esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 1 Introduction
### From Public Key Cryptography to Physical Layer Security  
Conventional encrypted communications employ symmetric encryption such as the advanced encryption standard (AES), which relies on a secret key shared between thembeforehand. Public key cryptography (PKC) is the de facto key distribution protocol, although efficient conventional PKC schemes are complex and computationally not suitable for IoT devices with limited capability [7]. This introduces not only a computational cost challenge, but also sets the IoT devices at a disadvantage against the most powerful eavesdroppers with orders of magnitude more computational power.  
Physical layer security (PLS) has been proposed in recent years as a way to overcome many of the aforementioned challenges, which can be categorised as key-less PLS [8; 9] and physical layer secret key generation (PL-SKG) [10; 11; 12]. Key-less PLS leverages the superiority of legitimate channels over wiretap ones, and tries to maximise the secrecy rate or signal to interference plus noise ratio (SINR) by steering the variables, e.g., beamforming vectors [13], trajectory of autonomous system [14], and even the phases of a reconfigurable intelligent surface (RIS) [15]. One challenge lies in that the superiority of the legitimate channel may not be always satisfied. For example, an Eve can make the channel act as adversary to legitimate nodes by introducing an Eve-controlled RIS. In such cases, key-less PLS cannot guarantee an existence of feasible solutions for secrecy rate and SINR maximisation.  
Another family in PLS is PL-SKG, which exploits the reciprocal channel randomness to generate the shared secret key between legitimate nodes, without the need for a PKC management and distribution protocol [6; 16; 17]. PL-SKG negates the risk of key intercept and high computational requirement of PKC schemes [18], which makes it very suitable for IoT devices. PL-SKG does, however, require the wireless channel between nodes to be reciprocal (true for most propagation environments), random (small-scale fading), and unique [19; 20]. This is to ensure robust symmetric key generation and avoid brute force attempts. Although PLS has been applied to a variety of embedded wireless (e.g., autonomous vehicle [21], UAV [22; 23], and molecular [24]) and wired (e.g., fibre [25]) communication systems, the shortfalls are always overlooked. First, the common features used by PL-SKG for data encryption are not detached from the communications, e.g., using channel phases or received signal strength (RSS), which may provide the chances of sophisticated eavesdroppers to decode the secret key [26; 27]. Second, for both key-less and PL-SKG, they require the IoT devices to make accurate estimations of the wireless channel statistics [28]. Accurate estimation requires reasonably powerful signal processing units and also requires a reasonably high communication signal-to-noise ratio (SNR). Many embedded or underground IoT devices operate in low-communication SNR regimes, and therefore PLS is not suitable.
## 3 Results and Discussions  
### Time Change of Neutron Dose Rates  
Figure 3 shows the time change of atmospheric pressure measured with a barometer and neutron-dose rates measured with WENDI-II near Dome Fuji Station (77.735\({}^{\circ}\) S, 39.114\({}^{\circ}\) E, 3762 m in altitude; location A in Figure 1). Each plot was obtained as an average of continuous measurements at the same location for 12 h. Total counts of neutron pulses at each location were around 7000, and thus the standard deviation of each value was derived as less than 2%. A clear, inverse correlation was observed between the neutron-dose rate and atmospheric pressure, which was considered to result from the nuclear spallation reactions with atmospheric atoms such as nitrogen and oxygen. The fact that this relationship was confirmed even for a small fluctuation (\(\sim\)10 hPa) of atmospheric pressure indicates that the employed portable system (WENDI-II and NM10) can be appropriately used for on-site monitoring of cosmic neutrons in Antarctica.
###### Abstract  
Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.  
Keywords:Internet of Things; wireless sensor networks; superframe structure +
Footnote †: journal: Sensors
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods
### GAs  
A GA is an optimization algorithm based on the principle of biological scientific selection. GAs are widely used in linear and nonlinear optimization problems [15; 33; 34]. In this study, the inversion of [PERSON]'s model is also carried out through an optimization approach using GAs.  
Based on Equation (2), the inversion process takes displacement data as the input to obtain the nine parameters of [PERSON]'s model. However, this study only estimates four parameters through inversion: the length, width, rake, and slip parameters. The other parameters of \(E\), \(N\), depth, dip, and strike are predetermined parameters based on the earthquake fault model used in the experiment. GAs, as heuristic search algorithms, seek the best solutions for the estimated parameters.  
In this study, the estimation results of the four variables along with the predetermined parameters are then used as input for the forward calculation of [PERSON]'s model to obtain the displacement estimation values. There are two displacement values involved in this inversion process: actual displacement and estimated displacement. The actual displacement is calculated from GPS time series data using the permanent displacement formula, while the estimated displacement is obtained from the estimation using [PERSON]'s model.  
The generic GA is shown in Figure 2. The algorithm starts by initializing a population that consists of individuals representing solution candidates for the problem to be solved. The fitness of each candidate solution for the problem is then evaluated using an objective function called the fitness function. Two individuals with highest fitness score are then selected for the next parent candidate to produce new offspring through a crossover phase. The next step is the mutation phase to maintain or increase the genetic diversity of the population. This process will continue until the fitness value stabilizes and the algorithm has converged to an optimal solution. The following paragraphs provide more detailed descriptions about the implementation of each step in this study.
## 2 Experimental
### Degradation Experiments  
The removal of Reactive Black 5 from the aqueous phase, by NGO-Mn\({}_{3}\)O\({}_{4}\) and NGO-MnO\({}_{2}\) nanocomposites, was studied by batch experiments. Firstly, the effect of the solution pH on the catalytic performance of the composites prepared was investigated. Solutions of the dye were prepared with initial concentrations of 100 mg/L, same volume (20 mL) and the same amount of catalysts (0.01 g) and the pH was adjusted between 3-9 with a pH-meter (CRISON basic 20), by micro-additions of dilute HCl and NaOH solutions. The adjusted suspensions were shaken (at agitation rate 150 rpm) for 24 h at 25 \({}^{\circ}\)C in a water bath with a shaker (JULABO SW-21C) and they were filtrated through a 0.45 \(\upmu\)m pore size membrane syringe filter (obtained by Schleicher-Schuell Micro Science). The remaining concentration of the dye was measured by UV--Vis. The removal percentage (R%) of RB5 was calculated according to the Equation (1):  
\[R\%=\frac{C_{o}-C_{e}}{C_{o}}\times 100\% \tag{1}\]  
where \(C_{o}\) and \(C_{e}\) (mg/L) are the initial and equilibrium concentrations of Reactive Black 5, respectively. The optimum pH for the degradation experiments was found to be 3.  
Kinetic experiments took place by mixing 0.01 g of catalyst and 20 mL of RB5 solution (Co = 40 and 100 mg/L), at optimum pH value. The suspensions were shaken in a water bath at 25 \({}^{\circ}\)C (agitation rate = 150 rpm) in various adsorption time (5 min-24 h), filtrated through 0.45 \(\upmu\)m pore size syringe filter and the remaining RB5 concentrations were measured by UV-Vis. All experiments were performed triplicate and the results presented are the mean value.
## 2 Methodology
### Data Generation for Fault Classification  
Based on the feature extraction process that uses Wavelet Packet Transform, illustrated in Section 2.1, the GAN network described in Section 2.2 and a random forest classifier detailed in the previous section, one can setup the learning scheme for the manipulator fault diagnoser. As shown in Figure 4, a real data observation of from each class is sent to the WPT to extract the vector of features (12). Meanwhile, a random signal \(z\) is input into the generator that will produce a vector of synthetic features \(\mathrm{G}(z)\).  
The goal of the discriminator \(\mathrm{D}(x)\) is to distinguish between the real vector of features, outputting a 1, and the synthetic vector of features, outputting a 0. The learning process described in Section 2.2 is applied. Once both the generator and discriminator are trained, the generator can be used to generate as many synthetic data as required. Notice that the learning process and subsequent synthetic generation are carried out for each faulty class, i.e., for each class, we need to increase the number of observations. Finally, (health and faulty) real data together with faulty generated data are used in the random forest classifier for fault classification. Based on the above description, the procedure can be outlined in the following flow chart (Figure 5).  
In addition, this whole process is illustrated in Figure 6.  
Figure 4: The learning scheme of the fault diagnoser.  
Figure 5: The flow chart of the procedure of the proposed approach.
## 3 Results
### Uncertainty Analysis under Different Noise Conditions
#### 3.2.3 \(\mathrm{FF_{Smith}}\)  
The ROC curves of \(\mathrm{FF_{Smith}}\) under different noise conditions (\(-20\) dB, \(-10\) dB and \(-7\) dB) are shown in Figure 13a-c, respectively. The shapes of ROC curves are similar, which indicates \(\mathrm{FF_{Smith}}\) is less affected by noise. However, the performance of \(\mathrm{FF_{Smith}}\) is poor that all the curves gather near the random guess line. The evaluation metrics are plotted in Figure 13d-f. The precision is close to \(0.8\) when the noise is \(-20\) dB, while the other metrics are less than \(0.7\) as shown in Figure 13d. The results in \(-10\) dB and \(-7\) dB (Figure 13e and f) are similar to the \(-20\) dB scenario.  
Figure 11: The ROCs of the \(\Delta K\) in the different noise scenarios: (**a**) \(-20\) dB, (**b**) \(-10\) dB, (**c**) \(-7\) dB. The metrics of the \(\Delta K\) in the different noise scenarios: (**d**) \(-20\) dB, (**e**) \(-10\) dB, (**f**) \(-7\) dB.  
Figure 12: The AUC curves of the \(\Delta K\) in the different noise scenarios: \(-20\) dB, \(-10\) dB and \(-7\) dB.  
The comparison of the AUC curves in the scenarios where the noise is \(-20\) dB, \(-10\) dB and \(-7\) dB is shown in Figure 14. The performance of FF\({}_{\text{Smith}}\) decreases with increasing noise. The approximate ranges of the AUC are 0.6 to 0.7 when the noise is \(-20\) dB (blue), \(-10\) dB (red) and \(-7\) dB (yellow), respectively.
## 2 Materials and Methods
[PERSON]'s correlation coefficients were also used to evaluate the relationships between BABF and FBF, as well as NIRS-derived variables (Table 4). The magnitudes for all correlations were interpreted as follows: 0.0-0.2 as \"poor\", 0.3-0.5 as \"fair\", 0.6-0.7 as \"moderate\", 0.8-09 as \"very strong\", and 1.00 as \"perfect\" [40]. To determine consistency, intraclass correlation coefficients with the 2,1 model (ICC2,1) were reported for the four resting values used to calculate the average resting values for each variable: FBF, O2 Hb, HHB, tHb, and HbDiff [41]. Again, this was carried out for the total sample, as well as independently for each sex (Table 5). Standard error of the mean (SEM) was reported for all ICCs2,1. In addition, 95% confidence intervals (CI95%) were reported for all mean differences and correlation coefficients. The collected data were exported to a custom Microsoft Excel template that allowed for organization and normalization. All calculations and statistical analyses were conducted using JASP version 0.14.1 (University of Amsterdam, Amsterdam, The Netherlands). A value of \(p\leq 0.05\) was considered statistically significant. All data were reported as mean \(\pm\) SD. \begin{table}
\begin{tabular}{c c c c c} \hline
**Variables** & **ANOVA** & \(p\)**Value** & \(\eta_{p}^{2}\) & **Effect** \\ \hline Mean Arterial Pressure & & & & \\ (mmHg) & Sex \(\times\) Time & 0.352 & 0.032 & \\  & Sex & 0.137 & 0.080 & \\  & Time & **0.047** & 0.138 & Resting \textgreater{} Peak \\  & & & & Reactive Hyperemia \\ Heart Rate (bpm) & Sex \(\times\) Time & 0.287 & 0.042 & \\  & Sex & 0.204 & 0.059 & \\  & Time & **0.007** & 0.240 &
\begin{tabular}{c} Resting \textless{} Peak \\ Reactive Hyperemia \\ \end{tabular} \\ \hline ANOVA models for resting and reactive hyperemic physiological variables between sexes. Bolded \(p\)-values correspond to significant at the \(\leq\)0.05 level. Resting was the time prior to complete occlusion. Peak reactive hyperemia was the 1 st seven seconds following complete occlusion. & \\ \end{tabular}
\end{table} 
Table 2: Sex-specific hemodynamics. \begin{table}
\begin{tabular}{c c c} \hline
**Reactive Hyperemia Variables** & \(r\) & **Lower-Upper CI95\%; \(p\)** \\ \hline
**Total (n = 29)** & & & \\ FBF and O2 Hb & \(-\)0.126 & \(-\)0.47 to 0.25; 0.516 \\ FBF and HHb & 0.228 & \(-\)0.15 to 0.55; 0.235 \\ FBF and tHb & 0.061 & \(-\)0.31 to 0.42; 0.753 \\ FBF and HbDiff & 0.046 & \(-\)0.33 to 0.41; 0.811 \\
**Men (n = 15)** & & & \\ FBF and O2 Hb & \(-\)0.219 & \(-\)0.66 to 0.33; 0.433 \\ FBF and HHb & 0.217 & \(-\)0.33 to 0.66; 0.438 \\ FBF and tHb & 0.010 & \(-\)0.51 to 0.52; 0.972 \\ FBF and HbDiff & 0.237 & \(-\)0.31 to 0.67; 0.395 \\
**Women (n = 14)** & & & \\ FBF and O2 Hb & \(-\)0.150 & \(-\)0.63 to 0.41; 0.608 \\ FBF and HHb & 0.207 & \(-\)0.36 to 0.67; 0.477 \\ FBF and tHb & \(-\)0.128 & \(-\)0.62 to 0.43; 0.663 \\ FBF and HbDiff & \(-\)0.048 & \(-\)0.56 to 0.50; 0.871 \\ \hline \end{tabular}
\end{table} 
Table 3: Relationships between reactive hyperemic forearm blood flow and oxygenation parameters.
## 5 Experimental Results
### Parameter Selection Strategy
#### 5.2.2 Embedding Size
This complementarity reduces the false positive rate by 6.49% compared to a single embedding method under the same setting, thus proving the rationality of the high-dimensional dual embedding configuration. Traditional network traffic detection methods usually directly use discrete features as numerical inputs or use one-hot encoding. They artificially assume that there is a certain order relationship between numerical values. For example, the numerical difference between port \"22\" and port \"80\" does not reflect the actual semantic difference and cannot capture the contextual semantic association between features, resulting in insufficient model generalization ability. To overcome the above limitations, the dual-channel embedding method that combines Word2 Vec and FastText, which can learn the global semantic structure of traffic patterns through contextual co-occurrence relationships, enables the model to identify potential behavioral commonalities between different attack types and enrich the semantic information of traffic representation. FastText, by decomposing features into subword units for embedding training, is an effective supplement to the generalization ability of Word2 Vec. This dual-channel embedding method combines the expression of global trends and local details, converting the originally sparse, discrete, and semantically inconsistent numerical features into high-dimensional vector representations with contextual semantics, which not only alleviates the representation bias problem in traditional methods but also improves the ability to identify malicious traffic. Our experimental results also verify that this method significantly improves detection performance. In summary, the dual-channel embedding strategy effectively bridges the semantic gap in traditional methods, improves the model's ability to express and detect abnormal traffic, and is a substantial improvement over existing numerical modeling methods.
# UAV Application for Typhoon Damage Assessment in Construction Sites
###### Abstract  
The safety inspection capability of construction sites before typhoons could be improved using a UAV, which has a rapid identification capability. However, the main safety inspection items need construction experience and technical safety specifications. This study aimed to obtain the influencing factors of typhoon and their weight proportion through the knowledge of disaster theory and the analytic hierarchy process (AHP). The effectiveness of this method was verified by collecting and analyzing the field data at the construction site. A set of construction site early warning flows and disaster prevention and mitigation measures for typhoons are proposed. The results show that UAVs can be used as a tool in this scenario, helping to improve disaster prevention and enhancing the capability of construction site management to evaluate typhoon risk. The research provides a much-needed common ground for collecting and analyzing advances in UAVs and immersive technologies, as well as their influence on building projects. Furthermore, this article provides a new horizon for beginner researchers working on digitalized construction research.  
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three-level image-based approach for disaster inspection on reinforced concrete bridges using deep learning with novel training strategies. The authors of [15] developed a model incorporating rainfall probability during a typhoon when evaluating construction projects related to climate adaptation to reduce infrastructure damages. The authors of [16] stressed the importance of social preparedness for flood loss mitigation based on early warning systems between the hydrological and social spheres, especially when technical forecasting and warning capabilities were limited.  
UAVs used to be mainly implemented in the military field initially, and with the development of science and technology, UAVs began to be used for civilian use [17]. UAVs are widely used in geological prospecting, surveying, entertainment photography, and so on [12; 18; 19]. Due to the powerful maneuverability and the high-resolution image data acquisition function, scholars began to study the UAV applications in natural disaster prevention [20; 21]; for example, [22] proposed a UAV-based method for emergency investigations on single geo-hazards. However, the use of drones in pre-disaster safety inspections on construction sites is still in its early stages [23]. This study aims to develop a pre-typhoon construction site warning and contingency treatment standard operational procedure (SOP) through a series of theoretical model development and construction site experiments. Various criteria of typhoon damage on construction sites were determined through qualitative and quantitative analysis. The effectiveness and feasibility of the developed SOP were verified through UAV experiments on an accurate construction site, the Xiamen Electronic City project. This study also proposed typhoon warning and response scenarios on construction sites.
## 1 Introduction
Using this standard, 99 administrative districts (Si, Gun, and Gu) across the country in Korea (approximately 44%) could not receive EMS within the appropriate time [19]. However, the number of vulnerable areas for EMS increases if the real-time traffic conditions to reach the emergency medical facility are considered. In addition, the dispatch time by an emergency medical technician (e.g., the EMT-P or ambulance workers from the 119-safety center) who can provide first aid should also be considered. In particular, an evaluation of time-based accessibility is an essential requirement considering real-time road conditions by region, because the survival rate could vary significantly depending on whether or not rapid first aid is given in emergencies, such as an acute cardiac arrest. Many studies on accessibility to medical or healthcare-related facilities have been conducted in various fields based on physical distance to identify vulnerable areas [21; 22; 23; 24; 25]. However, few related studies have used real-time traffic flow as well as driving speed data. Studies related to accessibility have mainly based the calculations on the physical distance, such as the Euclidean distance (linear distance), the estimated road distance using a circuity factor, and the actual road distance [26; 27; 28; 29; 30; 31; 32; 33; 34]. However, [PERSON] [13] and [PERSON] [35] pointed out that the linear distance is more likely to result in excessive distortion compared to actual road distances because the linear distance does not reflect the geographical features such as the terrain or road geometry. [PERSON] [13] suggested that calculating the estimated road distance by applying circuity factors to linear distance could be a substitute for the actual road distance for the vulnerability assessment of access to medical facilities. Studies on accessible vulnerability using physical distance have recently improved, and now estimate the accessible time using data about the shape of the road network and legal speed limit provided by the Korean government [36]. However, calculating accessibility using the speed limit of the road has limitations since it does not reflect the effects of slower vehicle speed during traffic jams or traffic signals. The goal of this study is to analyze EMS accessibility using real-time road traffic conditions with driving speed changes by time of day and to estimate the vulnerable areas and survival rate of emergency patients. We also compare the results of urban areas and rural areas. For this purpose, we established a nationwide road network map across Korea to identify the current status of road use distribution following real road speed flow by time. In addition, we evaluated vulnerable areas for EMS by estimating the real-time access time via an optimal route from a village to an emergency medical facility using network analysis. We set up four cases according to the patient transfer process to an EMS facility, considering whether the patient receives first aid or not, and the dispatch time of an EMT-P. We also examined the changes in the survival probability for each case in urban areas compared to rural areas.