG) to mimic gram-negative bacterial cells. The percentage of calcein leakage 3 min after peptide exposure was used to assess its ability to permeate the membrane. Figure 5A shows the dose-response relationship of the peptide-induced calcein release. All Pro-peptides induced significant dye release against the negatively charged EYPE/EYPG LUVs, implying that they potently permeate bacterial cell component membranes. Especially, Pro10-1 and Pro10-1D peptides showed more than 80% leakage, even at 4 \(\upmu\)M, whereas Pro9-3 showed 64.7% leakage at 4 \(\upmu\)M. These results suggest that the potent antibacterial activities of these peptides may be due to permeabilization of the bacterial cell membrane. Therefore, the newly designed peptides, Pro10-1 and Pro10-1D with higher cationicity, which retained high  
Figure 4: Chemical shift perturbations in the NH region of the \({}^{1}\)H NMR spectra at 298 K in 9:1 (_v/v_) H\({}_{2}\)O/D\({}_{2}\)O induced by dodecylphosphocholine (DPC): Sets of 1D \({}^{1}\)H NMR spectra of a 1 mM solution of peptides were acquired with 128 scans. One-dimensional \({}^{1}\)H NMR spectra of Pro9-3 were recorded with 9 different concentrations of DPC from 10 to 11 ppm (**A**), and 1D \({}^{1}\)H NMR spectra of Pro9-3 (**B**) and Pro10-1 (**C**) from 7 to 11 ppm were recorded for six different concentrations of DPC. Changes in chemical shifts and line broadenings in the spectra of the peptides upon addition of DPC indicate conformational changes and interactions with DPC.  
antibacterial activity similar to that of melittin, effectively interact with the membrane to cause fatal damage to the bacterial cells, resulting in improved antibacterial activities.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 2 Related Work
### Threshold Selection Strategies  
Threshold selection is a critical factor influencing the performance of OOD detection.  
Traditional OOD detection methods often rely on fixed thresholds, which are ineffective in dynamically changing data environments. In real-world IoT scenarios, where data distributions are complex and time-varying, fixed thresholds can lead to poor detection performance, necessitating adaptive threshold selection strategies.  
Several adaptive threshold selection methods have been proposed. The Otsu method [17] determines the optimal threshold by maximizing inter-class variance, making it effective for bimodal distributions. However, in skewed distributions, which are common in IoT traffic, the Otsu method often struggles to find an appropriate threshold, leading to degraded detection performance. The standard deviation inflection point method identifies a threshold based on changes in the standard deviation trend [16]. While useful when the data distribution has clear change points, it is ineffective for smoothly distributed data, limiting its applicability in IoT environments with gradually evolving traffic patterns. An alternative method based on Extreme Value Theory (EVT) employs the Peak Over Threshold (POT) framework to model tail distributions using the Generalized Pareto Distribution (GPD) to determine an optimal threshold [18]. Despite its solid theoretical foundation for tail anomaly detection, the POT method encounters challenges related to parameter initialization, GPD fitting stability, and computational efficiency. The Empirical Cumulative Distribution Function (ECDF) method selects thresholds based on a preset significance level (\(\alpha\)), allowing partial adaptability [19]. Although existing adaptive thresholding methods have made some progress, they still face problems such as uneven data distribution and dynamic changes. Especially in IoT scenarios, the distribution of data is often time-varying and uncertain, so more flexible and robust threshold selection strategies are needed.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 2 Related Work
### Spectrum Using Behavior Detection  
The detection of the abnormal usage of a spectrum is the premise for spectrum management. For 6G spectrum distribution, spectrum usage behavior detection is the key component to build the trust value assessment mechanism and to further realize fair spectrum distribution. [PERSON] et al. propose an algorithm for detecting abnormal behaviors based on electromagnetic data mining. The method is of a good accuracy and real-time performance [17]. In the literature [18], blockchain technology and machine learning are applied to detect malicious users in the IoT network. The proposed method can store the data including the spectrum access moment, occupied frequency and transmitting power, and separate the normal users from malicious ones with machine learning. A multi-attribute-based fairness-driven algorithm is proposed for the determination and interruption of SUs' services to ensure fairness among services in the network's resource utilization in [19].
# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 3 Results and Discussion
### Finite Element Simulation Analysis of a Flexible Quick-Change Process System
#### 3.3.1 Flexible Quick-Change Process System Lifting and Changing Process Error Analysis  
The fixture plate experiences the combined effects of the tensile tension, workpiece load, etc., during the lifting and transposition processes of the flexible quick-change process system. The positioning pin put at the bottom will shift and produce mistakes because the center portion is not rigid enough and will bend and deform downward with a maximum deformation of 1.97 m. Additionally, the internal and external locating pins are arranged clockwise in accordance with the varied deformation directions of each locating pin, and the simulation outcomes are as follows in Figure 10.  
The impact of a little corner deviation is not taken into account because the zero point clamping device has the capability of automatic clamping and centering, and there is a guiding chamfer at the fit. Consequently, the position deviations of the four positioning pins of the outer ring and the three positioning pins of the inner ring are each separately examined. Due to the deformation of the fixture plate, the positioning pins positioned on the bottom outer and inner rings will flex, producing an offset in the X-, Y-, and Z-axes. The errors produced are displayed in Table 1.  
In the process of lifting and transposition, the fixture plate deforms downward, which will increase the inaccuracy produced by the outer ring positioning pins in the X- and Y-axes. Ulteriorly, the three locating pins in the inner ring at the bottom of the fixture plate are first fitted with the base of the zero positioning device, and then the four locating pins in the outer ring are fitted. As those inner ring positioning pins interact with the zero point clamping device for a longer period of time and are affected by gravity, a larger Z-axis error is produced with their use. Indicating that the planned foundation plate and the static mechanical properties of the lifting system meet the technical criteria, the deformation amount is within the design requirements, and the error brought on by the lifting transposition process can be disregarded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **X-Axis (\(\Delta\)X)** & **Y-Axis (\(\Delta\)Y)** & **Z-Axis (\(\Delta\)Z)** \\ \hline Outer ring positioning pin & \(3.8\times 10^{-3}\) mm & \(3.8\times 10^{-3}\) mm & \(1.52\times 10^{-5}\) mm \\ Inner ring positioning pin & \(3.2\times 10^{-3}\) mm & \(2\times 10^{-3}\) mm & \(1.96\times 10^{-5}\) mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positioning pin error during lifting transposition.  
Figure 10: Simulation of a flexible quick-change process system for lifting and changing positions. (**a**) Simulation results of lifting and changing processes. (**b**) Positioning pin position change.
# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 3 Generative Models
### Ae-Gan  
AE-GAN is a hybrid approach that combines an Autoencoder and a Generative Adversarial Network to effectively address the imbalance issue in credit card datasets. This combination leverages the strengths of both models to improve the quality and diversity of synthetic data, which is crucial for training robust fraud detection systems, as shown in Figure 5. The process is outlined as follows:  
1. Extract fraudulent samples from the training set.
2. Pass these samples through an Autoencoder (AE) to encode the data into a lower-dimensional space.
3. Apply Principal Component Analysis (PCA) to reduce the dimensionality of the encoded data to 15 features.
4. Train a Generative Adversarial Network (GAN) using the PCA-reduced data. The GAN consists of a generator and a discriminator.
5. Use the generator to produce synthetic features based on the reduced data.
6. Pass these generated features through the decoder of the Autoencoder to reconstruct the synthetic data.  
```
0:\(fraud\_samples\), Number of epochs epochs
0: Trained generator model
1:Define Generator Model:
2:Create a sequential model with:
3: Dense layer with 128 units, activation'relu', input_dim=15
4: Dropout with rate 0.5
5: BatchNormalization
6: Dense layer with 64 units, activation'relu'
7: Dropout with rate 0.5
8: BatchNormalization
9: Dense layer with 50 units, activation'relu'
10: Dense layer with 40 units, activation'relu'
11: Dense layer with 15 units
12:Define Discriminator Model:
13:Create a sequential model with:
14: Dense layer with 128 units, input_dim=15
15: Dropout with rate 0.5
16: LeakyReLU with alpha=0.2
17: Dense layer with 64 units
18: Dropout with rate 0.5
19: LeakyReLU with alpha=0.2
20: Dense layer with 32 units
21: Dropout with rate 0.5
22: Dense layer with 1 unit, activation'sigmoid'
23: Define loss function BinaryCrossentropy()
24: Define optimizer Adam(learning_rate=0.001)
25:for epoch = 1 to epochs do
26: Train Discriminator:
27: Compute loss on real data and fake data
28: Update discriminator weights
29: Train Generator:
30: Generate fake data
31: Compute loss based on discriminator output
32: Update generator weights
33:endfor
34:return Trained generator
```  
**Algorithm 3** GAN Training  
Algorithm 4 describes the main steps of the AE-GAN model.  
Figure 5: Autoencoder and GAN-based synthetic data generation.
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
## 4 Discussion  
Most of the control methods for liquid crystal optical phased array devices are based on host computer software control [21, 22], which is very costly and without portability for operation. To solve these drawbacks, we improved the control method with a fully embedded system based on a touch screen and its human-machine interaction interfaces. The fully embedded control method has many advantages, such as low cost, strong portability, low power consumption, and real-time performance. On the other hand, in some fields, such as satellite laser communication and lidar, multi-beam fan-out [6, 9] can simultaneously emit multiple laser beams and scan in different directions. The superposition function of fan-out and focusing can enlarge or reduce the divergence angle of the laser beam flexibly based on actual needs, which can enhance the APT (Acquisition, Tracking, Pointing) performances.
## 1 Introduction
Alternatively, algorithms that consider the seasonal response of vegetation can be used for near real-time detection of disturbance. This is the case of the Continuous Change Detection and Classification (CCDC), which can detect land cover changes continuously using all available Landsat data or every time a new image is collected [24]. When compared to other methods, CCDC has the advantage of working with multiple input variables for providing robust detection of land covers, especially when working with high quantities of missing data in the time series, as demonstrated in a simulation study by [PERSON] et al. [25]. CCDC uses ordinary least squares in a time series model that has components of seasonality, trend, and overall values. The CCDC algorithm has been successfully used in forest environments [26; 27]. As far as we know, there are no studies using CCDC over the savannas in Brazil. When VIs are considered for change detection in other environments, most studies generally focus on the Normalized Difference Vegetation Index (NDVI). However, in the _Cerrado_, it is particularly important to consider adding other VIs that capture the information of different savanna properties associated with canopy structure, biochemistry, and plant physiology [28]. There are also specific VIs proposed to be sensitive to disturbance by fire, such as the Normalized Burn Ratio (NBR) [29]. For detecting disturbance in the Brazilian savannas, the performance of each VI, or all VIs in a multivariate approach with CCDC (henceforth named \"ensemble VIs\"), has not been evaluated yet. In this study, we investigated the detection of disturbance over the savannas in Brazil using seven VIs calculated from a Landsat time series (2017-2019) and the CCDC algorithm. The specific objectives were to evaluate: (1) the effects of disturbance on savanna reflectance spectra and VIs time series; (2) the accuracy of CCDC-detection in 2019, considering single VIs, ensemble VIs, and the type of disturbance (savanna clearing and fire) in the analysis; and (3) the possible existence of seasonal patterns of occurrence of fire and clearing for a better understanding of the dynamics of disturbance in the savannas.
## 2 Materials and Methods
### Data Extraction  
Data extraction was performed independently for all included studies. General study characteristics were collected, such as researched cancer type, clinical application, study design, and dataset specifics. The AI models developed in the studies were analyzed to provide contextual knowledge of the intrinsic data structures the XAI methods they aimed to explain. The types of XAI methods applied in the included articles were identified to provide an overview of the explanatory techniques employed in the AI-driven analysis of ultrasound imaging data within an oncological context. The characteristics of each identified XAI method were cataloged, including their operational mechanisms, explanatory function, input requirements, outputs, and evaluation metrics.  
The operational mechanisms of methods were categorized using taxonomic criteria proposed in previous research [11; 17; 22; 32]; these comprise the following:  
* Model-specific vs. model-agnostic: Model-specific explanation methods are limited to application on particular AI model architectures, e.g., a specific convolutional network model (CNN) (see Figure 1). These methods leverage the underlying network's internal characteristics and use reverse engineering to generate their explanations. Model-agnostic explanation methods only operate on the model input and output; thus, they are independent of the model architecture. They aim to clarify the model's underlying \(f(x)\) function, for example, by approximating \(f(x)\) with another, simpler model that should be explainable. Other agnostic methods attribute weights to each model variable, depending on its influence on the \(f(x)\), to decompose the importance between variables [36].
* Global vs. local scope: Global-scope explanations provide general relationships learned by the model, by assessing common patterns in the overall dataset that drive the model's predictions (see Figure 2). Local-scope methods offer explanations of the model's specific prediction for a given input or single case.  
* Intrinsic vs. post hoc explanation: Intrinsic explanation models can construct general, internal relationships between input and output made during predictions, due to their simple structure (e.g., decision trees, linear regression model, or support vector machine); see Figure 3. Post hoc explanations are applied to analyze models after these have completed training, providing insight into the learned relationships. The important difference is that post hoc explanations train a neural network and attempt to explain the behavior of the black box network after that. In contrast, intrinsic explanations force the neural network to be explainable itself [32].
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 3 Data  
In this research, 1C-level-processed, cloudless Sentinel-2A images were used to perform the analysis and evaluation. The acquisition dates of the images were 29 June 2017, 3 November 2017, and 19 September 2018 for the Istanbul, Ankara, and Konya regions, respectively. The European Space Agency (ESA) developed the operational Sentinel-2 mission within the frame of the European  
Figure 1: Location map of the main study area and the test regions (Country map from ESRI®, California, USA, closer look from the natural colour composite of the Sentinel-2A).  
Union Copernicus programme. The Sentinel-2 mission is based on a constellation of two satellites, Sentinel-2A and Sentinel-2B, flying in the same orbit but phased at 180\({}^{\circ}\), to observe the land surface, thoroughly, with a short revisit period, and to meet the requirements of applications, such as land management, agriculture and forestry, and disaster control [47].  
Sentinel-2A was launched on 23 June 2015 and was followed by Sentinel-2B on 7 March 2017; both maintain a sun-synchronous orbit at 786 km altitude. The temporal resolution is five days from the two-satellite constellations, at the equator. The multispectral imager covers 13 spectral bands with a swath width of 290 km and a spatial resolution of 10 m (four visible and near-infrared bands (NIR)), 20 m (six red-edge and shortwave infrared bands (SWIR)), and 60 m (three atmospheric correction bands). Sentinel-2 image products are available to the community at the Level-1C (top-of-atmosphere reflectance in cartographic geometry) and Level-2A (bottom-of-atmosphere reflectance in cartographic geometry) processing levels. The granules or tiles of Sentinel-2A images for these two levels are provided as 100 km\(\times\)100 km sized ortho-images in the UTM/WGS84 projection, which can be downloaded from the ESA website, free of charge. Sentinel-2 data provide a viable complementary source to the pre-existing moderate resolution images, with an increased spectral resolution, and provide a continuity of SPOT and LANDSAT-type image data, by contributing to the current multispectral observations of LCU [48,49].
## 5 Thermochemical Conversion of Carbonaceous Feedstocks  
### Steam Reforming  
The methane steam reforming process is the most widely used industrial process for producing hydrogen [107]. It is a chemical process commonly carried out at temperatures above 850 \({}^{\circ}\)C in furnaces fired by fossil fuels.  
Methane steam reforming essentially consists of two gas-phase catalytic reactions between steam and methane:  
\[\mathrm{CH_{4}+H_{2}O\to CO+3H_{2}\,\Delta H\,(298\ K)=+206\ kJ/mol} \tag{2}\]  
\[\mathrm{CO+H_{2}O\to CO_{2}+H_{2}\,\Delta H\,(298\ K)=-41\ kJ/mol} \tag{3}\]  
Reaction (1) of steam reforming of de-sulfurized natural gas is strongly endothermic and is usually carried out at 800-1000 \({}^{\circ}\)C in furnaces fired with raw natural gas. In contrast, Reaction (2), which maximizes the hydrogen yield, and is known as the Water-Gas shift reaction, is exothermic and hence favored at lower temperatures (200-450 \({}^{\circ}\)C). The hydrogen produced is subsequently separated from the gas mixture by pressure swing adsorption, while CO\({}_{2}\) can be captured by adsorption in aqueous amino-alkali solutions. A process diagram of methane steam reforming is shown in Figure 6.