esa-sceva/satcom-chunk-collection huggingface dataset:
# Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails
## 3 Materials and Methods
### Architectural Experimentation
#### 3.3.2 Deep Learning
The same varied accuracies were also produced when using LSTM. The implications of achieving varied accuracies in phishing detection are that an undetected phishing or spam email could severely impact organisations. Combining LSTM and Simple RNN did not see high accuracies either; however, accuracies above 70% were achieved more consistently. Overall, MLP was found to be the most suitable algorithm for numerical-based datasets as accuracies of above 90% were achieved. As [PERSON] et al. [43] consider Simple RNN and LSTM in their effectiveness for classifying URLs as malicious or benign, both algorithms were also tested for classifying text-based datasets. The Spam Classification dataset from [PERSON] [27] was used for this part of the experimentation phase because NLP techniques and deep learning algorithms could be applied. Using a similar approach to [PERSON] et al. [44] and Bountakas, Koutroumpouchos and Xenakis [6], TF-IDF was used because it is a popular technique for classifying phishing emails. In the case of [PERSON], [PERSON] and [PERSON] [6], a high accuracy was achieved when using TF-IDF alongside machine learning algorithms; therefore text-based datasets were converted into numerical form using TF-IDF prior to using the deep learning model. As previously highlighted, RNN algorithms have solely been tested for classifying the text-based datasets as [PERSON] et al. [45] state that RNN has obtained good performance in artificial intelligence tasks including natural language processing. LSTM was the first RNN-based algorithm that was tested, producing an accuracy of 98% and precision of 0.98 consistently throughout the five runs. Even when the model was changed to resemble the number of layers defined in Figure 2, the accuracy and precision remained the same. Likewise, Simple RNN achieved an accuracy of 98% and a precision of 0.98. As both algorithms produced high accuracies and precisions, another text-based dataset was used to determine the reliability of the results. The Spam Email dataset from [PERSON] by [PERSON] [26] was split into a training and testing dataset and then the training dataset was used. As this second dataset was larger, only 5 epochs were used but both Simple RNN and LSTM still achieved an accuracy of 99.89% and a precision of 0.998883. Both algorithms were combined to understand their value together, but high accuracies and precisions were still achieved. As LSTM has appeared more in similar research, this algorithm was chosen for classifying text-based datasets. It was observed from the experiments that NLTK and Keras were the most suitable Python libraries for this phishing and spam detection solution. MLP and LSTM were chosen to classify numerical-based datasets and text-based datasets, respectively. All these elements have been combined in the development of a phishing and spam detection solution.
## 2 Materials and Methods
### Comparative Analysis  
Based on the findings, the comparative analysis focused on the difference in the onboard experience. This research used the network graph algorithm and conducted the interpretation to visualize the difference between the groups and explain the effect of onboard training on various aspects of navigation performance. The network graph algorithm is composed of \"nodes\", \"edges\", and \"weight\". When the weight of the edges is high, the connectivity between the nodes becomes strong. Furthermore, if the nodes are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Sample** & **Unit** \\ \hline Time & \(4\times 10^{-1}\) & Second (s) \\ UTM (East) & \(-6.8094\times 10^{-4}\) & Meter (m) \\ UTM (North) & \(1.1170\times 10^{5}\) & Meter (m) \\ Heaving & \(0.5142\times 10^{-6}\) & Meter (m) \\ Yaving & \(2.2744\times 10^{-4}\) & Degree (\({}^{\circ}\)) \\ Pitching & \(-1.1577\times 10^{-7}\) & Degree (\({}^{\circ}\)) \\ Rolling & \(4.1719\times 10^{-4}\) & Degree (\({}^{\circ}\)) \\ Speed & \(6.7110\) & Meter per second (m/s) \\ Drift & \(-1.0535\times 10^{-3}\) & Meter per second (m/s) \\ Rate of turn & \(2.0434\times 10^{-3}\) & Degree per second (\({}^{\circ}\)/s) \\ Propeller (PORT) & \(1.0287\times 10^{2}\) & RPM \\ Propeller (STBD) & \(1.0287\times 10^{2}\) & RPM \\ Rudder angle & \(-7.2000\) & Degree (\({}^{\circ}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elements of raw data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Unit** \\ \hline Max ROT & The maximum value of “rate of turn” & Degree per minute (\({}^{\circ}\)/min) \\ \hline Mean ROT & The average value of “rate of turn” & Degree per minute (\({}^{\circ}\)/min) \\ \hline STD ROT & \begin{tabular}{c} Standard deviation value of “rate \\ of turn” \\ \end{tabular} & Degree per minute (\({}^{\circ}\)/min) \\ \hline Mean rudder & The average value of rudder angle & Degree (\({}^{\circ}\)) \\ \hline First max rudder & \begin{tabular}{c} Maximum rudder angle used for \\ first altering \\ \end{tabular} & Degree (\({}^{\circ}\)) \\ \hline Midship rudder & \
\begin{tabular}{c} The temporal ratio of rudder angle \\ at midship \\ \end{tabular} & Ratio (\%) \\ \hline Hard rudder &
\begin{tabular}{c} The temporal ratio of rudder angle over \\ 30 degrees \\ \end{tabular} & Ratio (\%) \\ \hline Idle time & Idle time to use of rudder & Second (s) \\ \hline First max time & Elapsed time to use “first max rudder” & Second (s) \\ \hline Reverse time & Elapsed time to end first altering & Second (s) \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of steering features.  
strongly connected, the network is visualized as an aggregate shape as shown in Figure 3a, whereas the nodes are dispersed as a dispersion shape as shown in Figure 3b.  
This study defined each participant as a node, feature similarity as an edge, and absolute difference in feature value as a weight; this is known as connectivity. Then, edges with weak connections based on the average of weights were excluded from taking the verification of navigation skills into account.
## 7 Evolutionary Methods
### Supply Chain and Logistics Optimization  
Efficient supply chain management is critical for minimizing costs, reducing waste, and ensuring timely delivery of perishable goods. Evolutionary methods, particularly GA and PSO, have been widely used to optimize supply chain networks in the food industry, addressing challenges such as vehicle routing, inventory management, and demand forecasting.  
In one application, GA was used to optimize delivery routes for a dairy company, reducing transportation costs by 15% and delivery times by 20%. Similarly, PSO wasapplied to optimize inventory management in a frozen food supply chain, achieving a 12% reduction in spoilage rates and a 10% increase in order fulfillment accuracy [66].
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 3 Proposed Method
### DIFSCL Method
#### 3.2.2 Supervised Contrastive Learning  
After obtaining the embedded features outputted by the feature extractor, the source domain embedding features and the target domain embedding features are fed into a projection head. The purpose of using the projection head is to convert the embedded features into a new feature space, allowing the model to learn more complex feature representations, which is beneficial for the calculation of contrastive loss and gradient propagation. \(i\in I=\{1,2,\ldots,N\}\) is the index of the samples. The supervised contrastive loss used in the experiments is formulated as follows  
\[L_{con}=\sum_{i\in I}\frac{-1}{|P(i)|}\sum_{p\in P(i)}\text{log}\Bigg{(}\frac{ \exp(F_{i}\cdot F_{p}/\tau)}{\sum_{a\in A(i)}\exp(F_{i}\cdot F_{a}/\tau)}\Bigg{)} \tag{5}\]  
In this formula, \(i\in I=|F|\) is the index of any sample from the input task, and \(F_{i}\) represents the normalized feature of the \(i^{\text{t}}h\) sample output by the projection head. \(P(i)=p\in I\) is the set of indices for all samples that belong to the same class as the \(i\)th sample. \(\tau\) is the temperature coefficient, which controls the scale of similarity computation between feature vectors. Using this \(L_{con}\), similar samples in feature space are drawn closer, while samples from different categories are pushed apart, and the label information of the data is fully utilized, allowing the feature extractor to learn more discriminative features.  
In DIFSCL, our contrastive loss is obtained by adding together the source domain contrastive loss and the target domain contrastive loss.  
\[L_{\text{con}}=L_{\text{con}}^{\text{s}}+L_{\text{con}}^{\text{t}} \tag{6}\]
# Size-Dependent Free Vibration of Axially Moving Nanobeams Using [PERSON]'s Two-Phase Integral Model
## 1 Introduction
Recently, [PERSON]'s strain-driven integral law was resorted to in applications to nanobeams [17; 18], without paying attention to the fact that constitutive boundary conditions on the stress field naturally appear in dealing with bounded domains [19]. As acknowledged by the scientific community, when properly formulated by adding the constitutive boundary conditions on the stress field, the resulting problem becomes ill-posed, due to conflicting constitutive and equilibrium conditions. As a consequence, no solution of the nonlocal structural problem does exist and this is the motivation why paradoxical results were reported in literature (see, e.g., [20; 21]). As shown in Refs. [22; 23], a partial remedy to these difficulties has consisted in adopting [PERSON] two-phase integral model. Applications of this approach to bending, buckling and vibration problems of nanobeams can be found in Refs. [24; 25; 26]. All drawbacks of strain-driven nonlocal methodologies can be overcome, according to the proposal in Ref. [27], by exploring the stress-driven integral model, where the roles of stress and elastic strain fields are interchanged. However, both strain-driven and stress-driven models are not invertible constitutive laws. Even when they are invertible, they are definitely not one the inverse of the other [28]. Structural applications of the stress-driven model have been successfully carried out in several recent papers (see e.g., [29; 30; 31] and references therein). Though there exists a lot of work which explores nonlocal integral models, the nonlinear effects (say, geometric nonlinearity) in such integral models have not been adequately examined, especially for [PERSON]'s nonlocal integral models. Therefore, nonlinear vibrations of axially moving nanobeams using [PERSON]'s nonlocal integral models will be investigated in the present paper. The arrangement of this paper is arranged as follows. In Section 2, using [PERSON]'s two-phase integral model, the integro-differential model equations are obtained for the axially moving nanobeam by Hamilton's Principle. In Section 3, the critical velocity and the natural frequencies are determined for free vibrations, the small scales effect on natural frequency is also studied. Finally, we make conclusions in Section 4.
## 5 Security Analysis
### Error Correction and Privacy Amplification  
It is known that IoT devices can calculate hash functions, which are used to improve security by integrating them into the KeyGen protocol. However, the specific functions they can compute depend on the device's processing power, memory, and energy constraints. They typically have limited resources compared to more powerful devices such as computers and servers. For this reason, one of the commonly used approaches to improve security in the IoT setting is the use of hash functions. Among them, BLAKE2 appears to be a good choice since it has versions optimized for 64-bit architectures (BLAKE2b) and optimized for 8- to 32-bit platforms (BLAKE2s) [25]. With this at hand, one possible procedure for error correction and privacy amplification that can be applied to ensure that the OTP key obtained by the SMP-OTP4 IOT procedure is as follows (Algorithm 2):  
```
0: SMP-OTP4 IOT derived Key \(K_{1}\) for user \(U_{1}\) and \(s_{1}+K_{2}\) for user \(U_{2}\) & agreed hash function \(h\).
0:\(K_{1}=s_{1}+K_{2}\)  Initialization: \(U_{1}\) generates a random position \(b\in\{1,2,\ldots n\}\) such that \(K_{1}=[k_{1},k_{2},\ldots k_{b-1}]||[k_{b},k_{b+1},\ldots k_{n}]\), with \(n=|K_{1}|\).  Procedure: \(U_{2}\) computes \(h_{b}=h(s_{2}+K_{2}[b,b+1,\ldots n])\) and sends it to \(U_{1}\) \(U_{1}\) verifies if \(h([k_{b},k_{b+1},\ldots k_{n}])=h_{b}\). If so, return True, otherwise return False.
```  
**Algorithm 2** KeyGen Security Check: Error Correction and Privacy Amplification  
Algorithm 2 enables the user \(U_{1}\) to verify that \(U_{2}\) has the same OTP key, which can be used by \(U_{2}\) in the same way. It should be noted that this concept is not novel and has been applied in a different context, such as in IoT with a quantum security layer, as illustrated in [26]. Observe also that the final steps in the KeyGen security check (Algorithm 2) might seem unnecessary since comparing the hash of the entire key should suffice, which is the case. However, selecting a random subset based on position \(b\) introduces a variable workload to the processor each time the procedure is executed, making it easier to prevent a side-channel attack by importing processor energy consumption, as in [27].  
The multiparty protocol outlined thus far enables user \(U_{1}\) to collaboratively generate and share a key with user \(U_{2}\), utilizing the dummy server \(U_{3}\) as a third party. This ensures the privacy of the inputs, which, in our scenario, are the private registers of the data involved in the computation. It is reasonable to assume that these inputs reside on different devices or architectures. To further bolster security in this context, a (2,3) secret-sharing scheme, as described in [28] or [29], can be considered. This scheme can be integrated into our algorithm at its conclusion, thereby eliminating the necessity for \(U_{1}\) to send \(s_{1}\) to \(U_{2}\) in plaintext. This makes our approach particularly well suited for IoT devices as it mitigates the risk of data breaches by ensuring that no single device possesses sufficient information to compromise the entire system.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 3 Proposed Model
### Data Integrity Verification Using Zero-Knowledge Proofs
A smart contract accepts or rejects the data integrity based on the result. This architecture allows secure, trustless verification of private data integrity while maintaining performance and scalability.
## 2 Investigation of Field Measurement Problems
### Carbody Lateral Vibration Monitoring  
According to Chinese specification Maintenance rules for ballasted track lines of high-speed railways [29], when the carbody lateral acceleration exceeds 0.6 m/s\({}^{2}\), the level-one alarm appears, so it is necessary to carry out daily maintenance on the vehicles and track lines. The level-two alarm sounds when the carbody lateral acceleration exceeds 0.9 m/s\({}^{2}\), and it is important to be concerned for both the safety of the vehicles and the track  
Figure 1: Wheel–rail force test: (**a**) wheel–rail force test results; (**b**) wheel–rail contact dynamic stress tester from Jiangsu Donghua Testing Technology Co., Ltd. Jingjiang, Jiangsu, China.  
lines. A level-three alarm sounds when the carbody lateral acceleration exceeds 1.5 m/s\({}^{2}\), necessitating temporary repairs to the vehicles and track lines. The speed limit of the vehicles is required when the carbody lateral acceleration exceeds 2.0 m/s\({}^{2}\). A level-four alarm then sounds. The value of 0.5-10 Hz band-pass filtering is used to determine the level-one and level-two standards of carbody lateral acceleration, while the level-three and level-four standards are determined by the value of 10 Hz low-pass filtering.  
The portable train vibration analyzer (Figure 2b) was used by railway department employees to test turnout from 10 June 2022 to 19 June 2022, as shown in Figure 2a. A total of 20 sets of high-speed trains were tested in all, 2 sets per day, 1 in the morning and 1 in the afternoon. The level-one alarm was activated four times, the level-two alarm was activated ten times, and the level-three alarm was activated six times as the trains passed through the turnout. A total of 50% of the warnings were level three or above (Figure 2c).
# Dual-Band Monopole Antenna for RFID Applications
## 3 Results and Discussions
As shown, more than 75% radiation and total efficiencies (R.E. and T.E.) have been achieved at the frequency bands. More than 2 dBi and 4 dBi maximum gain levels are obtained for the antenna at 2.4 and 5.8 GHz, respectively. Figure 7: Return loss results for different values of (**a**) L, (**b**) L\({}_{2}\), (**c**) W\({}_{1}\), (**d**) L\({}_{1}\), (**e**) L\({}_{3}\), (**f**) W\({}_{2}\), (**g**) L\({}_{\text{gnd}}\), and (**h**) L\({}_{\text{4}}\). Top and bottom views for the prototype are illustrated in Figure 9. The antenna has been constructed on a low-cost FR4 dielectric substrate with an overall dimension of 40 \(\times\) 28 \(\times\) 1.6 mm\({}^{3}\). The measured and simulated return loss characteristics of the antenna are represented in Figure 10. As can be observed, the antenna exhibits good return loss characteristic, covering 2.4/5.8 GHz resonant frequencies, which compared with the simulated result, the agreement is acceptable. However, there is a slight discrepancy between them which could be mostly due to possible errors in the prototype dimensions as well as the permittivity of the high-loss FR-4 substrate. Figure 8: The antenna fundamental radiation characteristics. Figure 4.0 Fabricated antenna, (**a**) top and (**b**) bottom views. The simulated and measured 2D-polar radiation patterns of the antenna for H-plane and E-plane at 2.4 and 5.8 GHz are illustrated in Figure 11; the antenna provides omnidirectional radiation patterns in the H-plane, while 8-shaped radiation patterns in E-plane have been achieved for different resonant frequencies. The simulation and measurement results of the antenna gain versus its operation frequency are plotted in Figure 12; the antenna provides sufficient gain levels with more than 2 dB at 2.6 GHz and 3.5 dB at 5.8 GHz. Figure 4.1 Simulated and measured 2D-polar radiation patterns. Figure 4.2 Measured and simulated return losses of the antenna.
## 3 Data Sources and Research Methods
### Research Methods
The formula for kernel density estimation is expressed as follows:  
\[f(x)=\frac{1}{nh}{\sum_{i=1}^{n}K(\frac{x-X_{i}}{h})} \tag{2}\]Here, \(f(x)\) represents the kernel density function estimated at spatial location \(x\), \(h\) denotes the bandwidth or distance decay threshold, \(n\) is the number of elements within or equal to distance \(h\) from the element at location \(x\), and \(K(x)\) is the spatial weight function, which in this study adopts a normal kernel function. The bandwidth \(h\) is critical as it determines the smoothing parameter of the kernel estimation, influencing the extent of the spread of the kernel function around each point. The choice of \(h\), or the distance decay threshold, is determined based on the smooth model principle, which ensures that the density estimate is neither too smooth nor too detailed, thereby optimizing the balance between variance and bias in the estimation. By carefully selecting \(h\), the KDE can effectively highlight areas of high element concentration, providing visual insights into the spatial distribution and density of the features being studied. This technique is widely applied in various fields such as environmental science, epidemiology, urban planning, and crime mapping, offering a powerful tool for spatial analysis and decision making based on geographic phenomena. Other Analytical Methods  
ArcGIS, a comprehensive geographic information system, offers specialized tools that facilitate detailed analyses of the directionality and correlation of spatial elements within a given dataset. To examine the directional patterns of spatial data, the Directional Distribution tool, part of the Spatial Statistics Tools in ArcGIS, is employed [71]. This tool is designed to analyze the spatial orientation and trends of geographic features, helping to identify prevailing directions or alignments in the data, which is crucial for understanding spatial dynamics in fields such as meteorology, hydrology, and urban planning. For analyzing the correlation between spatial elements, particularly when dealing with multi-band raster data, the Band Collection Statistics tool from the Spatial Analyst Tools suite is used. This tool is instrumental in computing statistical relationships across different spectral bands, aiding in the identification of patterns and correlations that are not immediately apparent [72; 73]. By utilizing this tool, researchers can effectively dissect complex interactions between multiple layers of data, enhancing their understanding of ecological dynamics, land use changes, or environmental monitoring. These tools in ArcGIS provide robust capabilities for advanced spatial analysis, allowing researchers and professionals to derive meaningful insights from their geographic data. The integration of these analytical tools into spatial studies promotes a more nuanced interpretation of spatial phenomena, supporting more informed decision making and strategic planning in various scientific and professional applications [74].
## 3 Results and Discussion
### Urease Activity (UA) and Functional Properties
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **SPI pH 4.5** & **SPI pH 7** & **Reported SPI [43]** \\ \hline UA & -* & 0.06 \(\pm\) 0.02 & 0.04 \(\pm\) 0.01 \\ WSI (\%) & 6.28 \(\pm\) 0.10 & 81.39 \(\pm\) 3.02 & 33.11 \(\pm\) 1.13 \\ WAI & 3.26 \(\pm\) 0.15 & -* & 7.97 \(\pm\) 0.16 \\ FA (\%) & 315.90 \(\pm\) 15.53 & 302.30 \(\pm\) 19.91 & 442.00 \(\pm\) 15.57 \\ FD (\%) & 12.12 \(\pm\) 1.89 & 24.90 \(\pm\) 1.27 & 18.46 \(\pm\) 0.54 \\ FS (\%) & 34.54 \(\pm\) 4.72 & 88.63 \(\pm\) 4.61 & 90.17 \(\pm\) 2.42 \\ EC (\%) & 82.78 \(\pm\) 5.08 & 79.76 \(\pm\) 3.72 & -* \\ ES 24 h (\%) & 82.78 \(\pm\) 5.08 & 79.76 \(\pm\) 3.72 & 43.39 \(\pm\) 0.92 \\ ES 48 h (\%) & 100.00 \(\pm\) 0.00 & 97.76 \(\pm\) 0.10 & 76.39 \(\pm\) 4.31 \\ \hline \hline \end{tabular}  
* Not reported or calculated. UA: urease activity; WSI: water solubility index; WAI: water activity index; FA: foam activity; FD: foam density; FS: foam stability; EC: emulsion capacity; ES: emulsion stability.  
\end{table} 
Table 8: Functional properties and urease activity of SPI. report of [PERSON] et al. (2020), who discussed that a lower WSI had a higher FA, but as SPI at pH 4.5 had such low solubility, almost no protein was available to form the foam [43]. On the contrary, FD of SPI at pH 7 was twice the value of SPI at pH 4.5 due to the higher solubility and greater availability of proteins to form and stabilize the foam. Furthermore, FS increased more than half from SPI at pH 4.5 to SPI at pH 7 because of the better availability of proteins in the foam. Moreover, SPI at pH 7 results were comparable to reported SPI. As foaming characteristics, emulsion capacity (EC) is also a two-phase interaction property, but with an oil-water interface interaction that is stabilized by the proteins, and emulsion stability (ES) is the ability of the proteins to stabilize the emulsion through time. To form stable emulsions, emulsifiers are needed. In this case, [PERSON] et al. (2018) discussed that mainly amino acids, such as L-arginine and L-lysine, because of their basicity, act as emulsifiers and accrue in the interface between water and oil to help soothe the emulsion by increasing the repulsion by electrostatic forces and diminishing the tension in the interface [59]. EC was slightly higher in SPI at pH 4.5 because proteins near their IpH have higher adsorption and viscoelasticity at an oil-water interface due to the increased surface hydrophobicity [58; 60]. As proteins at IpH have better emulsifying properties, ES at 24 and 48 h was expected to be higher for the SPI at pH 4.5.