esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Verification of Optimum Conditions  
Verification tests of the optimum conditions were carried out using the same procedure as in the Taguchi experiment. All of the data evaluated are listed in Table 8. All of the conditions that satisfied the optimum values are shown in red. This demonstrates the feasibility and reliability of the Taguchi experimental design.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **A** & **B** & **C** & **D** \\ \hline
1 & 31.95 & 37.20 & 33.93 & 35.99 \\
2 & 34.81 & 35.33 & 35.19 & 34.79 \\
3 & 35.86 & 34.01 & 35.07 & 34.79 \\
4 & 36.46 & 32.55 & 34.89 & 33.52 \\ Delta & 4.51 & 4.65 & 1.26 & 2.47 \\ Rank & 2 & 1 & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Signal/noise (\(S/N\)) ratio analysis for absorption efficiency (\(E\)).  
Figure 5: Significant parameters according the radar chart analysis: (**a**) whole system; (**b**) individual data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Optimum Condition** & **Importance of Parameters** \\ \hline \(E\) & A4B1C2D1 & B \(>\) A \(>\) D \(>\) C \\ \(R_{A}\) & A4B4C2D1 & B \(>\) A \(>\) D \(>\) C \\ \(K_{G}\)_a_ & A4B4C4D1 & A \(>\) D \(>\) B \(>\) C \\ \(\gamma\) (max) & A1B4C1D4 & A \(>\) D \(>\) B \(>\) C \\ \(\gamma\) (min) & A4B1C4D1 & A \(>\) D \(>\) B \(>\) C \\ \(\varphi\) & A1B3C1D4 & A \(>\) D \(>\) C \(>\) B \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimum conditions and importance of parameters in the absorption process, according to the Taguchi analysis.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 2 Related Work
### Security and ML/AI  
Utilizing AI to address security challenges is not just a current trend but also a fundamental aspect shaping the future telecommunications landscape. When integrated with NFV and SDN, AI becomes a powerful tool, enabling early detection and rapid responses to a wide set of attacks. The authors in [28] present the potential use of ML/AI-enabled network slicing in current and future infrastructures, offering a comprehensive survey of the use of ML for the life-cycle management of network slices, starting from the slice preparation phase and covering all network domains, but without considering ZSM.  
In [29], the authors present a comprehensive survey of the security challenges associated with NFV, proposing the use of anomaly detection techniques to mitigate potential risks of cyberattacks. However, they do not consider network slicing in their work. Additionally in this line, and as mentioned before, [23] presents an architecture that uses an ML approach to enhance the process and increase security. In [30; 31], different types of ML techniques used for identifying cybersecurity risks, including phishing, malware, and spam, are reviewed. These techniques fall into two primary categories: unsupervised and supervised learning. In the first category, clustering methods are utilized to detect anomalies based on data similarities, thereby identifying potential security risks. The second category focuses on classifying different types of attacks into predefined categories. Additionally, in recent years, deep neural networks have proven to be decisive for multiple cybersecurity applications [32], such as malware detection, intrusion detection, or malicious traffic classification, among others.  
A systematic review of recent publications investigating the application of ML for security within the context of NFV and SDN [33] highlights important shortcomings. Our study aims to address these deficiencies through a practical demonstration. The shortcomings addressed include the lack of a specific ML integration architecture tailored for SDN/NFV, the limited availability of ML solutions beyond well-established attack categories such as DDoS, and inadequate provision of coordinated mitigation and response mechanisms. In particular, we integrated a cryptomining detection ML model to expand this scope [34], autonomously managed by leveraging SDN/NFV paradigms and enabling seamless mitigation with an E2E perspective.
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 2 Related Works  
### Face Forgery Detection  
Recent works significantly advance the performance and generalization capabilities in the field of face forgery detection. [PERSON] et al. [15] use multi-task learning and construct a Y-shaped auto-encoder for detecting forged facial images. CORE [16] introduces an erasing-based augmentation, this approach captures consistent representation across different augmentation strategies by using explicit regularization. [PERSON] et al. [17] use data entirely synthesized by StylGan3 [18] to train the model, and observe that the model would pay more attention to the facial features rather than the surroundings and background when trained on synthetic data. ID-unaware Deepfake Detection Model [19] forces the model to  
Figure 1: Comparative frameworks for face forgery detection. In framework (**a**), the process starts with data augmentation, followed by feature extraction using a CNN, which then outputs a feature that is used to determine the real or fake status of the image. Framework (**b**) differs, as it includes an additional step of disentanglement after the CNN feature extraction, splitting the feature into multiple features (F1, F2, etc.) before making the real or fake classification.  
concentrate on local image regions to avoid the detector learning identity representations, thus reducing implicit identity leakage and learning a broader feature representation. UNTAG [20] propose R-Solicer data augmentation, assuming that embedding of real samples follows a hypothetical multivariate Gaussian distribution, and they finally use self-supervision techniques to improve the generalization. SBI [21] uses downsampling as a frequency variation along with random augmentations to enrich data, it further integrates the data with masks, thus directing the model's focus towards artifacts within the images, which results in commendable outcomes. MAX-API [22] exploits pairwise learning and complementary information from different color space representations in a fine-grained manner to improve the detection of manipulated images.  
While these methods greatly improve the generalization of classical detection methods, they encounter limitations due to their dependence on predefined forgery patterns and the need to consider the entire feature space. Such expansive consideration often incorporates irrelevant factors, such as background noise and inaccuracies in falsified technical information, which can dilute the focus on essential forgery characteristics. This over-reliance on the entire feature space can impede the ability of detection models to isolate and identify the most discriminative features for face forgery detection.
# Development and Application of a New Apparatus for Moisture Measurement in Building Composites
## 1 Introduction
Another interesting method of testing is described by [PERSON] et al. [14]. This is a measurement of the water permeability of cracked concrete. Prior to the measurement, the tested prisms with dimensions of \(40\times 40\times 160\) mm were deformed by the so-called three-point bend to create defined cracks. The water flowed through the test sample into a collection vessel placed on an electronic scale, which recorded the weight gain of the water that flowed over time using a simple program. There are, of course, many other methods of measuring the permeability of materials that are based on a different basis, as shown by [PERSON] et al. [15]; [PERSON] et al. [16]; [PERSON] et al. [9]; and [PERSON] et al. [17]. A useful method for how to measure effective porosity and the permeability coefficient of pervious concrete was described in [PERSON] et al. [18]. Parameters of the different devices in the different experiments were reported as follows: the permeability was determined experimentally using a constant load permeameter. Cylindrical specimens (L = 100 mm and radius = 50 mm) were placed in a cylindrical mould, which is subjected to constant water pressure. The flow Q was obtained from the water mass collected as a function of time [19]. The tests are carried out on samples that are 16 cm in diameter and 14 cm in height with a longitudinal opening of 6.3 cm in diameter [9]. The sample mounting plate between the two sub-chambers was made of 6.4 mm thick perspex, which contained six 64.5 mm diameter samples [20]. As shown, the usual sizes of the tested samples are from 5 to 16 cm and the thickness is a maximum 15 cm. On the apparatus, it is able to test four square or circle shaped samples 25 centimetres in size and 20 centimetres in thickness. [PERSON] et al. [20] have presented a methodology for carrying out moisture permeability measurements under a range of barometric pressures. The technique has been successfully applied to a particle board test material. Current methods for assessing the permeability of cracked concrete generally have long testing periods and poor experimental reproducibility. The authors [13] present a rapid test for establishing the water permeability of cracked mortar specimens. The principle of the method is based on the height of the water column. There are many existing permeability measurement methods, but each method is only suitable for specific materials. The new measuring device has been designed to test building composites up to 20 cm thick and is fully automated. The tested materials will be evaluated according to the water flow per unit of time and the shape of the flow curves.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 3 Fault Reconstruction Based on a Hybrid Multi-Objective Genetic Algorithm
### Construction of Hybrid Multi-Objective Functions  
The total energy consumption and fuel consumption balance must be made as small as possible, while the satellite swarm health must be made as large as possible. According to the gray clustering [34] weight determination method, weight values are assigned to the optimization indicators, and the multi-objective problem is transformed into a single-objective problem.  
Since the units of the three indicators are different, normalization and standardization are used to obtain the normalized indicator function \(f_{i}(x)^{*}\):  
\[f_{i}(x)^{*}=\frac{f_{i}(x)-\text{min}f_{i}(x)}{\text{max}f_{i}(x)-\text{min}f _{i}(x)} \tag{10}\]  
where \(f_{i}(x)\) is the \(i\)-th indicator function. Therefore, the evaluation function \(F(X)\) can be expressed as:  
\[F(X)=F(-H_{swarm},J_{sum},P) \tag{11}\]  
The detailed flow of the hybrid multi-objective genetic algorithm is shown in Figure 6.  
Figure 4: Decision tree of multi-objective fault reconstruction.  
Figure 5: Schematic diagram of the genetic code.
# Geo-Spatial Context Provision for Digital Twin Generation
## 5 Discussion  
In the previous section, we reviewed relevant related work pertaining to existing DT approaches that use geo-spatial context provided by LiDAR technology and methodologiesfor the creation of DTs. We discussed our research contribution in light of the existing approaches. Taking into concern DT-related approaches not considering process twins and subject orientation for geo-spatial CPS development so far, the presented approach extends the existing knowledge base of subject orientation and the application of this behavior-centered approach for this type of CPS and DTs (which were outlined in Section 3.1).  
Specifically, our approach was built on the digital twin conception presented in [8]. The authors of this article focused on designing a subject-oriented DT as a means of developing a CPS through behavior specification. They detailed how to depict certain aspects of CPS structure and behavior (such as variability of behavior) through subject orientation and associated modeling techniques and exemplified this through evolving models for a traffic management scenario. Data exchange between the DT and the CPS were established as a core aspect of the concept, without going further into details on how this data can be structured. With our contribution in this article, we detailed the initial concept of a behavior-centered DT in terms of how this integration between the data of a CPS and the process models at the core of this DT-type can look like and work effectively in the course of system design and engineering. We described this integration specifically centered around geo-spatial context data, a type of data with considerable importance in the CPS context, as was outlined in the introduction. With the architecture depicted in Figure 9 and the guiding methodology steps in Figure 10, including the associated considerations, we specified how an infrastructure set-up for this type of twin can be realized. We also discussed some of the modeling-related aspects and provided sample model variants for the illustration case, in the respect that they are relevant to the proposed architecture and methodology.  
As was also identified in the previous section, a point that is still open for exploration concerns the automatic deployment of subject-oriented models. This is also meant in the sense of a run-time environment ensuring automatic execution of models through the CPS and its components, as well as them providing the relevant data to the environment without further configuration and engineering activities (as they are currently required in the proposed methodology). Furthermore, this also represents a core step towards the idea of design-integrated engineering presented in [8], where the CPS is developed and adapted dynamically through the models in the vein of Model-Driven Engineering and based on the idea of the DT as a virtual entity connected with the twinned entity in such a way that changes to one are reflected in the other.
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 2 Intelligent Packaging Techniques
### RFID Coupling with Sensing Techniques  
Indicators and flexible RFID tags are regarded as the most commercially viable technologies that can be integrated in intelligent packaging [31].Recently, [PERSON] et al., (2017) developed a passive RFID/NFC sensor tag. The tag had rectangular antenna coil (32 mm width, 51 mm height) and worked at 13.56 MHz. In the coil structure, the sensor tag IC, including its peripheral components, was placed. There was no battery in the RFID sensor tag. The sensor tag utilized power from the magnetic field generated by the RFID reader. Additional smart sensors can be connected with the passive RFID/NFC sensor tag by employing copper wires [32].  
Combining sensors with RFIDs can overcome the limited functions of indicators, i.e., the RFID is used for product identification while the indicator is used to determine and display the products' current status. The data carrier does not record the products' historical status, though it can be detected by indicators. Consequently, in recent years, the main focus has been on the development of low-cost sensors that consume less power with RFID. This has allowed for a better management of the perishable products sold by retailers and giving consumers more quality information on the products. The three types of RFID tags are developed for different applications in practice.
## 3 Materials and Methods
### PlaceNet-Based SBD Method
#### 3.1.2 Shot Detection Network  
Most of the existing deep-learning-based SBD methods use deep neural networks to extract the features of each frame and then measure the similarity between two contiguous frames. Some researchers use video segments to train shot transition categories. However, the networks are targeted to recognize the object. Generally, the training object categories cannot represent most objects in real life and in videos. Therefore, place categories are used for training instead of object categories. Dataset Construction  
The datasets are mainly based on the Places database [52]. The website Places [53] provides the demo for testing the Place365 dataset. The training network is based on the PyTorch model. Here, we choose two different categories--\"forest\" and \"fields\"--for the test. The images are downloaded from the website by using the query word. The query word of each image, from left to right, from top to bottom, respectively, is \"forest\", \"forest cartoon\", \"forest 3D model\", \"fields\", \"fields cartoon\", and \"fields 3D model\". Their classified top-1 categories are shown in Figure 4.  
Figure 3: Comparison of the results using LBP-SVD for candidate segment selection. (**a**) LANEIGE, (**b**) [PERSON], (**c**) The Sound of Music. Figure 4 shows that the places classification by using the pre-trained Places365 demo still has some errors, especially for similar categories. What is more, the precision for computer-generated images, such as cartoon and three-dimensional (3D) models, is not as good as that for natural images. Due to the massive variation in videos, in order to get a good result for places classification, the training dataset also should include as many types of images as possible. Therefore, we make the following changes to the Place365 dataset. 1. We add computer-generated images, such as cartoons and 3D model images, to each category. 2. We merge the categories that have common features into one category. For example, the categories of \"forest_broadleaf\", \"forest_needleleaf\", \"forest_path\", and \"forest_road\" can be merged into the category \"forest\". Network Architecture  
The popular CNN architectures, such as Alexnet [27], ZFNet [54], GoogLenet [28], VGG [29], ResNet [55], and InceptionResNet-v2 [56], proposed in recent years have been widely used in image and video applications. Among them, InceptionResNet-v2 can perform with the top-1 error of 19.6% and the top-5 error of 4.7% in the ILSVRC 2012 image classification challenge dataset [57]. The ILSVRC dataset contains nearly all object classes, including rare ones, and is uniquely linked to all concrete nouns in WordNet. In Table 6, some deep neural networks are briefly described. Considering the performance and the computing complexity, the GoogleNet model and the ResNet-50 model are more efficient. Therefore, the pre-trained GoogleNet and ResNet-50 support
# Numerical Technique for Study of Noise Grating Dynamics in Holographic Photopolymers
## 2 Methods  
To study the how coherent light illuminating a scattering, photosensitive medium drives the development of both signal and noise, we implement a multi-physics code suite. The technique models the holographic recording process by iterating four primary calculations as a function of exposure time. These are (1) the Fourier beam-propagation (FBPM) method used to calculate optical propagation and diffraction within the 3D index structure, (2) a discrete time-stepping solution of the first-order photochemical rate equations, (3) diffusion of mobile species via a fast-Fourier transform (FFT) solution of Fick's law and (4) update of the material index via an a priori model for two-component holographic photopolymers. We describe each step in more detail below.
## 3 Construction of Knowledge Graph for Spectrum Sensing Data from LEO Satellite
### Ontology Construction
A total of five types of relationships are specified: be provided with, consist of, sensing of, subsystem, and numerical relationships. Figure 3: The structure of defined classes and attributes. * Verify and analyze ontology. After completing steps (a)-(d), the constructed ontology model should be analyzed and validated. If the constructed ontology model is unreasonable, each step needs to be checked according to the construction process. It is necessary to make corresponding modifications to the identified issues, and then the modified model should be re-evaluated until it passes validation. In this paper, Protegee [24], an ontology editor developed by Stanford, is applied to construct the ontology layer of the spectrum knowledge graph. The integral ontology layer is shown as Figure 5.  
Figure 4: The defined relationships.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Healthcare Scenario: MC Children's Hospital  
To implement the proposed risk-aware RBAC and ABAC model, several attributes and values of the risk factors need to be quantitatively identified. Hence, a closed-world scenario is used in the implementation process and the scenario is used to check the applicability of the proposed model in real-world scenarios in the healthcare system.  
Implementing the proposed model using real-world scenarios offers several key advantages. It provides a rich context for thorough testing within complex healthcare systems, enhancing understanding and decision-making. This approach validates design assumptions, ensuring the model accurately reflects the dynamic and evolving nature of healthcare environments. It also helps identify unexpected challenges, improving the model's robustness and adaptability, especially in emergency situations. Furthermore, real-world testing ensures compliance with stringent regulations like HIPAA and GDPR, ensuring the model meets necessary legal and ethical standards.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Conductive Charging
#### 4.1.2 Charging Level
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Aim** &
\begin{tabular}{c} **Charging** \\ **Mode** \\ \end{tabular} & **Pros** & **Cons** \\ \hline
[54] & 2019 & \begin{tabular}{c} Designed a low power AC single \\ phase charger \\ \end{tabular} & Mode 1 & \
\begin{tabular}{c} Controlled voltage and current \\ supply \\ \end{tabular} &
\begin{tabular}{c} Ignored charging efficiency, cost, \\ and EVs standardized connectors \\ \end{tabular} \\ \hline
[55] & 2021 & \begin{tabular}{c} Discussed an on-board AC charger \\ for plug-in EVs \\ \end{tabular} & Mode 1 & \
\begin{tabular}{c} Dynamic adaptability, steady \\ charging, and controlled power \\ supply \\ \end{tabular} &
\begin{tabular}{c} Ignorance of charging cost and \\ specific charging ports \\ \end{tabular} \\ \hline
[58] & 2021 & \begin{tabular}{c} Designed a 48V on-board AC fast \\ charger for EVs \\ \end{tabular} & Mode 3 & \
\begin{tabular}{c} Reduced charging time, high \\ charging efficiency, and controlled \\ charging charging \\ \end{tabular} &
\begin{tabular}{c} Need to highlight charging cost, \\ controlled power supply, and \\ charging connectors \\ \end{tabular} \\ \hline
[59] & 2022 & \begin{tabular}{c} Introduced a three phase fast DC \\ charger for EVs \\ \end{tabular} & Mode 4 & \
\begin{tabular}{c} High efficiency, low distortion rate, \\ high power yield \\ \end{tabular} &
\begin{tabular}{c} No discussion on charging cost and \\ different charging connectors \\ \end{tabular} \\ \hline
[60] & 2022 & \begin{tabular}{c} Designed a low voltage charging \\ technique for EV \\ \end{tabular} & Mode 4 & \
\begin{tabular}{c} Low voltage stress, high efficiency \\ with high output voltage \\ \end{tabular} &
\begin{tabular}{c} Need to deal with charging cost and \\ standardized charging standards \\ \end{tabular} \\ \hline
[63] & 2022 & \begin{tabular}{c} Proposed a two-level charging \\ strategy for hybrid EVs using an \\ adaptive dynamic programming \\ \end{tabular} & Level 2 & \
\begin{tabular}{c} Low energy consumption, high \\ efficiency \\ \end{tabular} &
\begin{tabular}{c} Should focus in real-time scenario, \\ less adaptability of dynamic \\ networks \\ \end{tabular} \\ \hline
[65] & 2022 & \begin{tabular}{c} Designed a three-phase off-board \\ charger algorithm to control the \\ powered grid from fast charging \\ \end{tabular} & Level 3 & \
\begin{tabular}{c} Improved feasibility, controlled \\ power flow, and low distortion rate \\ \end{tabular} &
\begin{tabular}{c} No discussion on charging \\ efficiency, cost, and standardization \\ of charging standards \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of various EV charging Levels/Modes.