esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Applications of Machine Learning in Educational Settings  
Machine learning (ML) has emerged as a cornerstone technology in contemporary educational settings, reshaping the landscape of learning and teaching methodologies [8]. The efficacy of ML lies in its ability to analyze extensive datasets, extracting patterns and insights that are imperceptible to the human eye [9]. This capability finds its application in several areas within the educational sphere. One of the primary areas is the development of personalized learning environments [10]. Here, ML algorithms assess individual student's learning patterns, preferences, and performances, enabling the creation of customized educational content that matches their unique learning trajectories.  
Another significant application of ML in education is the automation of administrative tasks [11]. Tasks such as grading and assessment traditionally consume considerable time, and resources are now being streamlined through ML algorithms. This enhances efficiency and gives educators more time to focus on interactive and student-centered teaching.  
ML has revolutionized the domain of predictive analytics in education [12]. By analyzing past student performance data, ML algorithms can predict future learning outcomes and identify potential academic risks. This foresight enables educators and institutions to intervene early, providing targeted support to students who might be at risk of underperforming [13].  
ML contributes to the evolution of adaptive testing mechanisms. These systems adjust the difficulty level of tests based on the student's responses, ensuring a more accurate assessment of their knowledge and skills. Such adaptive tests are crucial in understanding each student's mastery of subjects, allowing for more effective and targeted educational strategies [14].  
Integrating ML in educational tools has facilitated more engaging and interactive learning experiences. Gamified learning environments, interactive simulations, and virtual labs powered by ML algorithms offer students an immersive and hands-on learning experience, significantly enhancing their engagement and knowledge retention [15].  
In essence, machine learning applications in educational settings are vast and varied, each contributing to a more effective, efficient, and personalized learning experience. As ML technology continues to advance, its role in shaping the future of education is both significant and indispensable.  
In the context of the aforementioned applications of ML in education, Figure 2 visually encapsulates the diverse and transformative roles that ML plays within the educational ecosystem. The diagram illustrates the flow from data processing to tailored educational interventions, encapsulating the multifaceted impact of machine learning on the educational experience.
## 2 Lumped Parameter Model of V-Shaped IPMSM
### Q-Axis Inductance  
The solving of q-axis inductance goes in the same way as in d-axis inductance with saturated core in d-axis path due to PM. Q-axis magnetic path is shown in Figure 5. It is turned out in Figure 2 that the entire path is saturated by PM. Therefore, the affection of PM should be considered as in d-axis inductance. Since q-axis MMF does not neutralize d-axis PM flux linkage and is orthogonal to d-axis path, core saturation caused by q-axis flux is simply added to core saturation from d-axis flux. With this fact, Q-axis MEC can be solved using the same sequence in Figure 4.  
\[F_{q\text{sn}}=\frac{3}{2}\frac{4}{\pi}\frac{N_{a}k_{w}}{p^{2}}I_{q\text{m}} \tag{13}\]  
\[F_{q\text{sbr}}=\frac{1}{\alpha_{k}-\alpha_{k-1}}\int_{\alpha_{k-1}}^{\alpha_ {k}}F_{q\text{sn}}\sin(\theta)\;d\theta,\;k=1,\;2 \tag{14}\]  
\[\Phi_{q\text{sn}}=\frac{F_{q\text{sbr}}}{R_{q\text{sbr}}},\;\lambda_{q\text{m} }=N_{a}k_{w}\Phi_{q\text{m}} \tag{15}\]  
\[L_{q\text{m}}=\frac{\lambda_{q\text{m}}}{I_{q\text{m}}} \tag{16}\]  
where \(I_{q\text{m}}\): \(n\)-th q-axis current, [A], \(F_{q\text{sbr}}\): fundamental amplitude of MMF on airgap at \(I_{q\text{m}}\), [A], \(F_{q\text{sbr}}\): values in \(k\)-th MMF source at \(I_{q\text{m}}\), [A], \(\Phi_{q\text{sbr}}\): flux passing through \(k\)-th MMF source at \(I_{q\text{m}}\), [Wb], \(R_{q\text{sbr}}\): equivalent reluctance obtained from [PERSON]'s voltage law, [H\({}^{-1}\)], \(\lambda_{q\text{m}}\): flux linkage at \(I_{q\text{m}}\), [Wb], \(L_{q\text{m}}\): q-axis inductance at \(I_{q\text{m}}\), [H]. Given Equations (13)-(16), relatively low inductance can be estimated provided that magnet cavity is thicker or longer with the narrow included angle. Thicker and longer cavity will make the area of flux path narrow and extend the length so that the rotor yoke can be saturated easily larger amount of PM, and the reluctances grow totally higher. Therefore, if one designs narrow angle of cavity in which to insert more interior PM, it will have low inductance along q-axis current, \(I_{q}\).
## 3 A Fast Prediction Model for the Coupling Coefficient
### Prediction Results and Errors  
Electromagnetic simulations are performed to obtain the electric field strength at the window and the LTE-R fixed antenna on the roof. After data preprocessing, a nonlinear relationship between the coupling coefficient and the spatial coordinate, frequency, and average electric field strength at 400 MHz-500 MHz at the window and the roof is established. The prediction output, the prediction error of the proposed cascade network model based on the PSO-BP algorithm, is shown in Figure 9. The prediction output and prediction error are also compared with those of a single BP neural network model or a single RBF (Radial Basis Function) neural network model. From Figure 9a, the proposed cascade PSO-BP model is closest to the expected output. Further, according to Figure 9b and Table 3, the maximum error of the predictions of the three network models is shown: the BP and RBF neural network models have relatively large errors, reaching 9.8 dB and 15.2 dB, respectively. The prediction accuracy of the two models, in this case, needs to be improved. Therefore, the neural network model is modified and the proposed cascade PSO-BP neural network is introduced, which effectively reduces the prediction error to 3.6 dB. The accuracy of the prediction results is significantly improved, which meets the uncertainty requirements in measurement. Based on the predicted coupling coefficient, the disturbance electric field strength at the roof antenna can be calculated from the electric field strength at the window position, and the electromagnetic response coupled to the antenna port when the pantograph arcing occurs can be quickly predicted.  
Analysis of the Electromagnetic Interference Induced by the Coupling of the Pantograph Arcing to the LTE-R Roof Antenna  
In this section, the model of the LTE downlink physical layer is simulated in MATLAB, and simulation metrics including throughput and block error rates are introduced. A detailed analysis of the impact of the pantograph arcing interference on LTE-R communication quality has been performed and verified by field measurements.
## 6 Conclusions  
This study, based on the construction project of the ventilation tunnel in the Wuhai Pumped Storage Power Station, employs TGP sidewall forecasting and wave velocity-based rock mass parameter predictions to systematically investigate the mechanical characteristics of the surrounding rock during construction. The following conclusions are drawn:  
Figure 12: Distribution of the plastic zone: (**a**) S20, (**b**) S21, (**c**) S24, (**d**) S25, (**e**) S26, and (**f**) S30.  
1. TGP side wall prediction can provide the more accurate location of joint fractures, compensating for the deficiencies in geological surveying.
2. The cumulative deformation of the ventilation tunnel during the excavation and expansion phases accounts for more than half of the total deformation. It is advised to enhance monitoring during excavation, promptly reinforce support, and implement advanced support when necessary.
3. During expansion, there is a certain degree of stress concentration in the shoulder and bottom corner. Strengthening the monitoring of the shoulder and bottom corner during construction is recommended. If the surrounding rock in certain construction sections of the shoulder and bottom corner is relatively fragmented, enhancing the rock mass strength by injecting grouting anchors is suggested.
4. Prior to expansion, the plastic zone primarily concentrates within the ventilation tunnel outline. Post-expansion, the plastic zone predominantly focuses on the sidewall and shoulder, influenced by the inclined structure of the surrounding rock. The expansion range of the left-side plastic zone is slightly larger than the right side. It is advised to enhance monitoring during construction and control plastic zone expansion by increasing the anchor rod density when necessary.
5. This study primarily investigates the mechanical characteristics of surrounding rock during construction by estimating the rock mass mechanical parameters using wave velocity. However, the information concerning the structural planes in geological forecasting has not been incorporated into the numerical model. Therefore, considering the influence of structural planes on the stability of surrounding rock is the next step that needs to be carried out.  
Conceptualization C.F., G.W. and J.Q.; methodology C.F.; formal analysis C.F. and G.W.; resources G.W.; data curation C.F. and G.W.; writing--original draft C.F.; writing--review and editing J.Q.; visualization C.F.; supervision J.Q.; project administration G.W.; funding acquisition [PERSON] and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U1839202, Foundation of Key Laboratory for Prediction and Control on Complicated Structure System of the Education Department of Liaoning Province, grant number DLSZD2023[010] and the APC was funded by J.Q. and G.W.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Proposed Methodology
#### 2.1.4 Spatiotemporal Kriging  
The spatial kriging can be extended to obtain spatiotemporal predictions by incorporating an additional temporal dimension, \(t\), to the spatial kriging [4; 17; 18; 20].  
The spatiotemporal set of observed data are given by \(\{Z(X_{1},\ t_{1}),\ Z(X_{2},\ t_{2}),\ \ldots,\ Z(X_{n},\ t_{n})\}\), and let \(Z_{i}=Z(X_{i},\ t_{i})\) be the value of the variable \(Z\) at location \(X_{i}\) and time \(t_{i}\). The spatiotemporal kriging prediction at an unobserved location \(X_{0}\) at time \(t\) is defined by:  
\[\hat{y}_{f}=\hat{Z}(X_{0},\ t)=\sum_{i=1}^{n}w_{i}(X,\ t)(Z(X_{i},\ t_{i}))\ \ \text{ subject to}\ \sum_{i=1}^{n}w_{i}(X,\ t)=1 \tag{14}\]  
where \(w_{i}\) are weights for \(Z(X_{i},\ t_{i}),\ i=1,\ 2,\ \ldots n\). Following the study in [18], the optimal prediction is taken by minimizing the mean squared prediction error subject to the constraint:  
\[E\big{[}\hat{Z}(X_{0},\ t)-Z(X_{0},\ t)\big{]}=0 \tag{15}\]The kriging weights can be computed through the covariance function or by means of a semivariogram. Once the covariance values are calculated, the optimal weights of the kriging estimator can be computed through Lagrange multiplier application. Finally, the optimal value of \(\hat{Z}(X_{0},\ t)\) is calculated for spatiotemporal location \((X_{0},\ t)\)[18].  
The spatiotemporal covariance function has different models such as linear, exponential, spherical, Gaussian, or logistic. Capturing the spatial and temporal variability is difficult for the variogram due to their dissimilarities. Therefore, the two-dimensional kriging is modified to incorporate the time component. The type of variogram (metric, separable, SumMetric, and product sum models) varies according to the way it combines and models the temporal and spatial dependencies. The best-fitting model can be identified by comparing the mean squared errors (MSE) between the sample variogram and fitted variogram.
## 2 Materials and Methods
### Rheological Properties  
#### 2.3.1 Evaluation of Sludge Consistency  
In literature, several mathematical models are available in order to describe the rheological properties of BS [33]. Most of these models adopt a power-low curve that includes an offset to account for the yield stress (\(\tau_{0}\)). When considering thermophilic biological sludge from a membrane system [34], the Herschel-Buckley model (Equation (1)) [35] can provide an adequate representation of the experimental data because BS is characterized by a high concentration of solids [36]. Denoting with \(\tau\) the shear stress and with \(\dot{\gamma}\) the shear rate, the model reads:  
\[\tau=\tau_{0}{}^{HB}+k\dot{\gamma}^{n} \tag{1}\]  
where \(k\) and \(n\) represent the consistency coefficient and the flow behaviour index, respectively. When \(n\) equals to 1 a Newtonian behaviour is obtained, \(n\) lower than 1 indicates pseudoplastic properties, and \(n\) higher than 1 indicates a dilatant behaviour. The more \(n\) differs from 1, the more fluid deviates from Newtonian properties. \(\tau_{0}^{HB}\) indicates the yield stress.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter (u.m.)** & **Value** & **Analytical Method** \\ \hline \multicolumn{3}{c}{_Rheological Properties_} \\ \hline k (–) & 0.012–0.22 & - \\ n (–) & 0.51–1.1 & - \\ \hline \multicolumn{3}{c}{_Physico-Chemical Properties_} \\ \hline pH (–) & 7.5–8 & [24] \\ TS (kg m\({}^{-3}\)) & 175–185 & [25] \\ VS (kg m\({}^{-3}\)) & 85–95 & [26] \\ NVS (kg m\({}^{-3}\)) & 85–95 & * \\ COD (mg L\({}^{-1}\)) & 180,000–190,000 & [27] \\ Not (mg L\({}^{-1}\)) & 4000–5000 & [28] \\ Ptot (mg L\({}^{-1}\)) & 45–50 & [29] \\ N-NH\({}_{4}\)\({}^{+}\) (mg L\({}^{-1}\)) & 150–160 & [30] \\ N-NO\({}_{3}\)\({}^{-}\) (mg L\({}^{-1}\)) & 250–300 & [31] \\ N-NO\({}_{2}\)\({}^{-}\) (mg L\({}^{-1}\)) & 40–55 & [32] \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of unconditioned TBS. u.m.: unit of measure; (*): calculated as a difference between TS and VS.  
For every different dosage of diverse type of NVS, the estimated values \(k_{i}\) and \(n_{i}\) of Herschel-Buckley model were related to the corresponding parameters \(k_{0}\) and \(n_{0}\) of unconditioned sludge.
## 4 Model Building  
After data preprocessing in the previous section, the original dataset was processed into three parts: temporal vehicle trajectory data, the vehicle relationship matrix, and road map data. Then, the overall structure of the framework of our trajectory prediction model is shown in Figure 3:  
The overall model is divided into VGAE and the main trajectory prediction model based on CVAE. Since it is necessary to generate individual feature vectors based on the perspective of each driver, the input of VGAE includes both map data and vehicle trajectory data. The main model consists of two modules. The first one is predicts the driver's destination, and then the second fits the complete trajectory based on the most probable destination. In order to fully consider road information during the training process, inputs of the destination prediction module will also include map features extracted by VGAE. Vehicle interactions are only considered in the trajectory fitting module. This design of the main model is intended to reflect the fact that a driver's final destination will not be significantly altered by the presence of surrounding vehicles, but their specific driving details are greatly influenced by them.
# Novel Fault Injection Attack without Artificial Trigger
## 1 Introduction  
In 1996, [PERSON] demonstrated that confidential information could be stolen through various physical signals, such as operation time, power consumption, or electromagnetic(EM) emission, occurring when cryptographic algorithms proved to be a mathematically secure were processed on a real device [1]. A side-channel attack is defined as an analytical method to obtain confidential information using the side data from a real device. [PERSON] et al. proposed the concept of a fault injection attack, corresponding to side-channel attack methods, in 1997 [2]. Fault injection attacks imply injecting artificial faults to induce malfunctions in a device on which a specific algorithm is operated and using the resulting incorrect output to steal the confidential information. Differential fault analysis (DFA) is a method to reveal the confidential information using the difference characteristics and differential between the normal ciphertext and the one obtained as a result of injecting a fault. Existing injection attack methods for the advanced encryption standard (AES) relied on various attacker assumptions. [PERSON] et al. proposed a DFA method for AES considering an attacker's assumption that a fault was injected into the input byte of the MixColumns function in the 9 th round of AES [3]. The above AES DFA method is called the PGO ([PERSON], [PERSON], and [PERSON] who are authors of the paper) DFA method. Then, [PERSON] and [PERSON] proposed a DFA method of AES implying injecting a fault into one arbitrary byte of the key scheduling process, which was considered as an attacker's assumption [4]. In addition, several research works were focused on using a bit stuck model or a bit-flip model [5; 6]. However, all these attacker's assumptions were deemed unrealistic concerning real devices.  
Fault injection attack systems usually utilize artificial and unnatural triggering to easily obtain fault-injected ciphertexts which are then used in DFA. The attacker needs to revise the code incorporated into the target device to generate an artificial trigger. If a target device is a complete product, revising its code is not suitable for actual scenarios.
## 3 Numerical Analysis and Simulation Calculation
### Construction of Temperature Rise and Thermal Elongation Testing System  
Figure 33 presents a schematic of the testing scenario. In most cases, the hardware of the detection system consists of temperature sensors (T1, T2, T3, and T4), transmitters,  
Figure 31: Steady-state thermal error of screw node 24 (or 27).  
Figure 32: Steady-state thermal error of screw node 25 (or 26).  
data acquisition cards, micro-displacement sensors (W1), etc. To detect the temperature of the proximal bearing, temperature sensor T1 is installed near the screw motor bearing, while temperature sensor T2 is installed far away from the screw motor bearing to detect the temperature of the remote bearing; in addition, temperature sensor T3 is installed at the flange of the nut assembly to detect the temperature of the nut assembly and temperature sensor T4 is adopted to detect the ambient temperature. Micro-displacement sensor W1 employs the Keens LK-031 laser micrometer to measure axial thermal elongation at the end of the screw, with a maximum measurement length of 1 mm, a transmission output of \(-\)5-5 V, and a resolution of 0.1 \(\mu\)m.  
Based on the temperature measurement requirements of the machine tool as well as the measurement range and accuracy requirements, the temperature sensor takes a Pt100 thermistor and a matching temperature transmitter, with a temperature measurement range of 0 \({}^{\circ}\)C-100 \({}^{\circ}\)C and a transmission output of 0-5 V. An advantech USB-4711A multi-channel data acquisition card with 16 analog input channels, 12-bit resolution, and a maximum sampling rate of 150 k/s is selected.  
Figure 34 presents the experimental setup. According to a previous publication by the author of this paper [33], the minimum speed of both motors is no less than 60 RPM, and the speed difference between the two is no less than 36 RPM to ensure the stable output speed of the dual-drive system table. During the temperature rise and thermal deformation experiment, the nut motor speed was set to 180 RPM, and the screw motor speed was set to 216 RPM. At this speed, the synthetic feed rate of the table was 3 mm/s. The dual-drive system was operated for a sufficient amount of time. The temperature increases at the near-end bearing of the screw motor, the far-end bearing of the screw motor, the flange of the nut component, and the thermal deformation at the end of the screw were detected through temperature sensors, transmitters, data acquisition cards, and micro displacement sensors.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### The Algorithm  
A computer equipped with a 12 th Gen Intel(R) Core(TM) i9-12900 KF 3.20 GHz processor, 32.0 GB RAM, and 16 GB GPU was used to perform the research work. The Self-Organizing map was written in Python code, and several libraries were used. NumPy and Pandas packages were needed to manipulate the data, while the MiniSom library was used for map creation and the Bokeh package for graphical representation. The MiniSom Function appears as follows:  
\[\text{SOM}=\text{MiniSom}(\text{x},\,\text{y},\,\text{input}\,\text{len},\, \text{topology},\,\text{sigma},\,\text{learning}\,\text{rate},\,\text{neighborhood}\,\text{function},\,\text{seed}) \tag{27}\]  
where x and y are the numbers of rows and columns of the output grid, input len is the length of the database supplied to the network, topology defines the topology of the grid (rectangular or hexagonal), sigma is the initial radius of the neighbourhood, learning rate represents the \(\alpha(0)\), neighborhood function is Equation (10), and the seed is the seed of the network. The readings from the steady-state test and all the Level 3 anomalies are collected in a database of 5321 samples. To improve readability and ensure a homogeneous output, the number of rows and columns in the output map was set to be equal, resulting in a square map. Applying the formula explained in Section 3.2 to the spatial dimension of the map, it is also possible to calculate these last two parameters. There are 18 rows and columns of the output map. In function (27), the hexagonal topology is set for the grid cells, the inverse learning rate function is chosen (Equation (5)), the Gaussian neighbourhood function is imposed (Equation (10)), and the seed is also set to 0 to realize an isotropic, repeatable map that preserves the initial topological relationships. The algorithm is hyper-parametrized for the optimal choice of parameters characterizing the learning rate and neighbourhood size. In Equation (14), \(\mu\) is set to 4 for both linear distributions. Therefore, the range \([1,\,\,5\times 10^{\mu}]\) in Equations (15) and (16) is composed of 50,000 values, and the algorithm tests the SOM network by combining all possible values between these two distributions.