esa-sceva/satcom-chunk-collection huggingface dataset:
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 8 Discussion
### Exfiltration Countermeasures
#### 8.1.2 Signal Interference  
The method of signal interference is commonly employed to obstruct electromagnetic [48] and acoustic [49] air-gap channels. Deceptive interference is conducted on the receiving end by generating random signals resembling those of the air-gap channel, or high-power signals are generated to suppress interference on the air-gap channel signal. Conventional WiFi jammers can impede WiFi signal communication within distances of several hundred meters [50]. For the power line air-gap channel discussed in this article, workloads with comparable power to that of the air-gapped computer can be connected in parallel to the power line to generate false or strong signals for interference purposes. However, workloads are energy-intensive devices, which may result in increased user power consumption and could prove impractical in certain environments where the power system cannot support them.
## 1 Introduction  
Conventional ways of improving the low-frequency absorption by a porous layer are to increase its thickness and to mount it with an air space between it and a rigid backing surface. Restrictions on space and weight in aerospace or automotive applications make such solutions impractical. Perforation of porous materials may improve their sound absorption [1, 2]. Long convoluted sound channels or labyrinths in a solid matrix result in good impedance matching [3] and high absorption coefficients at wavelengths much greater than the layer thickness [4]. However, computations for complex perforations and for labyrinthine structures use numerical methods such as Finite Element Modelling. This paper offers analytical approximations for predicting the result of making labyrinthine slit perforations in a rigid-porous layer. An array of narrow labyrinthine slits in a non-porous solid may be considered as if it were a porous material with highly tortuous slit pores. In this paper, contiguous units, each of which contains a labyrinthine slit, are considered to form a rigid-porous solid acting, acoustically, as an effective fluid layer. Analytical approximations for the acoustical properties of horizontal- and vertical-wall labyrinths in a non-porous matrix use the classical result for sound propagation in a rigid-porous medium with tortuous slit-like pores [5]. Then the dual-porosity theory [6, 7] is used to predict the results of incorporating labyrinthine slits in a matrix of a rigid-porous material with high flow resistivity. The influence of additional tortuosity due to changes in labyrinthine slit cross section [8] is considered. Useful narrowband low-frequency absorption is predicted to result from pressure diffusion and tortuosity effects. Example predictions suggest that labyrinthine slit perforations in porous materials offer a great potential for deep sub wavelength narrowband absorption peaks.
## 2 Critical Infrastructures
### Critical Infrastructure Definition
#### 2.1.1 The European Union Perspective  
In the European Union, the European Program for Critical Infrastructure Protection (EPCIP) provides the overall framework for activities aimed at enhancing the protection and resilience of critical infrastructures across all EU member states. The program acknowledges that threats to critical infrastructure extend beyond terrorism and include criminal activities, natural disasters, and accidents. It adopts an all-hazards cross-sectoral approach to effectively address these threats. The EPCIP is supported by the Directive on European Critical Infrastructures, which identifies and designates European critical infrastructures (ECIs) in the energy and transport sectors. The directive mandates the preparation of operator security plans by the owners/operators of designated ECIs, promoting advanced business continuity planning. It also emphasizes the appointment of security liaison officers to facilitate coordination between the owner/operator and the responsible national authority for critical infrastructure protection [6; 7].
# FANT: Flexible Attention-Shifting Network Telemetry
## 5 Attention-Switching Strategy
### Problem  
Formally, FANT addresses the following weighted set cover problem (WSCP), where sets may contain overlapping elements. Given:  
* \(L\): \(L\) The universal set containing all links \(l\) in the topology.
* \(R\subseteq L\): The target links that need comprehensive measurement.
* \(P\): A collection of probe cycle sets, where each probe cycle \(p\in P\) is a set of links \(l\). If \(l\in p\), \(I_{l,p}=1\).
* \(C(p)\): The cost associated with each probe cycle \(p\).
* \(x_{p}\): A binary decision variable where \(x_{p}=1\) if probe cycle \(p\) is selected, and \(x_{p}=0\) otherwise.  
**Objective:**  
\[\min\sum_{p\in P}C(p)\cdot x_{p} \tag{1}\]  
**Constraints:**  
\[\sum_{p\in p}x_{p}*I_{l,p}\geq 1,\forall l\in R \tag{2}\]  
\[C(p)=|p|,\forall p\in P \tag{3}\]  
The **Objective** is to minimize the total cost of the selected probe cycles. A probe cycle is considered'selected' when it is configured for detailed measurement. **Constraint (1)** ensures that every target link in subset \(R\) is covered by at least one of the selected probe cycles. **Constraint (2)** requires that the cost of a probe cycle \(p\) is determined by the number of links it contains, reflecting the length of the probe.  
To clearly introduce the algorithm, as shown in Figure 3, we present an example using a topology with seven links and seven probes (i.e., Probe 1 visits links 2 and 5; Probe 2 visits link 1; Probe 3 visits links 2, 3, and 4; Probe 4 visits links 1, 2, 4, and 6; Probe 5 visits links 4 and 6; Probe 6 visits links 3 and 7; Probe 7 visits links 1, 6, and 7).  
Figure 3: Basic idea of attention-switching strategy.
## 4 Findings
### Smart Environments
#### 4.3.3 Smart Factory  
A multi-center blockchain-based security and privacy model is proposed to reshape traditional IoT architecture for smart factories [119]. The proposed architecture consistsof five layers: application layer, firmware layer, storage layer, management hub layer, and sensing layer. Users are provided with different services by the application layer. The firmware layer is used to connect all layers through underlying implementation technologies, data are stored in a distributed form in data centers represented by the storage layer, the process of managing the data and creating blocks is completed by the management hub layer, and finally, the process of obtaining data and preprocessing occurs in the sensing layer using sensors with microprocessor (computing power). This architecture is divided into intranet and extranet; the first deals with data collection and storage, and the latter aims to offer users different services by utilizing the data. This model is designed with a private blockchain where all nodes are trusted initially; thus, it does not include a reward mechanism or competition. The block structure is created with two parts: block body and header (stores structured data and its attributes). Finally, the authors combined two models: Biba and Bell-La Padula (BLP) to ensure CIA requirements.
# Evaluating the Robot Inclusivity of Buildings Based on Surface Unevenness
## 4 Experimental Validation
### Test Site 1: Study Area  
The study area was a multi-purpose space with synthetic polymer material as the predominant floor material. Challenges posed by environmental conditions included a carpeted area in one part of the study area and a difference in floor levels near access doors to technical rooms. The generated LiDAR map of the study area and the path taken by the robot during the auditing process are shown in Figure 4a. The corresponding unevenness of the surface (_U_), measured using Algorithm 1, is plotted onto the map as depicted in Figure 4b. The resultant RIL-SU heatmap for this site is presented in Figure 4c.  
To validate the generated RIL-SU heatmap, three sample zones with different RILs were selected in the second phase. The selected zones are represented as 'L1', 'L2', and 'L3' in Figure 4b,c. The robot traversed through the selected locations at its nominal speed. Location 'L1' in the surface unevenness map suggested that \(U\) was around 0.5. This low surface unevenness was compliant with manual observations of the environment, as this zone's surface area was smooth without uneven bumps, as shown in Figure 5a. According to the generated RIL-SU heatmap, location 'L1' was tagged as 'High' RIL. When the robot traversed through this location, no distortion of the SLAM was observed, validating the high robot inclusivity at the location.  
Figure 4: (**a**) A 2D LiDAR map of the study area and path taken by the audit robot; (**b**) recorded unevenness variation as a heatmap; (**c**) resultant RIL-SU heatmap for the site.  
Figure 5: Robot navigation performance at different zones in the study area during the validation phase. (**a**) Location 1, (**b**) Location 2, and (**c**) Location 3.  
Location 'L2' was a slightly bumpy area due to the height difference of the floor mat and the floor. Thus, a \(U\) value around 1.5 was observed (see unevenness heatmap in Figure 4b). As a result of this moderately elevated \(U\), the RIL of that location was tagged as 'Moderate' (see Figure 4c). When the robot traversed this location, a drift on the laser scan (Figure 5b) was observed, indicating the moderate RIL.  
According to the RIL heatmap, location 'L3' was a 'Low' RIL zone. Here, the surface area was highly uneven for the robot to traverse, as shown in Figure 5c. When the robot traversed here, a huge distortion of the SLAM could be observed, implying navigation failure. This result is in agreement with the generated RIL-SU heatmap.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 7 Comparison and Discussion of Research Results
### Comparison with Centralized Global Registry Systems
* Smart Contract Functionality: Unlike LCCN or ISAN, which are static assignment systems, our NFT-based approach allows for programmable registries. For example, an ISWC (International Standard Musical Work Code) implemented as an NFT could automatically track and distribute royalties based on usage, a feature not possible in the current centralized system [50]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Centralized Systems** & \begin{tabular}{c} **Our NFT-Based** \\ **System (Ethereum)** \\ \end{tabular} & \
\begin{tabular}{c} **Our NFT-Based** \\ **System (BSC)** \\ \end{tabular} &
\begin{tabular}{c} **Our NFT-Based** \\ **System (Polygon)** \\ \end{tabular} \\ \hline Transparency & Moderate & High (Public ledger) & High (Public ledger) & High (Public ledger) \\ \hline Scalability (transactions  per second) & High (1000+ TPS) & Low (15–30 TPS) & High (300+ TPS) & Very High (7000+ TPS) \\ \hline Single Point of Failure & Yes & No & No & No \\ \hline Interoperability & Limited & High & High & High \\ \hline Cost Efficiency (per  transaction) & Low ($0.001–$0.01) & High ($1–$10) & Moderate ($0.1–$1) & Low ($0.001–$0.01) \\ \hline Immutability & Moderate & High & High & High \\ \hline Smart Contract  Functionality & No & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._* Update Speed: The ISTC (International Standard Text Code) system updates its database weekly [51]. Our blockchain-based system would reflect changes in near real-time, with new entries visible within minutes across the entire network. * Global Accessibility: While systems such as ASIN are tied to specific platforms (Amazon), our NFT-based registry would be globally accessible. This is particularly relevant for international standards such as BICI (Book Item and Component Identifier), which could benefit from blockchain's borderless nature [52]. These comparisons demonstrate the significant advantages our NFT-based system offers over traditional centralized registries. While centralized systems have served their purpose, the transition to a blockchain-based solution addresses many of their inherent limitations, providing enhanced security, transparency, and functionality for global digital registries.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 2 Related Works
### CNN Residual Module  
Convolutional Neural Networks (CNN) [20], as a special form of feedforward neural network, can be divided into two main modules: feature extraction and classification. The feature extraction module cleverly integrates multiple convolutional layers and pooling layers. Through the sliding computation of convolution kernels on the data and the downsampling mechanism of pooling layers, it effectively mines and abstracts the deep features within the data. Given the unique advantages of one-dimensional CNNs in processing time series data, this paper specifically selects this architecture to handle the latitude and longitude time series data of vessels.  
The input data for a one-dimensional CNN is a one-dimensional array, and the convolution process involves the sliding of the convolution kernel along the one-dimensional axis. Figure 1 shows an example with a convolution kernel size of three, one convolutional layer, and a stride of one, where \(X_{1}\), \(X_{2}\), and \(X_{3}\) represent the input data, and \(Y_{1}\) represents the output feature vector.  
Figure 1: The Convolution Principle of One-Dimensional CNN.  
As the depth of neural networks increases, the capability for feature extraction becomes richer, indicating that neural networks possess stronger nonlinear representation abilities. However, experiments have shown that increasing the number of network layers often leads to performance degradation. [PERSON] et al. [21] highlighted the issues of gradient vanishing, gradient explosion, and model degradation that can arise from blindly stacking convolutional layers and pooling layers when proposing the residual network.  
This paper introduces a residual module, and the operation process of this module is as follows: The input data first enters the first one-dimensional convolutional layer. The convolutional kernels slide over the data and perform multiplication and addition operations to extract preliminary features. These preliminary features then enter the second one-dimensional convolutional layer, where the convolutional kernels further dig into the data to obtain more abstract features. Subsequently, the abstract features are added element-wise to the preliminary features, integrating the information from both the high-level and low-level features, which helps prevent the vanishing gradient problem. The results of the addition go through an activation function to introduce non-linearity, and finally, the final output is generated. This approach allows the network to learn the mapping relationship between input and output more effectively, overcoming issues such as gradient vanishing and further optimizing network performance. The structure is shown in Figure 2. If the input of the \(i\)-th residual block is denoted as \(X^{i}\) and the output as \(X^{i+1}\), the relationship between them is as follows:  
\[X^{i+1}=Activation\Big{(}X^{i}+F\Big{(}X^{i}\Big{)}\Big{)} \tag{1}\]  
In Equation (1), Activation refers to the activation function, and \(F(X^{i})\) represents the operations of the residual module.
## 6 Evaluation
### User Study 1: Similarity Rating
#### 6.2.4 Results and Discussion  
The experimental results are plotted in Figure 11 for the eight comparisons. The condition RTA-RTA showed the highest rating among all real-real pairs (93.47%). The average similarity scores for the three real-real pairs were used as the upper reference. The lower bound's similarity score was selected using the average score of the two lower references. The highest-rated pair of real-virtual texture surfaces VTA-RTA had a score of 76.93%, whereas the lowest was 59.33% for VTC-RTC. This result indicates that the haptic similarity between the virtual surface generated by our system and the corresponding real surface was quite comparable to the similarity between two real surfaces. In other words, the realism of the virtual feedback was not very inferior to the realism of the real feedback.  
These results can be considered promising since the feedback for the virtual surface completely lacked kinesthetic information, while the real ones had it. Achieving 59.52% realism only with tactile feedback in a rigid surface exploration task can be considered encouraging. Nevertheless, our approach tried to indirectly mimic this kinesthetic feedback of rigidity using the pressure feedback based on the dual-layer design, and the effect of our new design was evaluated in the following User Study 2.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors
### Additive Manufacturing Metal Mirror Fabricating Considerations
#### 2.2.2 Design of Support
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Manufacturing Method** & **P** & **\(\
u\)** & **H** & **Post- (mm/s)** & **(J/mm)** & **Treatment** & **Orientation** & **E (GPa)** & \(\sigma_{y}\) (MPa)** & \(\sigma_{uts}\) (MPa)** & **Ductility (\%)** & **HV** \\ \hline PBF & 250 & 500 & 0.50 & As built & Longitudinal & & 250 & 350 & 2.5 & 145 \\  & & & & Transverse & & 240 & 280 & 1.2 & & \\ \hline  & & & & T6 & Longitudinal & & 285 & 340 & 4.5 & \\  & & & & Transverse & & 290 & 330 & 2.2 & & \\ \hline  & 250 & 500 & 0.5 & As built & Longitudinal & & 125 & 250 & 6.6 & 75 \\  & & & & Transverse & & 140 & 270 & 4.6 & 75 \\ \hline  & & & & T6 & Longitudinal & & 295 & 350 & 6.5 & 118 \\  & & & & Transverse & & 285 & 340 & 4.9 & & \\ \hline  & & & & As built & Longitudinal & \(75\pm 10\) & \(270\pm 10\) & \(460\pm 20\) & \(9\pm 2\) & \(119\pm 5\) \\  & & & & Transverse & \(70\pm 10\) & \(240\pm 10\) & \(460\pm 20\) & \(6\pm 2\) & \(119\pm 5\) \\ \hline  & & & & 2 h/300 \({}^{\circ}\)C & Longitudinal & \(70\pm 10\) & \(230\pm 15\) & \(345\pm 10\) & \(12\pm 2\) & \\  & & & & Transverse & \(60\pm 10\) & \(230\pm 15\) & \(350\pm 10\) & \(11\pm 2\) & \\ \hline  & 200 & 1400 & 0.14 & As built & Longitudinal & & \(68\pm 3\) & & \(391\pm 6\) & \(5.55\pm 0.4\) & \\  & & & & Transverse & & & 396 \(\pm\) 8 & \(3.47\pm 0.6\) & 127 \\ \hline  & 200 & 1400 & 0.14 & As built & Longitudinal & \(68\pm 3\) & & \(396\pm 8\) & \(3.5\pm 0.6\) & \(136\pm 9\) \\ \hline  & & & & 6 h/175 \({}^{\circ}\)C & Longitudinal & \(66\pm 5\) & & \(399\pm 7\) & \(3.3\pm 0.3\) & \(152\pm 5\) \\ \hline  & 195 & 800 & 0.24 & 2 h/300 \({}^{\circ}\)C & Longitudinal & & \(252\pm 10\) & \(348\pm 5\) & \(6.6\pm 0.3\) & \(105\pm 2\) \\  & & & Transverse & & & \(240\pm 5\) & \(347\pm 6\) & \(5.1\pm 0.3\) & \(108\pm 3\) \\ \hline  & 195 & 800 & 0.24 & 2 h/300 \({}^{\circ}\)C & Longitudinal & \(73\pm 1\) & \(243\pm 7\) & \(330\pm 3\) & \(6.2\pm 0.3\) & \\  & & & Transverse & & \(72\pm 1\) & \(231\pm 3\) & \(329\pm 2\) & \(4.1\pm 0.2\) & \\ \hline  & 200 & 571 & 0.35 & As built & Transverse & & & \(33\pm 10\) & \(1.4\pm 0.3\) & \\ \hline  & & & & 6 h/160 \({}^{\circ}\)C & Transverse & & & \(292\pm 4\) & \(3.9\pm 0.5\) & \\ \hline  & 175 & 1025 & 0.17 & As built & Longitudinal & & 250 & 340 & 1.2 & \\  & & & Transverse & & & 225 & 320 & 1 & \\ \hline  & 400 & 1000 & 0.40 & 2 h/300 \({}^{\circ}\)C & Longitudinal & & \(182\pm 5\) & \(282\pm 5\) & \(25.2\pm 1\) & \\  & & & Transverse & & & \(184\pm 5\) & \(288\pm 5\) &
## 3 Materials and Methods
### HPGE \(\gamma\)-Spectrometry Set-Up  
For the HPGE \(\gamma\)-spectrometry analysis, each aliquot of the investigated samples was first powdered then dried at 105 \({}^{\circ}\)C in an oven for 24 h to completely remove moisture and finally inserted in a Marinelli container of 1 L capacity. After 40 days, the secular radioactive equilibrium between \({}^{226}\)Ra and its daughter radionuclides was reached, and samples were ready for \(\gamma\)-spectrometry counting [31].  
In addition, samples were counted for 70,000 s to reduce the statistical uncertainty, and spectra were analyzed to assess the specific activity of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K. In particular, the \({}^{226}\)Ra-specific activity was quantified through the 295.21 keV and 351.92 keV \({}^{214}\)Pb and 1120.29 keV \({}^{214}\)Bi \(\gamma\)-ray lines, respectively, and the \({}^{232}\)Th-specific activity was determined  
Figure 1: A photo of the experimental set-up for the radon exhalation assessment.  
by using the 911.21 keV and 968.97 keV \({}^{228}\)Ac \(\gamma\)-ray lines. For \({}^{40}\)K, the evaluation was performed from its \(\gamma\)-line at 1460.8 keV [32].  
The true coincidence summing corrections (TCC) was not carried out for the photopeak of \({}^{214}\)Bi nor for the 911.21 keV \({}^{228}\)Ac \(\gamma\)-ray line because it fell within the counting uncertainty. Moreover, the correction due to the interference of \({}^{228}\)Ac in the photopeak of \({}^{40}\)K was not performed because the concentration of the \({}^{232}\)Th series was very low and such influence was not produced.  
Table 1 reports the operating parameters of the employed detector.  
The sample was installed into lead sums to screen off background ambient radiation. It is worthy to note that efficiency and energy calibrations were carried out for the 1 L sample holder shape using Eckert and Zigler Nuclitec GmgH traceable multinuclide radioactive standard, number AK-5901 [33]. The Gamma Vision (Ortec) software version 8 was used for data acquisition and analysis [34].  
The activity concentration (Bq kg\({}^{-1}\) dry weight, d.w.) of the investigated radioisotopes was given by [35] as follows:  
\[\mathrm{C(Bqkg^{-1}d.w.)}=\frac{\mathrm{N_{E}}}{\mathrm{\epsilon_{E}}\mathrm{ V_{d}}\mathrm{M}} \tag{2}\]  
where N\({}_{E}\) indicates the net area of a peak at energy E; \(\mathrm{\epsilon_{E}}\) and \(\mathrm{\gamma_{d}}\) are the efficiency and yield of the photopeak at energy E, respectively; M is the mass of the sample (kg); and t is the live time (s) [36].  
The quality of the HPGe gamma spectrometry results was certified by the Italian Accreditation Body (ACCREDIA) on the basis of the quality controls performed according to the UNI 11665:2017 [37].
## 5 Experimental Setup and Results Evaluation  
To build the ANN model, MATLAB R2022b is used, whereas the encoding-based model is constructed in LabVIEW 2017 since it provides a real-time and user-friendly GUI for monitoring. Additionally, the LabVIEW environment contains a comprehensive set of tools utilized for acquiring, analyzing, storing, and displaying data from real-time applications. Figure 7 depicts the main part of the designed graphical programming block diagram for the proposed encoding-based machine learning model. As can be seen, every feature is considered either as a nominal or continuous value. Then these values become  
Figure 7: Main part of the LabVIEW block diagram for the proposed encoding-based model.  
encoded and compared to several threshold values to classify three classes of status and compute the accuracy accordingly.  
To evaluate the performance of the proposed model, the dataset is divided into two groups, training and testing, with 70% of the data used for training and the remaining 30% utilized for testing (225 patients). Then, K distinct learning trials were executed by partitioning the data into K groups using the K-fold cross-validation, where K is set to 10 (10% of the testing data) in each testing cycle, resulting in the formation of 10 groups. Then, we run the presented encoding-based model and evaluate its accuracy. Figure 8 displays the testing window that shows the validation process of the encoding-based model. Clearly, it can be seen that both the actual (hospital) and predicted labels provide the same status, which indicates that the status of patient 136 (out of 225 for testing data) is unstable non-critical and the patient should be taken to the emergency room.