esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Data Preparation and Analysis of Vulnerability Factors  
Seven thematic maps were established for the calculation of the DRASTIC index:  
**Depth to water**  
The depth to water is an important parameter in the DRASTIC model. For the Tebessa-Morsott aquifer, this parameter was determined during the piezometric surveys from May 2010. The map of this parameter (Figure 9a) shows the presence of six (6) classes. The lowest values are located near the west (north of the Hammamet city) and southwestern borders (Bekkaria city) and the central zone of the study area, while the higher values are located in the southern (north of the Tebassa city) and northern zones of the study area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Groundwater level & 1.0–78 & m \\ Number of layers & 1 & U \\ Aquifer thickness & 150–300 & m \\ Hydraulic conductivity & \(2\times 10^{-5}\)-\(3\times 10^{-4}\) & m/s \\ Specific storage & 8–22 & \% \\ Recharge & 84,354 & m\({}^{3}\)/day \\ Withdrawal (wells) & 36,986 & m\({}^{3}\)/day \\ Stream leakage & 195,320 & m\({}^{3}\)/day \\ Boundary limits & 147,953 & m\({}^{3}\)/day \\ \hline \hline \end{tabular}
\end{table}
Table 2: The aquifer parameters used for model setup and calibration.  
Figure 5: (**a**) Groundwater flow map for steady-state simulation. (**b**) Water budgets for steady-state simulation (May 2010).  
Figure 6: Relationship between calculated and observed heads in meters (May 2010).  
Figure 8: Groundwater flow map for transient simulation in the case of the second scenario.  
Figure 7: (a) Groundwater flow map for transient simulation in the case of the first scenario. (b) Water budgets for transient simulation in the case of the first scenario.
## 11 Hardware and Testbeds  
In order to have the capability of handling multimedia applications in WMSN, the ability to support their requirements and challenges, and to examine and test the proposed protocols and algorithms developed for WMSN, the underlying enabling technology and platforms are required to be more efficient and cover the drawbacks of the existing hardware designed for WSN for detecting scalar events. Therefore, many works have been presented in the literature to modify the existing platform (hardware) or present new hardware implementation and testbeds. These proposed platform and testbeds are more powerful and have more potential to process and handle multimedia traffic efficiently in terms of processing power, memory, data rate, power consumption, and communication capabilities. The work in [91] described the different applications of WMSN and some of the devices and testbeds used in WMSN, but in this section, we introduce most currently off-the-shelf hardware as well as available research prototypes and show their specifications and performance comparing to each other. In addition, we categorize the existing platforms and research prototypes according to their capabilities and functionalities in WMSN as shown in Figure 4 below.
## 7 Compliance with Maritime Regulations
### Data Governance and Security  
As autonomous vessels grow increasingly data-driven, ensuring confidentiality, integrity, and the lawful use of data becomes mission-critical. AI agents play a central role in enforcing both cybersecurity protocols and data governance frameworks, acting as real-time guardians of digital integrity.  
AI agents manage onboard data streams from sensors, navigation systems, and communications. They classify, encrypt, and store these data according to predefined governance policies aligned with GDPR, IMO cybersecurity guidelines, and internal audit requirements [131; 132].  
For example, an AI agent may detect that engine performance data contain identifiable crew behavior patterns. It automatically anonymizes the dataset before storing it, ensuring GDPR compliance without manual preprocessing.  
Agents also monitor data flows for integrity violations, detecting tampering, loss, or corruption using hash validation, digital signatures, or anomaly detection techniques [133; 134; 135; 136; 137; 138; 139; 140; 141]. These tools protect both compliance logs and safety-critical operational records.  
On the cybersecurity front, AI agents act as intelligent firewalls and threat hunters. They analyze network traffic in real time, identifying abnormal patterns (e.g., lateral movement, unauthorized access attempts) and initiating automated responses, such as isolation of compromised subsystems or dynamic key rotation [135].  
In one scenario, a spike in outbound traffic from the navigation subsystem triggers an alert. The AI agent verifies the behavior against historical baselines, detects a suspected breach, severs the link, and launches a root cause analysis, all within seconds.  
Crucially, these systems are adaptive and self-updating. Behavioral learning enables them to respond to new forms of attack or governance breaches that static rules would miss [136].  
By embedding governance and security intelligence into every layer of such autonomous operations, AI agents ensure that vessels remain not only compliant and traceable but also resilient against digital threats in a globally connected maritime ecosystem.
# Influence of a Helium-Nitrogen RF Plasma Jet on Onion Seed Germination
In addition to changes in the cell itself, plasma can also reduce the bacterial bearing rate of seeds or change the seed coat structure, increasing the permeability of the seed's coat and stimulating seed germination [17; 18; 19; 20; 21; 22]. Onion (_Allium cepa_ L.) is one of the most commonly consumed vegetables worldwide. However, it has specific climate and soil requirements that may impede cultivation, which subsequently leads to poor establishment and yield reduction. It reacts very strongly to the length of the day (in short-day conditions, onion varieties produce only leaves instead of bulbs). Additionally, with its shallow root system, onion is sensitive to water shortage in soil at every stage of growth. However, excessive rainfall at the end of the growing season can delay the onion ripening and deteriorate the quality and storage stability of the product. Furthermore, onion requires very carefully prepared field conditions, especially when cultivated from seeds sown directly into soil [23; 24; 25; 26]. Partial reports focused on helium-air plasma impact on onion were presented during a conference [27; 28]. The highest germination rate of 80% was achieved for 240 s treatment, in comparison to 52% for the control. The aim of the current study was to investigate the effects of radiofrequency plasma treatment on physical and morphological properties, and further improvement of onion seeds' germination rate using a different gas mixture: He + N\({}_{2}\).
## 4 Experiments and Performance Evaluation
### Dataset  
In this experiment, the VoiceBank+DEMAND dataset is employed for both training and evaluation of the proposed model. Constructed by [PERSON] et al., this dataset is widely used as a standard benchmark for speech enhancement, and most lightweight models reported in recent studies are also evaluated on this corpus. Therefore, using this dataset ensures a fair and consistent performance comparison. Furthermore, to simulate real-world noise conditions in hearing aids, we introduce custom noisy samples by mixing the clean VoiceBank utterances with actual background noise recorded from hearing aid environments. This extended dataset allows for a practical assessment of the model's noise suppression capability in deployment scenarios.
## 3 Results and Discussion
### Microscopic Pore Characteristics
To investigate the evolutionary patterns of pore sizes in the grouting reinforcement body, the variations in \(T_{2}\) spectral areas and pore proportions of micropores, mesopores, and macropores under different freeze-thaw cycles are plotted and analyzed, as depicted in Figure 16. At 0 freeze-thaw cycles, the \(T_{2}\) spectral areas for micropores, mesopores, and macropores are 9931.66 a.u., 3053.76 a.u., and 220.48 a.u., respectively. The micropores exhibit the largest proportion at 75.21%, followed by mesopores at 23.12%, and macropores constituting the smallest fraction at 1.67%, suggesting the presence of initial cracks in the grouting reinforcement body, predominantly microcracks and micropores. With the increase in freeze-thaw cycles, the proportions of micropores, mesopores, and macropores all increase, but the evolution of these three pore types shows differences. While the number of micropores continues to rise, their proportion in the total \(T_{2}\) spectral area gradually decreases. After 30 freeze-thaw cycles, the proportion of micropores reached 53.73%, signifying an increase in micropores alongside their transition into mesopores and macropores. At 20, 25, and 30 freeze-thaw cycles, the mesopore proportions were 30.91%, 32.39%, and 32.22%, respectively, exhibiting an initial increase followed by a slight decline. This indicates that mesopores increase consistently during the initial stages of freeze-thaw cycles. This growth is attributed partly to the increase in initial mesopores and partly to the transformation of micropores into mesopores. In the later stages of the freeze-thaw \begin{table}
\begin{tabular}{c c c c c} \hline
**Freeze-Thaw Cycles** & **Micropore** & **Mesopore** & **Macropore** & **Total \(T_{2}\) Spectral Area** \\ \hline
0 & 9931.66 & 3053.76 & 220.48 & 13,205.90 \\ \hline
5 & 10,553.34 & 3454.50 & 324.23 & 14,332.07 \\ \hline
10 & 11,098.72 & 4522.72 & 597.62 & 16,219.06 \\ \hline
15 & 12,323.45 & 5610.13 & 1002.85 & 18,936.43 \\ \hline
20 & 12,529.08 & 6304.11 & 1557.66 & 20,390.85 \\ \hline
25 & 13,089.59 & 8455.29 & 2400.37 & 23,945.25 \\ \hline
30 & 13,096.60 & 9293.20 & 3660.87 & 26,050.67 \\ \hline \end{tabular}
\end{table} 
Table 4: \(T_{2}\) spectral area variation characteristics of grouting reinforcement body. cycle, the proportion of mesopores first increases gradually and then begins to decline. This decline is attributed to intensified freeze-thaw damage, which accelerates the transition of mesopores to macropores, thereby reducing the proportion of mesopores. The proportion of macropores increases with the number of freeze-thaw cycles, exhibiting slow growth during the early stages (0-15 cycles) and rapid growth in the later stages (20-30 cycles). After 30 freeze-thaw cycles, the proportion of macropores reached 14.05%.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 3 Solution Design
### Workflow for UE Environment Perception
The server compares the wireless signal features uploaded by the UE with the signal features already collected in the database, using a matching algorithm to find the most similar signal feature sets. This process ensures that the system can accurately identify the environmental characteristics based on the signal data, thus providing support for environmental sensing calculations. 5. Signal analysis and processing. After environmental sensing matching, the environmental sensing server performs further analysis and processing on the matched signal feature data. Using signal processing algorithms, the system extracts key environmental information from the matched data and optimizes it. This stage of processing includes not only signal strength matching but also the application of signal attenuation models, noise filtering, etc. This process ensures that the most representative environmental information is extracted from a large volume of signal data for subsequent analysis. 6. Environmental sensing response. After completing signal matching and analysis processing, the environmental sensing server returns the calculated results as a response to the UE. This response includes the environmental sensing information of UE's current environment, such as the trend of signal strength changes, environmental interference levels, and the number of available signal sources. The specific environmental sensing data will be customized according to the system design to meet the needs of different scenarios. 7. Environmental sensing update. To maintain the timeliness and accuracy of the data, UE can periodically update its collected environmental signal feature data based on the requirements of the environmental sensing server. The environmental sensing update mechanism ensures that the system maintains high data processing efficiency in a dynamically changing environment and can adapt to new network conditions or user demands. UE actively collects new signal features based on trigger conditions or preset time intervals and uploads them to the environmental sensing server for analysis and updating. An overview of the UE environmental sensing workflow is shown in Figure 3:
# Nanoplasmonic Sensor Based on Surface Plasmon-Coupled Emission: Review
## 1 Introduction  
Surface plasmon resonance (SPR) is a unique phenomenon due to its charge density oscillation at the interface of metal and dielectric medium. Surface plasmon-coupled emission (SPCE) is a versatile [1] and powerful technique based on fluorescence and plasmonic properties of the nanostructure, or nanoplasmonic effect, of metal nanoparticles. The sensors, based on fluorescence, have extensive application for numerous assays in the biotechnology, medical diagnostics, and biological research. Fluorescence detection is a highly sensitive technique, and the sensitivity must be enhanced for detection of particular molecular in small size [2].  
The fluorescence can be controlled through a semitransparent thin metal film and a thin layer with fluorescence property. Fluorophores in an excited state can generate surface plasmon. In turn, surface plasmon can excite the fluorophores, such as nanomaterial and quantum dots [3]. This technique is based on highly sensitive sensors, probes, and new devices for the detection and interaction of molecules with fluorophores. On the other hand, when the molecules bind to fluorophores, there will be a shift in the energy in the fluorescence spectrum or the surface plasmon wave. This is the basis of detection of chemical low-concentrations and investigations of biomolecular interaction. In this article, the plasmonic and fluorescence properties of nanomaterial, SPR theory, the concept of SPCE, and the sensor application of SPCE using nanomaterial are presented.
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 4 Model Development and Training
### Model Development  
After evaluating the correlation between each sensor (air humidity, soil water content, and light intensity) readings and the pixel values of thermal images, a separate model for each of the mentioned sensors was implemented. In this paper, CNN was used for the proposed sensor forecast model development due to its known capabilities in both the image data classification and regression tasks discussed in the literature review. CNNs  
Figure 4: Different sensor correlations with thermal image pixels during all data gathering period. Correlation heatmap between pixel values and **(a)** air temperature sensor data; **(b)** air humidity sensor data; **(c)** soil water content sensor data; **(d)** light intensity sensor data.  
are useful for automatically detecting and learning spatial hierarchies of features from images, which was useful for the interpretation of thermal images where temperature patterns and other parameters can be indicative of plant health or other surrounding environmental conditions.  
The proposed model was implemented using \"Tensorflow\" library [25]. The model contains convolutional layers to extract high-level features from the thermal images, pooling layers to reduce the dimensionality of the data, and fully connected layers to interpret these features. While a CNN is often used to process images, in this case, it was used to forecast a single sensor measurement for the same datetime used in the model input. Therefore, it was required to include a dense layer with a single neuron as model output--single sensor measurement. The dataset was divided so that 60% was used for the training phase and an additional 20% of the data were designated for validation purposes, used for hyperparameter tuning, in total using 80% of the data available. The remaining 20% of the dataset were used for model testing.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 3 MAC Address Randomization
### Not as Random as It Appears  
Recent studies have delved into various aspects of MAC address randomization, including its effectiveness and techniques for resolving to a global address. The research [34] focused on demonstrating that MAC address randomization alone does not guarantee privacy. The study revealed that when combined with predictable sequence numbers, the PR information elements (IEs) in MAC addresses create a device fingerprint that allows tracking of the device, even in the presence of MAC randomization. The report indicated that approximately 50% of devices could be tracked for up to 20 min. In addition, the authors proposed several attack techniques, including fake AP attacks, karma attacks, and hotspot 2.0 honeypot attacks. In the karma attack, a fake AP broadcasts a known SSID, anticipating that the victim device, while scanning using a random MAC address, will associate with the network using its global MAC address (a behavior observed in many devices). In the hotspot 2.0 [35] attack, the fake AP is configured to provide minimal information in the beacon frames, prompting the STA (station) to send an access network query protocol (ANQP) request. When the STA transmits the ANQP request, it uses a unique MAC address, and the frame also contains a dialog token field that can be predicted.  
The research outlined in [36] sheds light on the limitations of MAC address randomization. While using a random MAC address can obscure the true identity of a device, it does not guarantee protection against tracking. The authors identified two specific attacks that exploit vulnerabilities in MAC randomization: content-based attacks and timing-based attacks. Content-based attacks exploit the predictability of certain fields within the PR frame, such as the sequence number, OFDM scrambler seed, and information elements (IEs). By analyzing these elements, adversaries can overcome MAC randomization and track the device. On the other hand, timing-based attacks rely on the periodicity of the PR frames. Even though the device changes its MAC address, the intervals at which the PR frames are generated, as well as the sequence in which the device scans the channel, contain sufficient information to circumvent MAC randomization and facilitate tracking. The research presented in [37] introduces a machine learning technique called Cappucino, based on self-supervised learning. Unlike other methods that aim to resolve random MAC addresses to their original values, Cappucino focuses on associating PR frames with their respective source devices for statistical analysis purposes, such as device counting and trajectory reconstruction. The reported results indicate that Cappucino achieves a V-measure score higher than 85, showcasing its effectiveness in performing these tasks.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 1 Introduction
The research results indicate that the frequency of this structure can be better applied to low-frequency regions. [PERSON] et al. [28] validated the characteristic of adjusting the bandgap of supercell structure by designing a supercell phononic crystal plate and obtained a wide and low-frequency bandgap. [PERSON] et al. [29] designed a 45\({}^{\circ}\) rotating supercell and achieved band gap optimization. In addition, the idea of realizing multi-resonant modes or achieving the best results by changing the parameters is also applied to the control of electromagnetic waves [30; 31]. The supercell method provides a possibility for studying the mechanism of widening bandgap using periodic gradient arrangement. In this paper, the gradient idea is applied to the design of radial seismic metamaterial, and its low-frequency broadband characteristics and mechanism are analyzed and studied with supercell method. Firstly, the dispersion curve and attenuation spectrum of the radial gradient seismic metamaterial (RGSM) single-cell structure are calculated using finite element software, and the mode at the boundary of the band gap is analyzed. The influence of geometric parameters and material parameters on the band gap is further studied. The optimized supercell seismic metamaterial structure is designed through structure and parameter optimization, and its three-dimensional frequency response and displacement field diagram are calculated for analysis and verification. Finally, the attenuation effect of a radial gradient supercell seismic metamaterial on seismic surface waves is studied using a three-dimensional model and is verified in time domain.
## 2 Related Work
### SOS Algorithm
The \(BF\) value is generated using Equation (10):  
\[BF=1+round[rand(0,1)] \tag{10}\]  
As stated earlier, the newly generated solutions are accepted as a replacement for the existing solution if, and only if, they are better. In other words, new solutions are rejected if the existing solutions are better. Equations (11) and (12) incorporate this:  
\[x_{inew}=x_{i}+rand(0,1)\times(x_{best}-x_{mutual}\times BF_{1})\;if\;f(x_{inew })<f(x_{i}\;) \tag{11}\]  
\[x_{jnew}=x_{j}+rand(0,1)\times(x_{best}-x_{mutual}\times BF_{2})\;if\;f(x_{inew })<f(x_{j}\;) \tag{12}\]  
In simulating the commensalism phase, an organism \(x_{j}\) is randomly selected for interaction with organism \(x_{i}\) exhibiting the characteristic of the commensalism symbiosis relationship where only one of the two organisms \(x_{i}\) derives a benefit from the relationship. This relationship is simulated using Equation (13):  
\[x_{inew}=x_{i}+rand(-1,1)\times\big{(}x_{best}-x_{j}\;\big{)}\;if\;f(x_{inew})> f(x_{i}\;) \tag{13}\]  
Thus, only the benefiting organisms generate a new solution, as reflected by Equation (10). The new solution is accepted only if it is better, in terms of fitness, than the previous solution before the interaction. The parasitism phase is the last phase, which is simulated using Equation (14). In simulating the parasitism symbiotic relationship in the SOS algorithm, a duplicate copy of the \(x_{i}\) organism is created as a parasite vector with some of its selected dimensions modified using a random number. An organism \(x_{j}\) is then randomly selected from the ecosystem to play host to the parasite vector \(x_{parasite}\). If the fitness value of \(x_{parasite}\) is better than that of \(x_{j}\), then \(x_{parasite}\) replaces \(x_{j}\) in the ecosystem. If the opposite happens, and \(x_{j}\) builds immunity against \(x_{parasite}\), then \(x_{parasite}\) is removed from the ecosystem:  
\[x_{parasite}=rand(0,1)\times(UB-LB\;)+LB \tag{14}\]