esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Extended-Capability Vehicles
### The Hyperloop Concept
This reliable transportation solution could represent an affordable option (SDG7), possibly improving the infrastructure due to the decreased arrival times (SDG8). However, rural areas could not benefit from this infrastructure due to inequalities, reduced income, and distance from central districts [62]. The urgent deployment could further aggravate the climate situation due to the required energy for implementation and the part of fossil fuel used to generate energy and operate the vehicles (SDG7, SDG13). Considering the ongoing climate events and based on the data from [63], the reduction in energy consumption for two hyperloop infrastructures in contrast to road electric vehicles represents only a small quantity. This might not overcome the generated energy using fossil fuel for operating the hyperloop combined with the resulting energy consumption from building the extended transportation infrastructure before a possible climate collapse. However, this scenario is uncertain, and an infrastructure similar to Chicago-Pittsburgh might compensate for some of the climate effects during the extended time operation based on renewable energy. From the robustness and renewability point of view, the hyperloop is robust but currently not renewable, requiring a mostly renewable energy infrastructure to approach the zero-emission commitment, and currently remains a plausible improved transportation solution. Even though railway vehicles have specific advantages that conventional automobiles lack, the implementation cost of such a network may remain much higher than that of a road network for most countries. Also, such systems would be available only for public transportation due to the sophisticated algorithms and reduced mass-production logistics. Last but not least, from an electromagnetic compatibility point of view, a vulnerable point can be identified for both magnetic levitation systems covered in previous chapters. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Topic** & \multicolumn{2}{c}{**Advantages and Compromises**} & \multicolumn{1}{c}{**Expected Effect**} & \multicolumn{1}{c}{**Future Directions**} \\ \hline
1. Intensive use of grid electricity to power hyperloop 1 & Infrastructure development increases, sustainability decreases & Shorter arrival time for freight and passengers & Increased green energy and fossil fuel consumption for electricity production 2 \\ \hline
2. Immediate deployment of hyperloop vehicle networks 1 & Infrastructure development increases, sustainability significantly decreases & Replacement of conventional transportation solutions & Significant non-sustainable resource depletion, immediate GDP impact is uncertain 3 \\ \hline \hline \end{tabular}  
* Corresponds to changes related to energy infrastructure. 2 Confirmed by [6]. 3 A steady GDP (Gross Domestic Product) increase and infrastructure development is confirmed by [111].  
\end{table} 
Table 4: Robustness and renewability analysis for hyperloop.
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 1 Introduction
To investigate the impact of the imbalance between benign and phishing emails on the classifier performance and improve the detection rate of phishing emails, this paper presents the following contributions:  
* To mitigate the impact of unbalanced datasets on classifier performance, two novel algorithms based on undersampling are proposed: FMPED and FMMPED. These algorithms differ in their approach to undersampling benign emails;
* To improve classification performance, we use an ensemble learning approach for handling the hybrid features of emails, which combines decision tree (DT) and support vector machine (SVM) as basic classifiers to train content-based features and text-based features respectively;
* To simulate real-life scenarios, the ratio of benign emails to phishing emails of the dataset used in this article is almost 10:1. In order to conduct a more comprehensive evaluation of the classifier performance, we utilize a variety of evaluation metrics that apply to an unbalanced dataset, including the F1-score, accuracy, AUC, G-mean, and Matthews correlation coefficient (MCC). The structure of the remainder of this paper is as follows. In Section 2, we introduce research related to detecting phishing emails. In Section 3, we provide a detailed explanation of the entire process for detecting phishing emails using our method. In Section 4, detailed descriptions of the proposed algorithms FMPED and FMMPED are provided. In Section 5, we present the experiment results and discussion. In Section 6, we will summarize the main points of this paper and provide insights into future work.
# A Review on Recent Trends and Applications of IoT in Additive Manufacturing
## 3 IoT-Based AM Applications and Limitations  
IoT has been implemented in various sectors for enhancing the efficiency of the technology and to improve the production levels. However, there are several limitations along with their merits for each application as given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Name** & **Applications** & **Advantages** & **Limitations** \\ \hline \multirow{7}{*}{1} & & Reengineering Aircraft & Speed prototyping and & Issues related to data privacy and \\  & & Structural Life Prediction & product redesigning & cyber security \\  & & Cyber-Physical Production & Real-time simulation & Lack of standards, frameworks, \\  & & System (CPPS) & & and regulations \\  & & Simulation of & Two-way data integration & High cost of implementation \\  & & medical products & and interaction & Required AI and big data for \\  & & Model-Based System & Predictive maintenance & long-term data analysis \\  & & Engineering (MBSE) & & Lifecycle & Digital twin is based on 3D CAD \\  & & Agriculture, healthcare, & management platforms & models and not on 2D drawings \\  & & mining, robotics, & Support or improve human & Blurring boundaries between \\ \hline \multirow{7}{*}{2} & & Brain implants and artificial & capabilities & advanced therapies and \\  & & body parts implants & and performance & medical devices \\  & & & & Blurring boundaries between the \\  & & Neuralink & Reduce social injustice & biomaterial and \\  & & Augmentation & & non-biquaterial products \\  & & Exoskeletons for & & Issues of safety and \\  & & workplace safety & Alleviate social inequalities & informed consent \\  & & Hololens & Improvement of overall & \\  & & physical and mental health & Ethical concerns \\  & & Human genetic engineering & Enhance body integrity & Threats of reality modification \\ \hline \multirow{7}{*}{3} & & Aerospace & Rapid production with & It cannot scan beyond the \\  & & customized design & surface level \\  & & Surgical planning & The increased success rate & It cannot determine the material \\  & & & of treatment & being scanned \\ \cline{1-1}  & & Reverse engineering & View detailed anatomy & It cannot transparent or \\ \cline{1-1}  & & Conduct architectural surveys & & from different angles & reflect objects \\ \cline{1-1}  & & Artists and art historians & Time and cost efficiency & We cannot show the inner details \\ \cline{1-1}  & & Artists and art historians & Improved learning and & Precision doubts in \\ \cline{1-1}  & & reduced risk & droplet placement \\ \hline \hline \end{tabular}
\end{table}
Table 1: IoT-enabled AM Applications, Advantages, and Limitations.
## 3 Results and Discussion
### Computer Simulation Results  
The simulated object is a phase object given by:  
\[O(x,y)=\exp[\mathrm{i}\cdot\varphi(x,y)], \tag{11}\]  
where \(\varphi(x,y)\) is the Peaks function in MATLAB. We set the maximum phase to be \(\pi\) radians. The three-dimensional phase distribution of the phase object is shown in Figure 1. The light wavelength \(\lambda\) is 632.8 nm, and the number of pixels in the recording CCD is 1024 \(\times\) 1024, with each pixel size being 4.65 \(\upmu\)m \(\times\) 4.65 \(\upmu\)m. The area of the object is 100 \(\times\) 100 pixels, and the distance from the object plane to holographic recording plane, \(d\), is 60 mm. All of the used parameters satisfy the sampling theorem, thereby avoiding aliasing issue [2]. We can successively construct the in-phase hologram, the quadrature hologram, and the complex hologram according to Reference [16] (see pp. 747-748 of the reference for details).  
We first present the reconstructed phase result with the algorithm of two-step-only quadrature phase-shifting holography proposed by [PERSON] and [PERSON] in Figure 2. Figure 2b contains the line traces based on the original phase (red curve) from Figure 2a and the reconstructed phase (blue curve). Clearly, the result is effective and precise. However, the process for finding the reference-wave intensity, \(R^{2}\), is relatively complex, and the computation is intensive due to the required iteration process. We have also performed simulations using our proposed fast method to retrieve the phase. Here, we set both \(R\) and \(\beta\) to be 1, so \(A\) is equal to 2. The reconstructed results, according to Equation (8), are presented in Figure 3. Figure 3a shows the three-dimensional distribution of the retrieved phase. Figure 3b presents the line traces based on Figure 3a and the same section of the original phase. From Figure 3, we can easily see that the correct phase could be retrieved using our proposed method, but with some slight deviation. That is because term \(A\) is in general space-variant and is not equal to a constant. In the actual experimental work, \(A\) should be found by averaging over its neighborhood to gain a better estimate of \(R^{2}\). Comparing Figure 2 with Figure 3, [PERSON] and [PERSON]'s method gives a better result, but the computation is intensive. On the other hand, the reconstructed phase using our proposed method is fast and satisfactory upon most occasions. It would be practically useful in some cases where the spatial bandwidth of objects is lower than that of the holographic system.  
Figure 1: Original phase distribution \(\varphi(x,y)\), where \(\varphi(x,y)\) is the Peaks function in MATLAB (**a**) Three-dimensional (3D) phase distribution; (**b**) Phase line trace along Y at X-pixel = 50 on Figure 1a.
## 3 Methodology
### Fuzzy Fault Tree Analysis
#### 3.3.2 Fuzzification  
The expert judgment rating can be assumed in linguistic terms, which are used for soliciting expert opinions for each BE. Studies have shown that the suitable number of linguistic term selections for humans to make appropriate judgments is between 5 and 9 [62]. In this study, a linguistic scale consisting of seven terms is used to solicit opinions from experts on BEs with unknown error rates. The linguistic scale used to evaluate the failure probability of BEs included the terms'very high', 'high','medium high','medium','medium low', 'low', and'very low'.  
After obtaining the expert linguistic terms of BEs, a triangular fuzzy number (TFN) is used to determine the occurrence probability values of BEs. A TFN represents a triple set of fuzzy probability values (\(a_{1}\), \(a_{2}\), \(a_{3}\)) of a BE. The corresponding relationship is shown in Table 2.  
The membership function of the TFN can be calculated as follows:  
\[\mu_{\text{A}}(x)=\left\{\begin{array}{ll}0&x\leq a_{1}\\ (x-a_{1})/(a_{2}-a_{1})&a_{1}\leq x\leq a_{2}\\ (a_{3}-x)/(a_{3}-a_{2})&a_{2}\leq x\leq a_{3}\\ 0&x\geq a_{3}\end{array}\right. \tag{7}\]  
where A is TFN (\(a_{1}\), \(a_{2}\), \(a_{3}\)), \(\mu\)A(\(x\)) is the membership function.
## 5 Results and Discussion
### Preparatory Works
For the position and temperature sensors, the collected calibration values were collected in increasing and decreasing progression, in order to take into account possible hysteresis effects. This effect does not occur in the tilt sensor, due to the measuring principle it uses. Figure 9c shows the measurements taken in the calibration of the inclination sensor, where the low measurement error produced can be observed, as well as its non-linear behavior, being higher for positive values of inclination.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
###### Abstract  
Battery degradation is a critical challenge in the operation and longevity of low-Earth-orbit (LEO) satellites because of its direct impact on mission reliability and power system performance. This study proposes a data-driven approach to accurately estimating the degradation of satellite batteries by integrating a transformer network model for voltage prediction and unscented Kalman filter (UKF) techniques for online state estimation. By utilizing on-orbit telemetry data and machine-learning-based modeling, the proposed method provides processing-time improvements by addressing the limitations of traditional methods imposed by their reliance on predefined conditions and user expertise. The proposed framework is validated using real satellite telemetry data from KOMPSAT-5, demonstrating its ability to predict battery degradation trends over time and under varying operational conditions. This approach minimizes manual data processing requirements and enables the consistent and precise monitoring of battery health.  
low-Earth-orbit (LEO) satellite battery; battery state of health (SOH); battery state of charge (SOC); deep neural network model +
Footnote †: journal: Applied Sciences  
1
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 4 Results and Evaluation
### Real-Time Implementation
During these predictions, except for the minor misclassification shown in the talking and texting class, the model produced state-of-the-art results with accuracies of 98.2% and 89.7% for day- and nighttime, respectively. Figure 10a,b illustrate the binary prediction as either non-distracted driving or distracted driving of driver 1 for daytime with RGB images and nighttime with IR images, respectively. The predictions reveal that the performance was significantly improved in both cases for a binary classification task, with accuracies of 99.6% and 97.9% for day- and nighttime, respectively. Figure 9: Real-time assessment results for multiclass classification driver 1: **(a)** RGB images in the daytime (accuracy of 98.2), **(b)** IR images in the nighttime (accuracy of 89.7). On the other hand, the proposed model achieved accuracies of 75.8%, 92.9%, and 74.8% during the daytime, while 80.8%, 88.3%, and 75.4% accuracies were achieved during the nighttime for driver 2, driver 3, and driver 4, respectively, in the real-time assessment. A more detailed result can be found in Appendix A, Figures A1 and A2. On average, the proposed model performance in real-time prediction was 85.4% and 83.6% during the daytime and nighttime, respectively, for multiclass recognition. Also, the average binary classifier performance was 98.7% and 97.2% for the daytime and nighttime scenarios, respectively. Based on the overall real-time prediction results, it can be concluded that the proposed model showed promising performance for both day- and nighttime scenarios even though there are few result fluctuations based on the type of driver. This is mainly because the drivers engaged in several non-driving activities in real-life driving different from the predefined driver activities studied in this work, and thus led the proposed system to slight misclassification. Here, it is worth noting that the model has achieved a state-of-art performance for the binary classifier that identifies whether the driver is in a non-distracted driving or distracted condition. This is very important in real-time practical use, as the main objective of building driver detection models is to give a warning whenever the driver is getting distracted regardless of which distracted activity is performed. The real-time prediction recorded video for driver 1, during both daytime and nighttime, can be found in the Supplementary Materials as Videos S1 and S2.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### Edge Environment Model
#### 3.1.2 Energy Model  
In the energy model, we consider the energy consumed with communication among edge nodes and with VNF processing. Concerning the energy consumption with communication, we used the propagation delay with the power for edge nodes to send (or receive) data to (from) other edge nodes. Thus, we denote the communication energy consumption between two edge nodes \(h_{i},h_{j}\in H\) in Equation (1), where \(p_{ij}\) is the power consumption in the communication between \(h_{i}\) and \(h_{j}\), and \(d_{p}(l_{ij})\) is the propagation delay between the edge nodes.  
\[E_{ij}^{cm}=p_{ij}\times d_{p}(l_{ij}) \tag{1}\]  
The energy consumption regarding the VNFs processing at the instant \(\tau\) by an edge node \(h\) is defined in Equation (2), where \(p_{h}^{IDLE}\) is the consumed energy in the idle state of an edge node \(h\) at time \(\tau\), \(p_{h}^{MAX}\) is the consumed energy in the full state, and \(u_{h}(\tau)\) is the utilization of edge node \(h\) at \(\tau\). Therefore, the energy consumed with processing in \(h\in H\) at \(\tau\) is defined in Equation (3).  
\[p_{h}(\tau)=p_{h}^{IDLE}+(p_{h}^{MAX}-p_{h}^{IDLE})\times u_{h}(\tau) \tag{2}\]  
\[E_{s,u}^{prc}=\sum_{v_{inst}^{ob}\in v_{inst}}\sum_{h\in H}p_{h}(\tau)\times \chi_{h}^{sur_{inst}},\forall v\in V,s\in S_{u},\forall u\in U \tag{3}\]  
We compute the energy consumption regarding the propagation delay of a user \(u\) and service \(s\) at time \(\tau\), as described in Equation (4).  
\[E_{s,u}^{p}=\sum_{v_{inst}^{ob}\in I_{c}}\sum_{l_{ij}\in L}d_{p}(l_{ij})\times p _{l_{ij}}\times y_{l_{ij}}^{v_{inst}^{ob}},\forall v\in V,s\in S_{u},\forall u \in U \tag{4}\]
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
###### Abstract  
Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there's no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.  
mmWave communication system; AF relay; hybrid beamforming; manifold optimization; channel state information +
Footnote †: journal: Applied Sciences
## 4 Dynamic Error Propagation Model in UWB-Based Positioning  
### UIWB Signal Generation  
The amplitude of the received UWB signal decreases greatly with time after indoor path loss. When the obstacle blocks the signal diameter propagation between the UWB tag and the base station, the signal amplitude of the first path will suffer serious losses, which is called non-line-of-sight (NLOS) propagation. In NLOS, there is a time delay, which is denoted as \(\tau\). The distance will be bigger than the actual distance because of the time delay \(\tau\). In the indoor environment, UWB signals are prone to be affected by different types of obstacles during signal transmission, resulting in a non-linear transmission phenomenon.  
Most of the research shows that the indoor path loss follows a logarithmic path distance loss model:  
\[PL(d)=\left[PL_{0}+10\gamma Log_{10}\left(\frac{d}{d_{0}}\right)\right]+S(d), \ d>d_{0} \tag{6}\]  
where \(PL_{0}+10\gamma Log_{10}\left(\frac{d}{d_{0}}\right)\) represents the signal loss in the line-of-sight (LOS) case, \(\gamma\) denotes the loss index, and \(d_{0}\) denotes the reference distance. \(PL_{0}\) is defined as:  
\[PL_{0}=20 Log_{10}\left(\frac{4\pi\sqrt{f_{1}f_{2}}}{0.3}\right) \tag{7}\]  
where \(f_{1}\) and \(f_{2}\) represent the frequencies in the radiation spectrum at the \(-10\) dB edge. \(S(d)\) denotes the signal loss under NLOS conditions.  
The phenomenon that causes signal superposition is called the multipath effect. As shown in Figure 8, a signal is transmitted along the direct path(black line) from Node 1 to Node 2, and it's refracted signal is also transmitted along two possible reflection paths(red or blue dash lines) to Node 2--if the signal is superimposed in the same direction, multipaths have little effect on the decoding of the signal. However, if the length of the two paths is exactly half a wavelength apart at this point, then the two signals eliminate each other exactly when they reach Node 2, which distorts the signal and prevents the receiver from decoding the signal.  
According to the analysis above, the multipath effect is finally reflected in the superposition of signals under different paths. Signals received from different paths have been subjected to path loss and shadow fading in the propagation process. If a uniform line array is adapted to receive at the receiving terminal, the received signal is as follows [42]:  
\[y^{l}(t)=\sum_{i=1}^{M}\sum_{m=1}^{Q}\alpha_{i,m}x_{i}\left(t-\tau_{i,m}+\frac {(l-1)d}{c}sin\theta_{i,m}\right)+n^{l}(t) \tag{8}\]  
where \(\alpha_{i,m}\) is the amplitude; \(\theta_{i,m}\) is the angle of arrival; \(\tau_{i,m}\) is a time delay; \(l\) is an array element; \(x_{i}\) is the \(i\)th transmitting signal; \(c\) is the speed of propagation; \(d\) is the spacing of  
Figure 7: UWB signals received after indoor multipath CM4 fading.  
an array element; \(M\) is the number of information sources; \(Q\) is the number of multipath components in a cluster; \(n^{l}(t)\) is the Gaussian white noise whose mean value, which is 0; and the power spectrum is \(N_{0}\).
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 2 Smoothing Method Based on Angle and Deep Q-Network
### Workflow of the Algorithm  
#### 2.3.1 Overview  
In this paper, we propose a planar Delaunay mesh smoothing algorithm based on angle and a deep Q-network, which is obtained by constructing an inner polygon based on the mesh smoothing method of a deep Q-network, and the algorithm consists of three main steps. (1) The first step is to extract the coordinates of all nodes in the planar mesh dataset. The extracted points include the central, neighboring, and inner polygon nodes. (2) The second step is to determine the information of the dataset, train the features using the deep Q-network, add the features of the inner polygon as constraints, and output the  
Figure 3: The construction process of the inner polygon. (**a**) The initial state of a polygon; (**b**) rotation angle and updated central node; (**c**) updated scatters; (**d**) convex hull processing for scattered points falling inside the polygon; and (**e**) the inner polygon constructed.  
constrained DQN model after the training is completed. (3) The third step is to smooth the mesh according to the policy of constraining the DQN model. Each smoothing requires resetting the points that have been moved out of the inner polygon to the centroid of the inner polygon and continuing to smooth them according to the policy in the model.  
The entire workflow of the proposed algorithm is illustrated in Figure 4.