esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application
### Comparative Analysis of Monitoring Data
#### 4.3.3 Discussion on the Simplified Calculation Methods  
Currently, the calculation method for the CSP initial support structure in highway tunnels is based on Equations (8) and (10). This approach assumes a rectangular cross-section and does not account for the effects of the CSF). Consequently, it overlooks stiffness reduction and joint weaknesses, leading to underestimated deformations and overestimated structural safety. This shortfall results in insufficient design recommendations. In contrast, the simplified calculation method proposed in this paper, based on Equation (11), provides a more accurate computation for the initial support of corrugated steel in highway tunnels and aligns closely with monitoring data.  
Despite improved calculation results, the proposed method still relies on two assumptions that do not fully reflect actual structural conditions, requiring future research to address them. (1) Axial force impact: The method does not account for the impact of axial force on joint stiffness. Axial force significantly influences the stiffness and load-bearing capacity of the CSFJ [10]. Future research should develop a joint model that incorporates axial force and its numerical calculation method. It is important to note that axial force enhances the bending performance of flange joints. Therefore, deformation calculations without considering axial force will be greater than the actual situation, serving as a safety margin without affecting design calculations. (2) Two-dimensional model limitations: The two-dimensional model fails to capture three-dimensional effects. Corrugated steel support structures are assembled using staggered seams, similar to shield tunnel segments [29]. The two-dimensional model reflects continuous seam assembly but does not account for the strengthening effect of staggered seams. Future research could propose a simplified three-dimensional calculation method. Staggered seam assembly enhances the structure, so two-dimensional calculation results will be greater than the actual situation, providing a safety margin that does not impact design safety.  
Additionally, high-temperature fires in steel structures require significant attention. Heat conduction, thermal shock, and thermal waves during tunnel fires present thermal coupling problems [30; 31] for CSP initial support. Therefore, new calculation methods for CSP initial support under high-temperature conditions need to be developed.
## 2 System Components  
The overall block diagram of the FPGA-based dynamic star simulator star map display algorithm is shown in Figure 1. It is mainly composed of the following four parts: optical axis calculation, navigation star search, coordinate transformation, and star map display. LCOS (Liquid Crystal on Silicon) in Figure 1 is a commonly used display device for dynamic star simulators.  
Figure 1: Overall block diagram of the system.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 3 Threat Model
### UPF DoS  
The UPF is responsible primarily for transmitting user data, and if it is overloaded, service disruptions occur [64]. Specifically, the UPF drone is particularly vulnerable to serious threats from malicious ground traffic at the intersection between low-altitude airspace and ground segments, as shown in Figure 3.  
Here, we assume that attackers deliver malicious UE to unprotected remote areas covered by the 6G NTNs. The malicious UE can generate a large volume of protocol traffic, which is transmitted wirelessly to the low-altitude airspace of BSs and then to the UPF via the N3 interface connected to the low-altitude airspace of BSs. This surge in protocol traffic can overwhel the UPF, leading to a DoS attack. The steps involved in the UPF DoS attack are as follows:  
* The attacker UE is launched, with each piece of UE connected to the target 6G NTN.
* The malicious tool installed in the attacker UE generates a substantial volume of messages following the protocols of transmission control protocols (TCPs), user datagram protocols (UDPs), and internet control message protocols (ICMPs) within a short time. These requests are specifically configured with a high sending rate and large payload size.
* The UPF receives an overwhelming number of TCP, UDP, and ICMP requests from the attacker terminals.  
After these steps are completed in the UPF DoS case, a significant burden is imposed on the computing resources of UPFs, rendering them incapable of effectively handling legitimate UE services. The malicious traffic in the second step can be generated using tools such as Bonesi ([[https://github.com/[PERSON]/bonesi.git](https://github.com/[PERSON]/bonesi.git)]([https://github.com/[PERSON]/bonesi.git](https://github.com/Markus-Go/bonesi.git)), accessed on 2 December 2018) and Hulk ([[https://github.com/grafov/hulk.git](https://github.com/grafov/hulk.git)]([https://github.com/grafov/hulk.git](https://github.com/grafov/hulk.git)), accessed on 17 May 2012). In this article, the tool of Bonesi is used to generate misconfigured traffic.
## 2 Related Work
### Low-Light Image Enhancement Algorithm Based on Image Fusion  
In recent years, many Multiple Exposure Fusion (MEF) algorithms have been proposed, and the main idea of the algorithm is to calculate the weight of each image in the multisource image sequence by the algorithm, and the fused image is the weighted sum of each image. Various MEF methods use different techniques to find the best weight map. The process in [24] is very simple, metric scoring each pixel point based on three metrics: local contrast, color saturation, and exposure quality. The normalized weight map is obtained by normalizing the metric scores, and the weight map guides the multiscale image fusion. However, since the metric occurs at the pixel point, the brightness of the fused image is susceptible to color mismatch and edge artifacts. In [25], to make the fused image as close as possible to a single image of the original scene, the algorithm uses the intensity of the image details extracted by the bilateral filter as weights to guide the fusion. The algorithm fuses well for images with large exposure differences.  
In this study, the process of multiple exposure image generation is avoided, reducing the consumption of computer resources and time. We use an exposure image generator to generate exposure-corrected images corresponding to a single low-light image. To achieve a more comprehensive image quality enhancement, the details of the two images are fused together by a cross-fusion attention mechanism to generate the final enhanced image.
## 4 Experiments and Results
### Simulation Environment and Parameters  
We use the OMNeT++ (version 6.1.0) network simulator to simulate the dynamic Mobile Ad Hoc Networks (MANETs) in conjunction with Python libraries. Nodes follow the Random Waypoint Mobility model with continuous movement (and a pause time of 30 s). OMNeT++ facilitated the network simulation, while Python 3.10.0, was utilized to implement the reinforcement learning algorithms and data analysis. Each data point reported averages over 30 random-seed-based simulation cycles. Table 1 lists the main simulation parameters:  
During each run, we captured statistics such as Coverage, Overhead, Delay, and energy usage (as certain profiles require considering energy factors). We report the averaged results across all random seeds, and highlight the trends that occurred as the node density increased from 40 to 240.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 3 Our Method
### Improved Feature Extraction Algorithm
#### 3.1.1 Calculate Global Adaptive Threshold  
The setting of the feature extraction threshold has a significant impact on the number and quality of extracted feature points. Because the extraction of feature points depends on the image's gray information, it is necessary to determine during feature detection whether and how many times the gray difference between a pixel and its surrounding pixels exceeds the threshold. The threshold represents the strictness of extracting feature points and reflects the gray difference between a pixel and its surrounding pixels. Therefore, based on the overall image's gray information, we can determine whether the image's gray change is obvious, similar to the contrast. The gray difference between the pixels and the surrounding pixels may be significant for images with obvious changes. At this time, selecting a smaller threshold will extract too many feature points, which will affect the speed of feature extraction, and those feature points that are extracted near the threshold have a low degree of recognition, which will affect the matching of subsequent feature points. If a larger threshold is selected, it may lead to the fact that the majority of pixels cannot meet the threshold and the extracted feature points are too few, which is not conducive to subsequent processing.  
For adaptation to different scenarios, this work proposes an adaptive threshold calculation approach that can extract a sufficient number of feature points while simultaneously guaranteeing the quality of the feature points for better tracking. Different from ORB-SLAM2 [11], which sets a specific threshold value for feature extraction, this paper sets the threshold value for feature extraction by considering the image grayscale information. The thresholds are specifically derived from the following:  
\[\textit{Threshold}=(\frac{1}{n}\sum_{x=1}^{width}\sum_{y=1}^{height}(I(x,y)- \overline{I(x,y)})^{2})/\overline{I(x,y)}, \tag{1}\]  
\[\overline{I(x,y)}=\frac{1}{n}\sum_{x=1}^{width}\sum_{y=1}^{height}I(x,y), \tag{2}\]  
where \(n\) is the number of image pixels, \(I(x,y)\) is each pixel's gray value, and \(\overline{I(x,y)}\) is the average gray value of the image. \(\frac{1}{n}\sum_{x=1}^{width}\sum_{y=1}^{height}(I(x,y)-\overline{I(x,y)})^{2}\) is the variance of the image gray value.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 3 Results and Discussion
### Characteristic Wavelength Selection  
Hyperspectral images have huge spectral band resources, which leads to the increased correlation of two images in adjacent bands and produces a large amount of redundant information, which brings great difficulties for data analysis and modeling. Therefore, it will be of great use to reduce the dimensionality of the hyperspectral images through feature selection and extraction and to express information about the entire dataset with a few variables. In this paper, a characteristic wavelength selection method based on the PCA load coefficient is used. PCA transformation of corn kernels and the absolute value of the load coefficient corresponding to each wavelength indicates the contribution of the wavelength to the model, so the load coefficient corresponding to each principal component can be taken as a condition for selecting the characteristic wavelength [39].  
Taking XY335 as an example, the five main principal components with a cumulative contribution rate of 99.9% are selected, as shown in Figure 4. It shows more grayscale, texture, embryo size, stalk, and folds in PC1, more embryo characteristics in PC2, more granular characteristics in PC3, and more surface texture in PC5. In the first five principal components, PC4 contains a lot of noise information, and the features are not obvious, which can be eliminated when applied. Therefore, the PC1, PC2, PC3, and PC5 components are used to replace the original image, and the load coefficient of each principal component is shown in Figure 5.  
Figure 3: Average spectra of five kinds of corn grains.
# Magnetic Self-Healing Composites: Synthesis and Applications
## 3 State-of-the-Art Fabrication Methods of Magnetic Self-Healing Composites
### Direct Mixing and Dispersion of Magnetic (Nano)Particles in a Polymeric Matrix  
This strategy involves the mixing and dispersion of magnetic (nano)particles in organic or aqueous media under thermal or ultrasonic stimulation followed by sample casting or moulding. Mechanical and magnetic features of the resulting materials will depend on the selection of the polymeric matrix, the particle features and their compatibility/interactions. This strategy has been explored for self-healing magnetic composites ranging from hydrogels to elastomers. Magnetic self-healing hydrogels combining Fe\({}_{3}\)O\({}_{4}\) NPs and poly(vinyl alcohol) (PVA) have been reported by [PERSON] et al., who describe an effective manner of embedding Fe\({}_{3}\)O\({}_{4}\) in PVA by moulding polymer aqueous solutions with particle contents ranging from 2 to 10 wt% at low temperature [50]. As the particle content increased, an enhancement of the tensile strength from 0.13 MPa to 0.32 MPa was observed because of the formation of a physical particle network of the large, stiff Fe\({}_{3}\)O\({}_{4}\) particles in a soft polymer matrix. Interestingly, the self-healing efficiency increased up to 5 wt% of Fe\({}_{3}\)O\({}_{4}\) and then diminished, probably due to the formation of large particle agglomerates, yielding a poor interfacial interaction with the polymer matrix and reducing the polymer chain mobility required for efficient damage healing. The incorporation of magnetic (nano)particles in thermoreversible gels results in a material that undergoes a reversible gel transition when it is heated by the application of an alternating magnetic field. The ability of electromagnetic fields to penetrate thick composites combined with the ability of such materials to be repeatedly heated and healed could prove particularly interesting in applications where large polymeric objects are subjected to repeated stresses such as wind turbines or helicopter rotors. [PERSON] et al. described an elegant methodology for preparing magnetic self-healing gels by the sonication of trifuran (pentaerythnitol propoxylate tris(3-(furfurylthiol) propionate) with (1,1'-(methylene-di-4,1-phenylene) bismaleimide using chromium (IV) oxide (CrO\({}_{2}\)) NPs from 0.1 to 10 wt% [45]. The as-prepared magnetic gel showed a reversible gel transition after being incubated under an alternating magnetic field of 15 mT and 390 kHz for 150 s. Note that the steady-state temperature reached by the polymer composite under the mentioned conditions increases with the particle content from 120 \({}^{\circ}\)C for 0.1 wt% to 146 \({}^{\circ}\)C for 10 wt%. Figure 3 shows a room-temperature self-healing magnetic soft elastomer made of a soft poly(dimethylsiloxane) (PDMS) matrix mixed with 20 nm-Fe\({}_{3}\)O\({}_{4}\) NPs as functional magnetic nanofiller with wt% ranging from 10 to 25% [30]. The preparation consisted of the mixing of an aqueous/methanol solution of PDMS-COOH with Fe\({}_{3}\)O\({}_{4}\) NPs by sonication,
## 4 Detailed Design
### Selfishness Assessment System
The process of updating the reputation works as follows:  
**(i) Average Reputation**:  
At every contact, the node calculates its average reputation using Equation (9), that is, the arithmetic mean of the reputation of the neighbours at the moment of contact with node \(B\):  
\[AvgR(A)=\frac{\sum_{\forall k\in neighborhood_{A}-\{B\}}R_{k}}{|neighborhood_{A}|-1}. \tag{9}\]
**(ii) Node Update**:  
When a selfish node is detected, a node updates its reputation by using Equation (10), where \(D(B)\in\ \{0,1\}\) assumes 0 when \(B\) is detected as selfish, and 1 otherwise. \(\delta\) is the weight assigned to each new observation:  
\[R_{B}^{{}^{\prime}}=R_{B}+\delta\times(D(B))-P_{cooperation}(A,B)). \tag{10}\]  
By using this reputation model, each node can infer the behaviour of a neighbour node based on its reputation value. Additionally, we also consider that a node (socially selfish or non-selfish) can refuse to receive a message due to resource depletion. The intuition behind this reasoning is that, in the real world, when the devices have resources constrained, the message relay could not accept to receive messages to save  
Figure 4: The selfishness assessment system running at each network node. its resources. To model this situation, FSF considers two devices' resources levels represented by two different thresholds: the energy level (represented by \(\alpha\)), and the memory space (represented by \(\beta\)). When a contact opportunity arises, if the message relaying device has an energy level less than \(\alpha\), or if the memory space used is more than \(\beta\), the node will decline to relay the message, independently of whether it is socially selfish or non-selfish. To decide which specific values should be assigned to these thresholds, we have performed a performance evaluation of FSF by variating the values used by these thresholds. We compared the FSF performance in terms of the delivery ratio and the delivery delay, in the scenarios evaluated in this work. We variated \(\alpha\) from 10% to 80%, and \(\beta\) from 20% to 90%. Based on the results achieved, we find that, for \(\alpha\) greater than 30% and \(\beta\) greater than 70%, the results are very similar. Thus, in our simulations, these thresholds were set by using these values.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Sectors of Focus  
The sectors included in this review include residential, commercial, and industrial. The residential sector was the focus of 28 studies [1; 3; 10; 11; 13; 16; 22; 23; 25; 27; 28; 30; 32; 34; 35; 38; 39; 45; 47; 48; 50; 52; 54; 55; 57; 59; 63; 65], the commercial sector was the focus of 13 studies [14; 17; 19; 20; 24; 33; 36; 37; 44; 46; 51; 53; 62], and 9 studies focused specifically on industrial applications [6; 7; 8; 9; 26; 56; 61; 66; 67]. Although some studies focused on a single sector, others analyzed two or all three. For instance, [15; 29; 40; 58; 60; 64; 69] focused on the flexibility potential of residential and commercial buildings or end-uses that are present in each building type. For example, the authors of [40] studied the flexibility potential of electric vehicles (EVs) that could charge and discharge at home or at the workplace. The authors of [31; 46] included analyses of the commercial and industrial sectors. The focus of [31] was thermal energy storage (TES), which can be present in both building types. Finally, [12; 18; 21; 43; 49] quantified the potential flexibility of all three sectors. Figure 2 illustrates the number of studies focusing on each sector, including the overlap between studies of each sector.
## 2 Study on the Working Principle of an Online Vibrating Tube Liquid Densitometer
### Effect of Temperature Variation on Measurement Results  
Equation (6) is the density-frequency fitting equation under ideal conditions, and the temperature is not constant in practical applications. Changes in temperature, which affect the structure, material properties, and density characteristics of the vibrating tube, will affect the intrinsic frequency of the vibrating tube itself, and the measured frequency signal will deviate from the actual value, so the system needs to be corrected for the temperature of the vibrating tube densitometer being experimented with \(\rho_{tx}\):  
\[\rho_{tx}=\rho_{x}[1+K_{18}(t-20)]+K_{19}(t-20) \tag{7}\]  
\(\rho_{tx}\): Temperature-corrected liquid density (kg/m\({}^{3}\)).
# Effect of Deacidification Treatment on the Flavor Quality of Zaosu Pear-Kiwifruit Wine
###### Abstract  
Conventional pear-kiwifruit wine has a bland flavor and sour taste, because of excessive L-malic acid content and, consequently, little consumer appeal. An _Oenococcus oenti_ strain, GF-2, has good malicic fermentation (MLF) performance and high glucosidase activity. Through a Box-Behnhcal design, the optimum MLF parameters for deacidification by GF-2 were determined: initial pH of 3.4, 5% /\(\%\) inoculation, and temperature of 20 \({}^{\circ}\)C, which reduced the malic acid content by 98.3%. The changes in the content of organic acids, polyphenols, and aromatic compounds after MLF were compared with chemical deacidification. MLF significantly decreased the total concentration of organic acids by 29.7% and promoted the accumulation of aromatic esters, higher alcohols, and terpenoids, but chemical deacidification markedly decreased aromatic compound content by 59.8%. MLF wine achieved the highest sensory scores for aroma, taste, and overall acceptability. Therefore, MLF with \(O\). _oenti_ GF-2 has great potential to markedly improve the quality of commercial pear-kiwifruit wine.  
pear-kiwifruit wine; _Oenococcus oenti_; malolactic fermentation; chemical composition; sensory evaluation
## 4 Discussion
### Limitations/Trade-Offs with the Proposed Reconfiguration Method  
In the proposed method, the integration of adaptive and fixed panels enhances energy efficiency while introducing certain limitations and trade-offs. Table 1 demonstrates that the solution space grows exponentially with the number of rows in the PV array. This growth makes computation challenging with traditional methods, whereas the genetic algorithm effectively explores this solution space and determines the optimal configuration within reasonable timeframes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Adaptive Part 1 (A\({}_{\text{A}}\)A\({}_{\text{A}}\)A\({}_{\text{A}}\))** & **Fixed Part (F\({}_{\text{F}}\)F\({}_{\text{2}}\)F\({}_{\text{3}}\))** & **Adaptive Part 2 (A\({}_{\text{A}}\)A\({}_{\text{A}}\)A\({}_{\text{3}}\))** \\ \cline{2-3}  & **[G\({}_{1}\): Isc\({}_{\text{C}}\), G\({}_{2}\): Isc\({}_{\text{C}}\), G\({}_{2}\): Isc\({}_{\text{3}}\)]** & **[G\({}_{1}\): Isc\({}_{\text{C}}\), G\({}_{2}\): Isc\({}_{\text{C}}\), G\({}_{1}\): Isc\({}_{\text{C}}\)]** & **G\({}_{2}\): Isc\({}_{\text{C}}\), G\({}_{2}\): Isc\({}_{\text{C}}\), G\({}_{1}\): Isc\({}_{\text{C}}\)** \\ \hline Scenario 1 & [4:1.51, 4:1.51, 4:5.17] & [(5:6-7:5),7(7:8-9):0.6, (8-5-8-9:5):10.04] & [9:3.41, 10:3.79, 10:3.79] \\ Scenario 2 & [10:3.79, 10:3.79, 6:2.27] & [(10:10-10):9.10,9(9-8-8):9.41, (8-7-5-7):58.49] & [10:3.79, 7.2.65, 5:1.89] \\ \hline \multirow{2}{*}{**Scenario**} & **Adaptive Part 1 (A\({}_{\text{A}}\)A\({}_{\text{A}}\)A\({}_{\text{A}}\)A\({}_{\text{A}}\))** & **Fixed Part (F\({}_{\text{F}}\)F\({}_{\text{2}}\)F\({}_{\text{3}}\)F\({}_{\text{4}}\))** & **Adaptive Part 2 (A\({}_{\text{A}}\)A\({}_{\text{A}}\)A\({}_{\text{A}}\))** \\ \cline{2-3}  & **[G\({}_{1}\):Isc\({}_{\text{C}}\), G\({}_{2}\):Isc\({}_{\text{C}}\), G\({}_{3}\):Isc\({}_{\text{C}}\), G\({}_{2}\):Isc\({}_{\text{C}}\)]** & **[G\({}_{1}\):Isc\({}_{\text{C}}\), G\({}_{2}\):Isc\({}_{\text{C}}\), G\({}_{3}\):Isc\({}_{\textThe integration of adaptive panels increases system complexity and has implications for hardware durability. However, these costs are offset by the long-term increase in energy production. Simulation results show how the proposed method improves energy efficiency and adapts to varying shading scenarios. The following section addresses the limitations and trade-offs introduced by the proposed method, examining each aspect in detail.