esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
Footnote †: The ability to observe the world has seen significant developments in the last few decades, alongside the techniques and methodologies to derive accurate digital replicas of observed environments. Underwater ecosystems present greater challenges and remain largely unexplored, but the need for reliable and up-to-date information motivated the birth of the Interreg Italy-Croatia SUSHI DROP Project (SUstainable fisHeries wth DROnes data Processing). The aim of the project is to map ecosystems for sustainable fishing and to achieve this goal a prototype of an Unmanned Underwater Vehicle (UUV), named Blucy, has been designed and developed. Blucy was deployed during project missions for surveying the benthic zone in deep waters of the Adriatic Sea with non-invasive techniques compared to the use of trawl nets. This article describes the strategies followed, the instruments applied and the challenges to be overcome to obtain an accurately georeferenced underwater survey with the goal of creating a marine digital twin.  
UUV; ROV; AUV; surveying; monitoring; marine; digital twin
# Inverse Approach of Parameter Optimization for Nonlinear Meta-Model Using Finite Element Simulation
## 3 Results and Discussion  
### Results of RMSE  
The finite element simulation results applied with meta-model-based optimization were compared with those for each model. Figure 7 shows the comparison results of the RMSE. Figure 7a quantitatively compares the RMSE among the meta-models with the engineering data in the test results and the parameters initialized as a result of MATLAB CFT. Model 1 showed 0 error because it was compared with itself. Model 2 showed a large error because it had no parameters to change the curve characteristic, so curve fitting was not performed. Models 3-8 showed lower RMSE values than that of Model 2. The advanced meta-model showed the lowest RMSE value among the meta-models. Figure 7b shows the result of the FE simulation performed once after (a). The FE simulation was performed by LS-DYNA, and the error between the test result and the FE simulation result was represented by RMSE. When performing the FE simulation, it was necessary to verify whether the simulation was completed stably. The displacement rate (\(\delta\)) is a dimensionless number obtained by normalizing the maximum value of the test displacement to the maximum value of the simulation displacement, and it indicates the stability of the FE simulation. The simulation was stopped by force if the property meta-model did not  
Figure 5: Finite element model of tensile specimen.  
Figure 6: Schematic of inverse procedure using CFT and FE simulation.  
represent nonlinear displacement at the gage zone of the specimen. The results of the FE simulation showed that Model 2 had the lowest RMSE, whereas Model 6 had the highest RMSE. Furthermore, Models 7-8 showed significant differences in the value of \(\delta\), confirming the unstable simulation. Figure 7c presents the results of the inverse method that repeatedly performed the FE simulation. Models 6-8, which were used as advanced meta-models, showed lower RMSE values than Models 3-5, which were used as typical meta-models and as ASTM E646.  
Figure 7: The results of RMSE: (**a**) RMSE comparison after parameter initialization; (**b**) stability comparison of tensile test and simulation results with RMSE and \(\delta\); (**c**) stability comparison of tensile test and optimized parameter results with RMSE and \(\delta\).  
Figure 8 shows convergence in the meta-model-based optimization process for the engineering data. Excluding Model 3 with a small number of parameters, there was no significant difference in the number of iterations between the typical meta-models and the advanced meta-models. The RMSE significantly decreased for the most part for the inverse method.
## 5 New Challenge: Safety
### Robustness of HMI to Failures  
#### 5.2.1 The New Paradigm of AV HMIs  
The introduction of AVs represents a revolution from a regulatory safety perspective. Indeed, traditional vehicle safety regulations are built around human drivers, focusing on rules like speed limits, signaling, and driver attentiveness. In traditional vehicles, the HMI has always played a critical role in ensuring safety, usability, and minimizing driver distraction. Extensive research on human factors and ergonomics has historically focused on optimizing how information is presented to the driver and how controls are designed to reduce errors and cognitive load [50]. The revolution of AVs is that the safety responsibility is transferred from humans to the HMI and to the Artificial Intelligence. Unfortunately, potential users may still have unrealistic representations of AVs, the role of the driver in automation, or the impacts of full automation on the road transport system [51]. The term _autonomy_ is in this context a little ironic, as it does not refer to separation, but to a consistent connection and relation of mutual influence [52].  
Public perceptions play a crucial role in the wide adoption of AVs [53]. Therefore, a big challenge for HMIs is the huge media impact of accidents involving AVs. For example, the tragic Uber AV accident in Tempe, Arizona, on 18 March 2018, marked the first known pedestrian fatality involving a fully self-driving car. The Volvo XC90 (manufactured by Volvo Cars, Gothenburg, Sweden), operating in AV mode, struck and killed [PERSON], who was crossing the street outside a crosswalk at night while pushing a bicycle. Despite detecting [PERSON] six seconds before impact, the vehicle's sensors misclassified her as an \"unknown object\" and did not initiate emergency braking. The vehicle, traveling at 39 mph, continued on its path, and [PERSON] was struck. The human safety driver, who was distracted by a video on her phone, failed to intervene, even though she had several seconds to act. An investigation by the National Transportation Safety Board (NTSB) revealed multiple issues [54]. The vehicle's perception system, which relied on LIDAR, radar, and cameras, did not accurately classify [PERSON] as a pedestrian. The decision-making algorithm was overly cautious, designed to avoid unnecessary braking for non-hazardous objects, but it failed to respond to a real threat. Furthermore, the safety driver's distraction led to a critical failure in human oversight. The accident highlighted major gaps in both AV technology and the monitoring of human operators. This event prompted calls for stricter regulatory frameworks and improvements in both AI systems and safety protocols for AVs [55].
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 2 Related Work
### Performance Metrics  
Semantic communication, different from traditional communication systems, does not emphasize the perfect recovery of the transmitted message, but rather on the receiver correctly understanding the message in the same way as the transmitter. As a result, performance metrics commonly used in traditional communication systems (e.g., bit error rate and symbol error rate) are no longer suitable for semantic communication. Hence, this paper uses the bilingual evaluation understudy (BLEU) score [34], a metric for evaluation of translation with the explicit ordering (METEOR) score [35], and the semantic similarity score [36], as performance metrics.
# 3D Positioning of Drones through Images
## 2 Related Work  
### Overview of UAV Geolocation Methods  
Cross-view geolocation technology primarily refers to matching and comparing images from different perspectives. In this paper, it specifically refers to matching UAV images with satellite images to locate the position of the UAV image on the satellite image, thereby geolocating the UAV. Traditional cross-view image matching methods, such as SIFT, SURF, and ORB, often struggle to meet requirements due to differences in image sources.  
However, the rapid development of neural networks and their application in image matching has made cross-view geolocation technology feasible for practical positioning. Compared to traditional ground images, satellite and remote sensing images cover larger areas. In early work, [PERSON] [7; 8; 9] matched street-view images with aerial images to obtain location information. [PERSON] et al. [10] introduced neural networks for matching and adopted a Faster R-CNN structure.  
These methods achieved some success, but due to significant feature differences between images from different sources, extracting and matching features from these diverse sources remains a primary challenge in this field. [PERSON] [11] proposed aligning aerial images with satellite images to reduce differences, mainly using polar coordinate transformation.  
To reduce matching errors caused by perspective changes, [PERSON] [12] used generative adversarial networks to convert street-view images into drone images. [PERSON] [13] introduced GeoNet, which models by fully utilizing spatial hierarchies to learn common features in images from different perspectives. These methods employ traditional approaches to narrow the differences between images from various viewpoints, thereby achieving cross-view data matching.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 4 Overview of the Proposed Solution
### Device Authentication Process  
The device authentication process is crucial for maintaining network security, particularly against blended attacks that combine sybil attacks (creating fake identities) with flood attacks (overwhelming the network). This process ensures the legitimacy of devices transmitting messages and prevents unauthorized or fabricated devices from disrupting the network. The verification algorithm employed in this process is outlined below:  
Before transmitting a message, the device generates an Enhanced Message Authentication Tag (E-MAT) using a novel multistep cryptographic process, as follows:  
* Hash Chain Element Retrieval: The device retrieves the next hash value from its precreated hash chain. This value is denoted as \(h^{y+1}\), where y represents the index of the previous hash value used in a message (initially \(y=0\)).
* Timestamp Inclusion: The device combines the retrieved hash value (\(h^{y+1}\)) with the current timestamp (\(t_{s}\)). This ensures the freshness of the message in an effort to prevent replay attacks.
* Hash Function Application: The device applies a cryptographic hash function \[V=h(h^{y+1}\ ||\ t_{s})\] (5) to the combined data, creating a unique message authentication tag specific to the message and the current time.
* The final MAT and the chosen combination of elements form the message payload that is transmitted across the network; for instance, an example payload structure could be \[h^{0},V,(h^{y+1}\ ||\ t_{s}).\]
# Spatial Performance Indicators for Traffic Flow Prediction
## 2 Preliminaries  
### Traffic Networks Graph  
In this study, we consider a traffic network as an undirected graph \(G=(V,E)\), where \(V\) denotes the vertices or nodes of the graph with the number of nodes defined as \(|V|=N\) and \(E\) represents the set of edges that indicate the connection between nodes. The graph is represented by an adjacency matrix \(A\in\mathbb{R}^{N\times N}\). In this study, each node in a traffic network \(G\) represents a traffic sensor that measures traffic data.
## 3 Mathematical Formulation
### Coordinate Transformation  
As previously mentioned, when dealing with non-isotropic and asymmetrical radiation sources, the stand-alone element pattern \(\underline{\tilde{E}}_{c}(\tilde{\theta},\tilde{\varphi})=\{\underline{\tilde {E}}_{x},\underline{\tilde{E}}_{y},\underline{\tilde{E}}_{z}\}\in\mathbb{C}\) must be rotated according to its position under the rotation angles \((\theta_{r},\varphi_{r})^{n}\). The complex far-field element pattern \(\underline{\tilde{E}}_{c}(\tilde{\theta},\tilde{\varphi})\) is computed through a full-wave analysis in Ansys Electronics Desktop HFSS  
Figure 3: Sketch of the spherical coordinate system.  
Figure 2: Flowchart of the directivity computation for a 3D array.  
2022. For this analysis, a patch antenna radiator working in receive mode in the Ka-band is designed and simulated. Element pattern and rotation formulation are presented next.
## 3 Test Results
### The Attenuation of Surface Wave  
#### 3.2.1 The Attenuation of Surface Wave in Open-Ended Pipe Pile  
During vibration transmission, surface waves account for two-thirds of the total energy, and their distance-dependent attenuation coefficient is slower than that of body waves. Surface waves would have adverse effects on adjacent buildings. When the fluctuation frequency of the pile coincides with the inherent frequency of the adjacent buildings, resonance would be induced, causing significant damage to the structures. Therefore, it is essential to clarify the attenuation of surface waves in'strike-induced' vibration. The variation of vertical peak ground acceleration (referred to as VPGA herein) at various distances from the pile and penetration depths is shown in Figure 8. While the penetration depth was less than 300 mm, it was difficult to detect the variation in VPGA at each sensor. When the penetration depth exceeded 300 mm, the VPGA began to change abruptly and the vibration became evident, which was consistent with the stage changes in the increment filling ratio (IFR, which represents the ratio between increment of soil plug and increase of penetration depth in each strike) depicted in Figure 9. When the IFR was in the first stage, each striking caused a relatively large penetration and an increase in the length of the soil plug, which consumed the majority of the striking energy in pile driving and the formation of the soil plug. While the IFR was in the second stage, the soil plug was essentially formed. Although the frictional force between the inner wall of the pile and the soil plugs increased, their relative displacement decreased significantly. As a result, the product of frictional force and relative displacement decreased, i.e., the work of soil plug formation decreased, and the majority of the striking energy was converted into vibration waves that dispersed throughout the surrounding area.  
Figure 8: Variation of VPGA with varying penetration depths.
## 4 Threat Model  
We consider an attacker who can access the data stored in the user device \(d\), for example, by stealing \(d\), and the attacker can obtain the encrypted \(sk_{u}\) from \(d\). In addition, the attacker can obtain the nonce \(n\) from controller \(c\) and the corresponding signature generated by device \(d\) by monitoring the traffic between \(d\) and \(c\). However, we assume that the attacker does not know the user's password \(pass_{u}\). The attacker is also assumed to be computationally bounded to polynomial time, which in particular prevents the attacker from breaking computationally-secure cryptographic primitives (e.g., AES [19]). However, the attacker fully understands the description of all procedures for user authentication and key protection. In practice, such assumptions are often valid (e.g., [20]).  
The attacker's goal is to obtain the user's sign key \(sk_{u}\). Since \(sk_{u}\) is encrypted with key \(k\), which is generated with the user's password \(pass_{u}\) alone, the security of \(sk_{u}\) is only as good as the user's password, \(pass_{u}\), which is inherently susceptible to _offline_ password guessing attacks. That is, if the attacker holds any password-related data, they can then try to iteratively guess the user's password and check whether the guess is correct by trying to calculate the password-related data from the guess. Even though password complexity policies (e.g., requirements for minimum length, symbols, numbers, and upper/lowercase letters) or password strength meters are used to encourage users to choose strong passwords, advanced attackers are still developing methods to efficiently crack such passwords offline using various forms of hybrid password guessing attacks [21]. The basic attack procedure is as follows:  
1. An attacker captures a nonce \(n\) and signed message \(SIGN(sk_{u},id_{u},n)\) by observing the traffic between the device \(d\) and controller \(c\).
2. The attacker also accesses \(d\) and obtains the user's certificate \(cert_{u}\) and encrypted user's sign key \(ENC(KDF(pass_{u}),sk_{u})\); the attacker then extracts the user identity \(id_{u}\) from \(cert_{u}\).
3. With a candidate password \(\hat{p}\), the attacker calculates \(\hat{k}=KDF(\hat{p})\) and then tries to decrypt \(ENC(KDF(pass_{u}),sk_{u})\) with \(\hat{k}\). We use the notation \(\hat{sk}\) to represent the decrypted result. Finally, the attacker checks whether \(SIGN(\hat{sk},id_{u},n)\) is the same as \(SIGN(sk_{u},id_{u},n)\), which means that the candidate password \(\hat{p}\) is correctly guessed. This procedure is repeated with a new candidate password \(\hat{p}\) until \(SIGN(\hat{sk},id_{u},n)\) is the same as \(SIGN(sk_{u},id_{u},n)\).
## 4 Fragility Analysis Based on IDA Method
### Seismic Fragility Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Earthquake Level**} & \multirow{2}{*}{**Structure**} & \multicolumn{5}{c}{**Extreme State (DM: \(\gamma\))**} & \multicolumn{5}{c}{**Extreme State (DM:TP)**} \\ \cline{3-10}  & & **LS1** & **LS2** & **LS3** & **LS4** & **LS1** & **LS2** & **LS3** & **LS4** \\ \hline \multirow{2}{*}{SL-1} & Isolated structure & 26.8\% & 1.6\% & 0 & 0 & 22.0\% & 1.6\% & 0.7\% & 0.5\% \\  & Hybrid isolation structure & 8.8\% & 0.3\% & 0 & 0 & 25.5\% & 1.9\% & 0.6\% & 0.5\% \\ \hline \multirow{2}{*}{SL-2} & Isolated structure & 87.3\% & 30.8\% & 1.5\% & 0.3\% & 73.3\% & 18.3\% & 9.5\% & 6.7\% \\  & Hybrid isolation structure & 71.6\% & 11.3\% & 0.2\% & 0 & 73.2\% & 17.2\% & 8.7\% & 6.1\% \\ \hline \multirow{2}{*}{BDBE} & Isolated structure & 99.7\% & 78.0\% & 14.2\% & 4.4\% & 94.1\% & 46.8\% & 30.4\% & 23.8\% \\  & Hybrid isolation structure & 98.1\% & 52.7\% & 3.9\% & 0.8\% & 91.8\% & 41.1\% & 25.6\% & 19.6\% \\ \hline \multirow{2}{*}{Extreme rare earthquake} & Isolated structure & 100\% & 99.1\% & 65.4\% & 37.9\% & 99.5\% & 80.9\% & 66.5\% & 58.7\% \\  & Hybrid isolation structure & 100\% & 95.8\% & 40.1\% & 17.5\% & 99.1\% & 72.3\% & 55.2\% & 46.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fragility matrices of two structures based on shear strain \(\gamma\) and TP values. Figure 15: Comparison of LS4 limit state fragility curves. Additionally, the CMR values based on the TP value as the damage metric are consistently lower than those based on shear strain, indicating a more conservative assessment of collapse resistance when using the TP value. This finding is consistent with the increased sensitivity of the TP value in capturing tensile-related damage, as previously discussed.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 3 Measurement Campaign
### Harmonic Phasor Estimation
However, if the unknown grid frequency is estimated, then this frequency and the harmonics can be utilized in analytical expression of the window function to estimate the correct magnitude and phase information of the harmonic phasors. Hence, to minimize the effect of spectral leakage, frequency domain interpolation presented in [16] was used to estimate the correct power-grid frequency and then calculate the corrected magnitude and phase of the harmonic phasors. For a given harmonic number \(h\), in case of deviation of the frequency by \(\Delta\omega_{h}\) such that \(\Delta\omega_{h}=\delta f_{r}\), actual frequency (\(\omega_{h}\)) from the DFT frequency bins can be given by:  
\[\omega_{h}=f_{r}(l_{0}+\delta). \tag{8}\]  
where \(f_{r}\) is the frequency resolution given by \(\frac{2\pi}{N}\), \(l_{0}\) is the index of the frequency bin with highest magnitude, \(\omega_{k}=l_{0}f_{r}\), and \(|\delta|\leq\) 0.5. The deviation in the frequency is determined using the ratio between the two highest DFT components given by:  
\[\alpha=\frac{|X_{\mathrm{w}}^{+}[l_{0}+\epsilon]|}{|X_{\mathrm{w}}^{+}[l_{0}]|} \tag{9}\]where \(\epsilon\) could be either 1 or \(-1\) depending on the position of the second highest DFT bin compared with respect to \(l_{0}\). It can be shown that, for \(k=l_{0}+\epsilon\),  
\[\omega_{k}-\omega_{h}=(1-\delta)f_{r}. \tag{10}\]  
Similarly, for \(k=l_{0}\),  
\[\omega_{k}-\omega_{h}=-\delta f_{r}. \tag{11}\]  
Utilizing Equations (7), (10), and (11), Equation (9) can be written as:  
\[\alpha=\frac{|W(\mathrm{e}^{\mathrm{j}(\epsilon-\delta)f_{r}})|}{|W(\mathrm{e }^{-\mathrm{j}\delta f_{r}})|}. \tag{12}\]  
where the frequency response of the window function is dependent on the type of window used. The frequency response of a rectangular window of length \(N\) samples is given by:  
\[W_{\mathrm{rec}}(\mathrm{e}^{\mathrm{j}\omega})=\mathrm{e}^{-\mathrm{j}\omega (N-1)/2}\frac{\sin(\omega N/2)}{\sin(\omega/2)}. \tag{13}\]  
A \(\delta\)-\(\alpha\) look-up table was created using Equation (12) for uniformly spread out values of \(\delta\). A Hann window was used in the process whose frequency frequency response is given by:  
\[W_{\mathrm{hann}}(\mathrm{e}^{\mathrm{j}\omega})=0.5W_{\mathrm{rec}}(\mathrm{ e}^{\mathrm{j}\omega})-0.25W_{\mathrm{rec}}(\mathrm{e}^{\mathrm{j}(\omega- \omega_{1})})-0.25W_{\mathrm{rec}}(\mathrm{e}^{\mathrm{j}(\omega+\omega_{1}) }), \tag{14}\]  
where \(\omega\) is \((\epsilon-\delta)f_{r}\) and \(\omega_{1}\) is \(f_{r}\). In the look-up table, the values of calculated \(\alpha\) was paired with the closest value of \(\delta\) to determine the deviation in the frequency.
## 3 Bacteriophages
### Safety Attributes of Bacteriophages for biocontrol of E. coli O157:H7  
Recent studies have revealed a global resurgence of interest in phages as potential therapeutic agents for clinically relevant bacterial infections, especially those caused by multi-drug-resistant strains [130; 131]. This renewed interest in phages is attributed to some advantages they have over conventional antibiotics. Phages have the ability to selectively target and kill specific bacterial groups while preserving commensal microbiomes, which results in a reduced number of bacteria cells being subjected to selection pressure [132]. This specificity reduces the risk of phage-based biocontrol causing damage to beneficial bacteria or non-target organisms in the environment, thereby enhancing its safety profile. Aside from the fact that phages can replicate at infection sites and penetrate biofilms [133], extensive research has demonstrated that phages are generally non-toxic and harmless for human consumption [134] unlike antibiotics, which can lead to the development of antibiotic-resistant bacteria if mis-used. Furthermore, phages are self-limiting; their population tends to decrease as the amount of their host bacteria decreases [135]. They ultimately degrade in the environment, thereby reducing concerns regarding persistence and accumulation over time. This property helps prevent the overgrowth of phages when they are used as a biocontrol, thereby minimizing their effect on the bacterial community. Finally, bacteriophages can be found in a variety of environments, including water sources, sediments, and the digestive tracts of animals [136], hence isolating them is not a difficult task in terms of their availability.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.3.3 Relative Performance Improvement and Decline Rate  
The relative improvement (decline) rate quantifies the relative advantages (inferiority) in performance between two models and is calculated as follows:  
\[\text{Relative Improvement Rate}=\frac{P_{\text{Improved}}-P_{\text{ baseline}}}{P_{\text{baseline}}}\times 100\% \tag{22}\]  
\[\text{Relative Decline Rate}=\frac{P_{\text{original}}-P_{\text{perturbed}}}{P_{ \text{original}}}\times 100\% \tag{23}\]  
In summary, the evaluation metrics of accuracy, F1-Score, Standard Deviation, Confidence Intervals, Improvement Rate, and Decline Rate provide a comprehensive framework to assess the performance, robustness, and generalization of the LePB-SA4 RE model.