esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Attack Scenarios on Real-life Maritime Logistics and Supply Chain Services
### Intrusion Scenario on the Oil Monitoring System of the LNG Transport Service  
Liquefied natural gas (LNG) is natural gas, predominantly methane, CH4 that has been converted to liquid form for ease of storage or transport. It is odorless, colorless, non-toxic and non-corrosive. Hazards include flammability after vaporization into a gaseous state, freezing and asphyxia. Considering that a tanker contains more than one hundred thousand cubic meters of LNG, it represents a potential explosive hazard comparable to a nuclear bomb.  
A terrorist group seeks to cause significant human casualties, economic losses and environmental damages by attacking the LNG land-based facilities of a port or an LNG tanker. For example, a possible cyber-attack to LNG land-based infrastructure may cause catastrophic fires either inside the port or even nearby populated areas and an LNG tanker attack may result in a major spill that could pose a hazard to coastal communities along the tanker's route. Furthermore, a physical attack on the LNG storage facilities, either in the form of bombing or by impacting a vehicle in the facility, may cause an explosion that leads to a widespread fire jeopardizing people's lives within the port.  
Therefore, we assume that a terrorist group commits a cyber-attack during LNG shipping as follows. A shipping company receives an e-mail purportedly coming from the IT company that supports and maintains its ICT infrastructure asking them to download and install a software that improves the performance of their systems. In this way, the terrorists successfully download and execute arbitrary code on the victims' systems to gain access to them.  
Accordingly, the terrorists can leverage their access to go deep into the network by exploiting vulnerabilities in the oil company's monitoring software that provides remote tank monitoring, asset tracking, and data reporting services to break into the system. Therefore, they can empty the oil tank without being detected.
## 3 Edge Computing and Related Paradigms
### Multi-Access Edge Computing (MEC)  
Multi-access edge computing (MEC) is another paradigm that extends cloud computing capabilities to the edge of the network. The concept of MEC was introduced by the European Telecommunications Standards Institute (ETSI) Mobile Edge Computing Industry Specification Group (MEC iSG) to encompass the benefits of various access technologies such as 4G, 5G, Wi-Fi, and fixed access [73].  
MEC is vital in optimizing mobile resources by hosting computationally intensive applications, processing extensive data locally before transmitting it to the cloud, and offering cloud computing capabilities within the radio access network (RAN) close to users citesanti2021. By leveraging computing, communication, and caching (3C) resources at the network edge, MEC is positioned as a critical enabler for next-generation networks. This architecture reduces latency, saves energy, and enhances the network's bandwidth efficiency [74].  
MEC also enables the deployment of context-aware services and supports intelligent computation offloading, which is essential for handling the computational tasks of mobile devices by offloading them to MEC servers [75; 76]. Integrating MEC with emerging technologies like 5G and the internet of things further enhances its capabilities, making it a critical component for applications such as mobile augmented reality (MAR) [77]. Additionally, MEC facilitates the implementation of AI-driven systems with UAV assistance in dynamic environments, addressing challenges through efficient resource utilization at the network edge [78].
### Review on Groundwater Level Prediction Using Machine Learning Technique  
[PERSON] et al. [6] proposed a prediction model for identifying the nitrate level of aquatic factors by statistical and machine learning (ML) techniques and found that random forest (RF) produces better performance with a maximum R-value. [PERSON] et al. [7] studied the impact of aquifer exhaustion by monitoring groundwater extraction using a multivariate regression method and RF with a good \(R2\) value. [PERSON] et al. [8] predicted GWL using geoelectric parameters and explored the process by an artificial neural network (ANN) using RMSE and the regression coefficient. [PERSON] et al. [9] compared the various ML techniques and found that support-vector regression (SVR) produces good accuracy for measuring the availability of groundwater to mitigate the sustainability and scarcity problem. [PERSON] et al. [10] hybridized a neural network with the Whale algorithm for hyperparameter optimization using an adaptive neuro-fuzzy interface system (ANFIS).  
Even though machine learning techniques show better prediction accuracy, they have the limitation of stimulating a reduced amount of data. As the data increases, there is a chance for less prediction accuracy. Additionally, some machine learning techniques lack in identifying the best attributes to support the prediction process, where feature selection takes a major role. Furthermore, comparatively, the computation time is high as there is an increase in the training process. Thus, to overcome these issues, researchers have directed their attention towards deep learning techniques in groundwater level prediction.
# Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review
## 2 Lycopene Nutraceutical Properties and Its Effects on Human Health
### Fluorescent Lighting  
[PERSON] et al. [41] explored the effects of radiation on vegetable juice with fluorescent light. For the investigation, they used two trials, and each trial had 2 g of juice. One sample was treated with fluorescent light at a constant intensity at 230 ft-c and 4 \({}^{\circ}\)C while the other sample remained isolated from illumination. The samples initially contained 3.0 \(\upmu\)g/g of \(\alpha\)-carotene, 8.2 \(\upmu\)g/g of \(\beta\)-carotene, and 62.9 \(\upmu\)g/g of lycopene. They were exposed to radiation for four days and showed a reduction in antioxidants: 25% for lycopene, and 75% for \(\alpha\)-carotene and \(\beta\)-carotene. The authors concluded that the lycopene carbon rings at the ends of its chemical structure protect it from light degradation.  
In another experiment, [PERSON] et al. [42] reported lycopene's behavior at different temperatures (50, 100, and 150 \({}^{\circ}\)C) and radiated with fluorescent light at 25 \({}^{\circ}\)C for different periods. They observed that at 50 \({}^{\circ}\)C, isomerization predominated in the first 9 h and then degradation started. In contrast, for conditions between 100 and 150 \({}^{\circ}\)C, degradation proceeded faster than isomerization.
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme  
As is described above, on the one hand, DRADG dynamically decides the replica number for every data message through sensing the distance between the node, which generates the message, and the sink nodes. On the other, DRADG computes the delivery probability of each mobile node, and forwards data messages to nodes with higher delivery probability. Therefore, DRADG protocol can reach high data gathering performance. In this section, we will describe the proposed DRADG protocol in detail.
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 4 Discussion
### Trombe Wall Cavity as a Cultivation Space  
The plants effectively reduced peak temperatures, but the TW space remains suitable for cultivation only during specific seasons. Optimal conditions [88] occur from spring to mid-summer (March-August), with manageable temperature ranges (16-27 \({}^{\circ}\)C). In late autumn and early winter (October-December), low solar angles cause overheating, while winter nights often drop below 10 \({}^{\circ}\)C, hindering plant survival.  
A mixed-use strategy is recommended, with the TW cavity serving as a passive heat-capturing device in cooler months and a plant cultivation space in warmer months. Winter cultivation is feasible, but less efficient and productive due to maintenance demands. Alternative geometries, such as evenly shaped TWs with improved ventilation or larger Trombe walls with reduced temperatures but higher thermal mass, could enhance functionality. Modifications like increased water mass placed closer to the building or alternative plant types with lower albedo and greater heat resistance may further optimize performance (Figure 12).  
Figure 11: Trombe wall air temperatures correlated with the outside temperatures during typical sunny days before (BY) and during the experiment (EY).  
The current experimental setup favors short-cycle plants like bushy annuals, leafy greens, or succulents, which are more adaptable to the cavity's dynamic environment [69]. Long-cycle plants requiring stable conditions are less suitable.  
The results of this study are limited to the climatic conditions in our location and might not be reproducible in other places. For successful plant cultivation, the Trombe wall's interior temperature must remain above freezing year-round. Interior Trombe wall temperatures are greatly influenced by their specific configurations, including glazing, orientation, geometry, and materials, so generally, all Trombe walls whose air temperatures drop below zero (or 10 \({}^{\circ}\)C if used to cultivate annual plants), would be unfeasible. At the study site (32\({}^{\circ}\) latitude, subtropical climate), conditions were marginally favorable for conventional Trombe wall-based cultivation. In colder climates, plant cultivation in Trombe walls would generally be impractical without interventions such as insulating night covers (e.g., greenhouse-style covers) [89] or supplemental heat sources. These measures, however, may compromise their cost-effectiveness and sustainability.  
Solar radiation is another critical factor. While the study's chosen plant variety tolerated low light levels, locations with fewer than 6-8 h of direct sunlight per day may not support many plants, particularly those that flower or bear fruit. Conversely, excessively hot, arid climates could present challenges due to high internal temperatures (above 30-35 \({}^{\circ}\)C), though additional water introduced for cultivation might enhance conditions in such environments [90]. This hypothesis requires further experimental validation.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 5 Discussion  
### The Significance of PSR Image Denoising  
Clear PSR images are significant for both scientific exploration and mission operations. The advanced denoising techniques we provide can effectively enhance the clarity of these images, enabling researchers to identify obstacles and terrain hazards in poorly lit areas. This directly contributes to the safety of lunar PSR missions and reduces uncertainties in rover and human traversal plans. Additionally, it helps scientists identify surface-exposed water ice in PSRs [52], providing crucial data for studying resource distribution on the Moon. Therefore, by offering better denoising techniques for PSR images captured by LROC NAC, we establish a more reliable observational foundation for formulating lunar exploration strategies, optimizing lunar traversal paths, and prioritizing scientific sampling sites for the next phase of human lunar exploration.
# Analysis of the Development Status of eLoran Time Service System in China
## 1 Introduction
As IoT based services are becoming inevitable and getting deployed at very high rate, it would give more visibility. The inherent vulnerability of GNSS systems will limit the development of the Internet of Things. The combination of GNSS and eLoran is a very good complementary solution that will further promote and expand the application scenarios of the Internet of Things [15; 16].
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 2 The Langevin Equation as a Thermal Bath
### The Langevin Equation in Classical Molecular Dynamics Simulations  
In its basic form, the Langevin equation of motion writes:  
\[m\ddot{x}=-\frac{dV}{dx}-m\gamma\dot{x}+R(t) \tag{4}\]where \(R(t)\) is a random force with zero mean (usually taken from a Gaussian distribution) and characterized by a white noise correlation function:  
\[\langle R(t)R(t+\tau)\rangle=2\gamma mk_{B}T\delta(\tau) \tag{5}\]  
The random and friction forces model the effect of a thermal bath (see Figure 1) and ensure the canonical statistics, including the energy equipartition theorem and the fluctuation-dissipation theorem as stated in Section 2.1. The constant \(\gamma\) is the strength of the coupling of the thermal bath with the simulated system. Its inverse \(1/\gamma\) provides an indication of the relaxation time of the system, that is, the time it takes for the random force and the dissipation to equilibrate.  
Used as a thermostat in molecular dynamics simulations, the Langevin Equation (4) provides, for any \(\gamma\), an exact sampling of the canonical Boltzmann distribution. More precisely, it can be proved that the probability distribution converges exponentially to the invariant one starting from a wide range of different initial conditions [22], thus avoiding temperature oscillations or other spurious effects that other thermostats, such as Nose-Hoover, can generate. Dynamical properties, on the other hand, are affected by the coupling with the bath and depend on the value of \(\gamma\). In particular, the damping term generally broadens the peaks corresponding to modes in the vibrational spectra over a typical width \(\gamma\).
## 3 Measurements  
To verify the operation of the full system, the output power, power transfer efficiency, and port impedances were measured. The measurement configuration is shown in Figure 12. A Keysight 33220A function generator was used as the CW signal source. It was set to the continuous sine wave output mode. The measurements were carried out using a Keysight MSO9254A oscilloscope by connecting Keysight N2904A differential voltage probes and Keysight 1147B current probes to the input port and output port of the matched acoustic-electric channel. To measure the exact power output capacity of the system, a Keysight N3301A DC electronic load was used. It was set to the constant voltage mode. The function generator, oscilloscope and DC electronic load were all automatically controlled by a MATLAB (MathWorks Inc., Natick, MA) script on a personal computer (PC) to guarantee their synchronization. The fully automated measurements are faster, more objective and more precise than previous methods [15; 16; 17; 18]. This is because the entire process does not require manual intervention and all measurement results are automatically read by the MATLAB script. Furthermore, the impedance mismatch caused by the error of the circuits, which was ignored in [15; 16; 17; 18], was taken into account during the measurements. A photo of the measurement setup is presented in Figure 13.  
Figure 11: (**a**) One of the transducers; (**b**) TX matching circuit; (**c**) RX matching circuit and AC-DC converter. Figure 12: System measurement configuration. The output frequency of the function generator was scanned from 942.5 kHz to 944.9 kHz to find the most efficient working frequency of the system. Furthermore, at each frequency, the minimum output amplitude of the function generator, ensuring that the AC-DC converter operated stably, was determined using the bisection method. The automated measurement algorithm is given in Algorithm 1. Note that before carrying out the measurements, the time delay between the current and voltage probes has to be calibrated by using a Keysight U1880A deskew fixture. In order to make accurate measurements based on the voltage across and current through fast switching devices, it is important that any skew between the voltage probe and current probe signal paths is corrected [28]. After finishing the measurements, the parameters characterizing performance of the system can be either directly known or indirectly calculated from the data recorded during the measurements. These parameters are plotted in Figure 14.  
Figure 14: Measurement results of the system: (**a**) Input apparent power and output active power of the matched channel and DC output power of the system; (**b**) Power transfer efficiency of the matched channel and system; (**c**) Input resistance and reactance of the matched channel; (**d**) Input resistance and reactance of the AC-DC converter. Figure 13: Photo of the system measurement setup.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 5 Experimentation Including the Results and Discussion
### Developing the Base Model  
As shown in Figure 3, we start with developing the base model (shown in Figure 4a), which is used for comparison. After data preprocessing, the data is split into training/testing (80%/20%, respectively) and, to determine if concept drift could be detected in this dataset, UWF-ZeekDataFall22, some of the testing data was kept aside to test as new batch data. The new batches would be used to see if a concept drift was occurring and if this could be detected.  
The model was then trained with the training data using Random Forest, and the testing was performed with part of the testing data. The testing data was randomly selected from the 20% testing data and inserted in batches after inserting some attack data. The idea when generating the batches was to simulate the early detection of anomalies, thatis, to make the model more sensitive, hence triggering incremental retraining as early as possible when a drift is detected. Batch sizes affect how quickly the model adapts. Smaller, more frequent batches allow for more responsive adaptation, hence smaller batch sizes were used.  
To generate the batches, a Fibonacci sequence was generated and normalized to percentages. These percentages would determine the proportion of \"used\" (data the model was trained on) to \"unused\" (new data the model has not seen) data in each batch. Also added to the test data were attacks, a randomly picked percentage from the different attack groups to simulate the sporadic nature of attacks. Since the number of attacks in the attack groups are different, the batches are also different in count.  
The model's performance was evaluated through multiple metrics, as presented in Table 10, which presents the base model's accuracy, precision, recall, and F1-score for both the training as well as testing datasets. Table 10 also presents the total run times for training as well as testing. The nearly identical values across training and testing indicate that the model generalizes well and is not overfitting. These results provide a baseline against which a drift can be detected.
## 2 Compact Model Development
### Parameter Extraction Methodology  
The extraction method has been developed in accordance with the standard extraction flow used for bipolar transistor technologies. To begin the extraction process, an initialization of the physical and technological parameters is necessary. Next, a sequence of measurements and parameter extractions are followed that leads to the extractions of the intrinsic and extrinsic parameters as shown in the flowchart in Figure 3.  
The measurement of one-port \(S\)-parameters of the test structures are first measured to extract the electrical equivalent circuit elements of the RF pads and access lines. It is important to note that the design of the test structures can vary from one technology to another. Therefore, the de-embedding method must be adapted to the available test structures. Subsequently, one-port \(S\)-parameter measurements at various biases followed by a de-embedding process allow accessing the intrinsic \(S\)-parameters of the UTC-PDs. By converting these \(S\)-parameters to \(Y\) or \(Z\)-parameters, the elements of the UTC-PD equivalent electrical circuit can be extracted. The parameters of the junction, \(C_{j0}\), \(V_{j}\) and \(M_{mr}\) can be extracted by taking the logarithm on both sides of Equation (6) in order to linearize it and then by performing an iteration on \(V_{j}\), at low reverse voltage to obtain the best linear regression. Once \(C_{j0}\) is determined, the set of parameters: \(W_{SCR0}\), \(E_{max}(v_{d}=0)\) and \(R_{N^{-}}(0)\) can be calculated. An optimization on the \(V_{PT}\) value, at high voltage, allows to determine \(C_{j0}\). After determining the junction parameters, forward and reverse current measurements without illumination are used to extract the DC characteristics of the UTC-PD junction as well as the reverse current parameters (band-to-band tunnel current and trap assisted tunnel current parameters) [17,20].  
Finally, static photocurrent and bandwidth measurements are exploited to determine the optical parameters such as responsivity, \(R\), and the transit times. The expression of the photo current includes two transit times [14]: the transit time in the absorber, \(\tau_{a}\), and the transit time in the collector, \(\tau_{c}\). The calculation of \(\tau_{c}\) via the electric field, \(E_{max}(v_{d})\), at the collector input, leaves only one transit time, \(\tau_{a}\), to be extracted, which can be determined using the bandwidth measurements. It is important to note that, unlike conventional one-port \(S\)-parameter measurements that can be de-embedded, the bandwidth measurements still include the contributions from the pads and access lines. The extracted values of these extrinsic parasitic contributions are therefore re-injected into the final model to obtain realistic results.
# Comparison of Various Factors Affected TID Tolerance in FinFET and Nanowire FET
## 4 Conclusions  
In this paper, we found that the 5 ch-NW GAA devices exhibited less ionizing radiation sensitivity when compared to the FinFET device. Structurally, the 5 ch-NW device had an oxide area of approximately 96.1 nm\({}^{2}\), which is larger than that of the FinFET. However, in our experiment, the TID effect of 5 ch-NW was observed to be less than that of the FinFET. It was shown that the gate controllability factor is more crucial to the suppression of the TID effect than the oxide area. The GAA structure, which has larger gate controllability, confirmed that the TID effect problem can be effectively resolved. The GAA structure and high-k dielectric materials have to be introduced to improve gate controllability. Furthermore, the 5 ch-NW was not found to be affected by V\({}_{\text{DD}}\). However, the FinFET was affected by V\({}_{\text{DD}}\) when low radiation was applied. Therefore, devices with strong gate controllability were not affected by TID and channel doping had little or no influence on the TID effect.  
H.W. simulation and analysis of data and Methodology; I.H. correcting article and design figures; Y.J. writing review and editing; M.K. project administration and supervision and proofreading the manuscript and funding.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A03023788), the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (N000P0008500, The Competency Development Program for Industry Specialist), the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (2017M1A7A1A01016265). This research was also supported by the MOTIE (Ministry of Trade, Industry & Energy (10085645) and KSRC (Korea Semiconductor Research Consortium) support program for the development of future semiconductor devices.  
The authors declare no conflict of interest.