esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Results
### Descriptive Statistics  
To develop a better understanding of the attributes, it is required to construct summary statistics for all the features contained in the dataset used in this study. Table 2 shows the outcomes.  
From the over 4601 customer samples used in this research study, the number of referrals is 1.95, with an SD of 2.96, which means that, on average, each customer referred this telecom company to two friends. The average download volume in gigabytes to the end of the second quarter is 26.12 GB, and the customer's total charges for additional data downloads for the same quarter is 8.94, with an SD of 28.62. Table 2 also shows details of the customers' charge history, where the average values are 3042.59, 2.16, 8.94, and 888.91 for total charges, total refunds, total extra data charges, and total long-distance charges, respectively.  
The bar chart presented below was computed to examine the distribution of the target variable across the customer service call variable. It can be observed from the chart that the dataset is unbalanced, with almost twice as many churning samples than not churning samples. See Figure 3.  
To examine the inter-correlation among the features, a correlation matrix was computed and displayed in the form of a heatmap to indicate the pairwise relationship among the call activities and features of the telecom customers. See Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Count** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
**Age** & 4601 & 47.89307 & 17.362 & 19 & 33 & 47 & 62 & 80 \\ \hline
**Number of Dependents** & 4601 & 0.380569 & 0.880541 & 0 & 0 & 0 & 0 & 8 \\ \hline
**Number of Referrals** & 4601 & 1.947403 & 2.957352 & 0 & 0 & 0 & 3 & 11 \\ \hline
**Tenure in Months** & 4601 & 34.63117 & 24.19849 & 1 & 12 & 32 & 58 & 72 \\ \hline
**Avg Monthly** & 4601 & 25.5811 & 14.26574 & 1.01 & 13.02 & 25.84 & 37.97 & 49.99 \\
**Long-Distance Charges** & 4601 & 26.12889 & 19.53716 & 2 & 13 & 21 & 30 & 85 \\ \hline
**Monthly Charge** & 4601 & 81.20272 & 21.14967 & \(-\)10 & 69.9 & 83.75 & 96.2 & 118.75 \\ \hline
**Total Charges** & 4601 & 3042.595 & 2391.057 & 42.9 & 847.3 & 2564.3 & 4968 & 8684.8 \\ \hline
**Total Refunds** & 4601 & 2.163306 & 8.286778 & 0 & 0 & 0 & 0 & 49.57 \\ \hline
**Total Extra Data Charges** & 4601 & 8.943708 & 28.62071 & 0 & 0 & 0 & 0 & 150 \\ \hline
**Total Long-Distance** & 4601 & 888.9163 & 866.5069 & 1.13 & 178.89 & 582 & 1417.92 & 3536.64 \\
**Charges** & 4601 & 3938.291 & 3054.189 & 46.92 & 1119.4 & 3378.79 & 6412.05 & 11979.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of descriptive statistics.
## 4 \(p\)-Type Dopants  
In Ge, the diffusion of B is slow, and this is a consequence of its very high diffusion activation enthalpy, which is higher than the activation enthalpy of self-diffusion by more than 1 eV [46]. As the dominant intrinsic defect is the Ge vacancy with the higher activation energy of the B atom as compared to the vacancy self-diffusion indicates that it is not bound to a vacancy when it migrates. This is also confirmed by the DFT calculations, which show that the boron atom repels the vacancy [36]. This is the opposite picture to the donor atoms where diffusion is facilitated by vacancies with the donor-vacancy pairs being highly bound [36]. For B in Ge, the interstitial-mediated diffusion mechanism is proposed,  
Figure 2: The determined arsenic concentration profiles with respect to depth for arsenic-doped (blue line \(3\times 10^{13}\) As/cm\({}^{2}\) with 50 keV implantation energy) and fluorine + arsenic-co-doped (red line As: \(3\times 10^{13}\) As/cm\({}^{2}\) with 50 keV implantation energy; F: \(1\times 10^{15}\) F/cm\({}^{2}\) with 35 keV implantation energy) germanium [38].  
and this is consistent with the DFT investigations of [PERSON] et al. [48] and by the enhanced diffusivity of B under irradiation (where there is a self-interstitial supersaturation) [83].  
Conversely, the diffusion of \(p\)-type dopants such as Al, Ga, and In is mediated by vacancies. These \(p\)-type dopants have a higher activation enthalpy of diffusion as compared to \(n\)-type dopants [36; 42]. The main difference between the acceptor and donor atoms is that the latter have a higher attraction to the vacancies (i.e., Coulombic attraction of the positively charged \(n\)-type dopants to the negatively charged vacancies) and this is, in turn, reflected in the activation enthalpies of diffusion [42; 43; 18; 44]. For example, [PERSON] et al. [18] determined that indium diffusion in germanium is vacancy mediated but with a relatively high activation enthalpy of diffusion of 3.51 eV (under intrinsic conditions).  
It has been determined that for high-dopant concentrations (i.e., at \(\sim\)10\({}^{20}\) cm\({}^{-3}\)), there is a formation of dopant-defect clusters [44]. These clusters in turn will impact \(p\)-type dopant diffusion as in the case of analogous clusters for \(n\)-type dopants (refer to Figure 1 and References [4; 44]); however, the impact on the activation enthalpy of migration has not been calculated using advanced computational methods.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Virtual Tutors and Chatbots  
#### 3.2.1 Introduction to Virtual Tutors and Chatbots  
Virtual tutors and chatbots are AI-driven systems designed to simulate human tutoring and conversational interactions in educational settings. They provide learners with personalized assistance, feedback, and guidance, often available on-demand and scalable to large numbers of users [49]. These systems aim to enhance learning experiences by offering interactive dialogues, answering queries, and adapting to individual learner needs.  
The integration of multimodal large language models (MLLMs) has significantly advanced the capabilities of virtual tutors and chatbots. By leveraging MLLMs, these systems can process and generate content across multiple modalities--such as text, speech, and images--enabling richer interactions and more effective communication [50]. This multimodal capability aligns with the diverse ways in which learners perceive and engage with educational content.  
Recent advancements in MLLMs, such as the development of LOVA3 (_Learning to Visual Question Answering, Asking, and Assessment_) [51], have further enhanced the potential of virtual tutors and chatbots. LOVA3 equips MLLMs with additional capabilities beyond traditional question answering, enabling them to ask questions and assess responses. This aligns closely with human learning mechanisms and supports deeper multimodal understanding, making virtual tutors and chatbots more effective in educational contexts.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 5 Signal Processing Framework
### Spread Spectrum Signature Code Selection  
As introduced in section V-B, signature codes are used to separate signals from different tags. However, it is difficult to find a group of codes that guarantee zero cross-correlation with all different time offset \(\Delta t\). In other words, (18) cannot be guaranteed to be zero with all possible \(\Delta t\) values. It is preferred to have \(f(\Delta t)\) as small as possible compared to \(L\). In this paper, the signature codes are a randomly generated set of pseudo-random sequences which have  
\[\max_{\Delta t\
eq 0}(f(\Delta t))/L\approx 1/4 \tag{26}\]  
with different lengths from 8 bits to 64 bits. If the signal power from two tags is different due to distance, one signal's power is less than 1/4 of the other one, then only one tag's data can be recovered. Note that this ratio can be further reduced when a suitable signature code is selected. With this group of signature codes, increasing the length of the code doesn't decrease the ratio between \(\max(f(\Delta t))\) and \(L\) significantly. However, increasing the length of the signature code chip can increase the SNR. As a result, the EVM reduces as the length of the signature code increases. A simulation result of the EVM of recovered signal from two tags with different chip lengths and SNR is shown in Figure 8. The chip rate is 5 Gbps. With the increase of chip length, higher signal power is obtained at the receiver side. As a result, the EVM decreases.  
Figure 7: Simulated constellation diagrams of \(RX_{1}{{}^{\prime\prime}}\) with \(\Delta f_{2}-\Delta f_{1}\) of (a) 250 Hz and (b) 500 Hz.
## 4 Fatigue Wear Test
### CV Signal Analysis Method
#### 4.2.2 Extraction of CV Eigenvalues  
Figure 10a shows the CV and bearing load signals. It can be seen from the bearing load signal that the bearing had an alternating load. In addition, the compression peaks and the tensile troughs appeared at 45\({}^{\circ}\) CA to 135\({}^{\circ}\) CA and 225\({}^{\circ}\) CA to 315\({}^{\circ}\) CA, respectively, which corresponded with the maximum compressive load and maximum tensile load of the connecting rod. Several studies have shown that the area where the bearing load is heavier is the easily wear-prone area [26], and the CV signal is more sensitive to the wear area of the bearing [27]. Figure 10b shows the CV signal and the oil film thickness signal. The moment when the oil film thickness is smaller corresponds with the peak value of the CV signal. When the oil film thickness is small, there is asperity contact between the bearing and the shaft, which leads to an increase in the CV signal.  
Figure 9: CV signal of copper alloy bearing (1500 r/min, 10 MPa).  
Therefore, it can be considered that the CV value increases at the time when the bearing wears out. Considering that the average value reduces the contingency of the peak value, the average values of 45\({}^{\circ}\) CA to 135\({}^{\circ}\) CA and 225\({}^{\circ}\) CA to 315\({}^{\circ}\) CA are extracted as the characteristic value CV signals, which are the tensile eigenvalues and the compression eigenvalues, respectively.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 4 Numerical Simulations
### Seismic FWI Model  
For FWI simulations, a simple spherical inclusion model, as used by [12] for acoustic FWI is considered. A rectangular homogeneous elastic region in range \(x=(0,15)\) m and \(z=(0,30)\) m in a homogeneous full-plane with density \(\rho=1500\) kg/m\({}^{3}\) and wave velocities \(c_{1}=500\) m/s, \(c_{2}=300\) m/s with a circular inclusion of radius \(r=3\) m at the center of the grid with density \(\rho=1700\) kg/ m \({}^{3}\) and wave velocities \(c_{1}=800\) m/s, \(c_{2}=400\) m/s is considered (Figure 13). Three seismic sources are used at coordinates \(x=1\) m and \(z=7.5\) m, \(15\) m, \(22.5\) m. Fifty five receivers are used at \(x=14\) m and between \(y=1.5\) m to \(y=28.5\) m in interval of \(0.5\) m, as shown in Figure 13. The model is discretized  
Figure 12: Horizontal component of particle velocity \((v_{x})\), normalized to the velocity wavelet at the excitation point, in the forward seismic model.  
with \(nx=301\) and \(nz=601\) number of grids in x- and z-directions, respectively, with grid spacing \(dx=dz=0.05\) m. 10 convolutional PML layers are used in all four boundaries.  
A synthetic data set is considered as observed seismic records. A Ricker wavelet with peak frequency 2.50 kHz and amplitude 1.0 as shown in Figure 14, are fired simultaneously as input velocities in the z-direction, and the observed data is generated by the forward modelling of the true model. A seismic FWI simulation is performed with the developed parallel codes. The start model is a homogeneous full plane with the same material properties as the true model without spherical inclusion. The material updates in different iteration steps are shown in Figures 15 and 16 for longitudinal and shear wave velocity models, respectively. The convergence of the inversion model is represented by the change in L2-norm over the iteration steps, shown in Figure 17. The obtained results show a fair inversion of the given true model.  
Figure 14: Ricker wavelet of unit amplitude with center frequency 250 Hz used as velocity source for the excitation in FWI model: (**a**) normalized amplitude in time domain, (**b**) normalized amplitudes in frequency domain.  
Figure 13: A spherical inclusion in a rectangular finite difference grid considered for FWI simulation.  
Figure 16: Shear wave velocity inversion at different iteration steps in seismic FWI of a spherical inclusion in full-plane.  
Figure 15: Longitudinal wave velocity inversion at different iteration steps in seismic FWI of a spherical inclusion in full-plane.
## 3 Results
### Analysis for Scenario without a Dielectric Layer
This density of resonances causes them to overlap, which makes it difficult to recognize the shift. In Figure 8b it can be seen that the resonant frequency for Q = 0 \(\upmu\)m, and U = 8 \(\upmu\)m is 1.987.5 THz. It increases at Q = 1 \(\upmu\)m and Q = \(-\)1 \(\upmu\)m with frequency equal to 1.989 THz. Then the frequency fell to 1.981 THz with Q = \(-\)3 \(\upmu\)m and Q = 3 \(\upmu\)m. Moving toward the maximum position, the resonant frequency drops to 1.97 THz but has a small increase at Q = 4 \(\upmu\)m and Q = \(-\)4 \(\upmu\)m with frequency equal to 1.984 THz.
## 6 Results
### PCE Measurement and Simulation Results  
To validate our PCE model, we measured the normalized PCE of the multisine PSK modulated SWIPT system with increasing BW using the circuit in Figure 5a, applying the proposed pre-equalizers. Simulations with the same circuit parameters are also performed.  
Figure 8 plots the normalized PCE of the SWIPT system with increasing BW using an eight-tone multisine signal when the power splitter coefficient is 0.9. In both simulations and measurements, the balanced pre-equalizer \(p=0.1\) and the matched filter achieve the highest normalized PCE. Besides, the matched pre-equalizer performs very close to \(\mathbf{w}_{0.1}\). In addition, the pre-equalizer maximizing the PAPR gives the lowest normalized PCE. Nevertheless, in the measurements, the PCE using the pre-equalizer maximizing the PAPR is not so low as in the simulation. This is because the diode performs even more nonlinearly with increasing voltage than the Shockley equation describes and also the parasitic effects are not taken into account. It should be noted that in Figure 8, the normalized PCE can be as high as 80% which approaches the optimal theoretical PCE. This phenomenon actually shows the fact that normalized PCE can be improved by exploiting channel diversity.  
To clarify why the balanced pre-equalizer has the best performance on WPT, we further analyze the impact of received power and PAPR. The power levels of the received signals with increasing \(\rho\) using different pre-equalizers are plotted in Figure 9. As the pre-equalization is the same for both modulations, and each modulation has the same signal power, the received powers are the same for both multisine PSK and QAM modulations. The final PAPR of the received signals using multisien PSK is plotted at the left side of Figure 10. It is shown that the balanced pre-equalizer of \(p=0.1\) and  
Figure 7: Capacity as a function of \(\rho\) for an 8-tone signal using multisine PSK and QAM modulation of 1 MHz BW, and adopting the proposed pre-equalisers.  
the matched pre-equalizer have a similar PAPR and received power. The pre-equalizer \(\mathbf{w}_{PAPR}\) gives the highest PAPR and lowest received power; while the \(\mathbf{w}_{|\mathbf{h}|^{3}\bigtriangleup\mathbf{h}^{*}}\) gives the lowest PAPR and highest received power; both of them provide sub-optimal SWIPT performance.  
Figure 8: (**a**) Simulated and (**b**) measured normalized PCE with increasing BW using the proposed pre-equalizers of a two-tone based SWIPT system with \(\rho=0.9\).  
Figure 9: Received power \(P_{r}\) as a function of the splitting coefficient \(\rho\) using the proposed pre-equalizers in an 8-tone signal based SWIPT system of bandwidth 1 MHz.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### Land-Based Ultrasonic Broken Rail Detection System Test Results and Performance Analysis  
The performance of the UBRD system version 4 is illustrated in Ref. [80]. After having solved some initial problems (equipment failures and GSM network reliability in some areas), a preliminary analysis of this system has been accomplished. In the first two months of operation, four broken rails and approximately four cracks have been detected before complete fracture occurred. It is believed that the system has prevented at least one derailment, the cost of which is similar to the cost of installing the system on the entire 840 km line. Detailed performance results have not been published yet.
## Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs  
**[PERSON] *, [PERSON] and [PERSON]**  
Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germany;  
E-Mails: [EMAIL_ADDRESS] (L.K.); [EMAIL_ADDRESS] (H.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-228-73-3565; Fax: +49-228-73-2988.  
Academic Editor: [PERSON]. [PERSON]  
_Received: 24 August 2015 / Accepted: 8 October 2015 / Published: 16 October 2015_  
**Abstract:** In this paper, a newly-developed direct georeferencing system for the guidance, navigation and control of lightweight unmanned aerial vehicles (UAVs), having a weight limit of 5 kg and a size limit of 1.5 m, and for UAV-based surveying and remote sensing applications is presented. The system is intended to provide highly accurate positions and attitudes (better than 5 cm and 0.5\({}^{\circ}\)) in real time, using lightweight components. The main focus of this paper is on the attitude determination with the system. This attitude determination is based on an onboard single-frequency GPS baseline, MEMS (micro-electro-mechanical systems) inertial sensor readings, magnetic field observations and a 3D position measurement. All of this information is integrated in a sixteen-state error space Kalman filter. Special attention in the algorithm development is paid to the carrier phase ambiguity resolution of the single-frequency GPS baseline observations. We aim at a reliable and instantaneous ambiguity resolution, since the system is used in urban areas, where frequent losses of the GPS signal lock occur and the GPS measurement conditions are challenging. Flight tests and a comparison to a navigation-grade inertial navigation system illustrate the performance of the developed system in dynamic situations. Evaluations show that the accuracies of the system are 0.05\({}^{\circ}\) for the roll and the pitch angle and 0.2\({}^{\circ}\) for the yaw angle. The ambiguities of the single-frequency GPS baseline can be resolved instantaneously in more than 90% of the cases.  
**Keywords:** UAV; direct georeferencing; attitude determination; GPS; ambiguity resolution; MEMS IMU; magnetometer
# A Matrix Effect Correction Method for Portable X-ray Fluorescence Data
## 2 Materials and Methods
### Parameters for Evaluation of the Correction Results  
Some statistical parameters were employed to evaluate the correction results, including the coefficient of determination (\(R^{2}\)), relative error (\(RE\)), mean absolute error (\(MAE\)), mean absolute percentage error (\(MAPE\)), root mean squared error (\(RMSE\)) and root mean square percentage error (\(RMSE\)). The correlation between the pXRF testing value and the reference value of an element could be estimated by the \(R^{2}\) values. The larger the \(R^{2}\) value is, the stronger the correlation is [26]. It is generally considered that high-quality data can be obtained when the relative error (\(RE\)) does not exceed 20%. In this paper, the accuracy of the pXRF analysis was estimated by \(P_{RE}\), which represents the percentage of data with an \(RE\) not exceeding 20% [26]. When the \(P_{RE}\) value approached 1, the testing accuracy was high, indicating that the experimental results were accurate. The parameter \(MAE\) was used to measure the average absolute error between the corrected value and the reference value of the experimental data set, and the \(RMSE\) was used to measure the deviation between the observed value and the true value [27]. As for the _MAPE_ and _RMSPE_, they could be used to evaluate the relative errors and dispersion of the whole test values of a sample, respectively. The smaller the four values were, the more accurate and the less discrete the correction results were [28]. These parameters could be obtained by solving the following equations:  
\[R^{2}=\frac{\sum_{k=1}^{n}\left(C_{ik}^{\prime}-\overline{C_{i}}\right)^{2}}{ \sum_{k=1}^{n}\left(C_{ik}^{0}-\overline{C_{i}}\right)^{2}} \tag{8}\]  
\[RE=\frac{\left|C_{ik}^{\prime}-C_{ik}^{0}\right|}{C_{ik}^{0}}\times 100\% \tag{9}\]  
\[MAE=\frac{\sum_{k=1}^{n}\left|C_{ik}^{\prime}-C_{ik}^{0}\right|}{n} \tag{10}\]  
\[MAPE=\frac{1}{n}\times\sum_{k=1}^{n}\frac{\left|C_{ik}^{\prime}-C_{ik}^{0} \right|}{C_{ik}^{0}}\times 100\% \tag{11}\]  
\[RMSE=\sqrt{\frac{\sum_{k=1}^{n}\left(C_{ik}^{\prime}-C_{ik}^{0}\right)^{2}}{n }} \tag{12}\]  
\[RMSPE=\sqrt{\frac{1}{n}\times\sum_{k=1}^{n}\left(\frac{C_{ik}^{\prime}-C_{ik} ^{0}}{C_{ik}^{0}}\right)^{2}}\times 100\% \tag{13}\]  
where \(C_{ik}^{\prime}\) denotes the predicted value of element \(i\) in sample \(k\) by the new method or the simple linear regression method, \(C_{ik}^{0}\) is the reference value of element \(i\) in sample \(k\) and \(\overline{C_{i}}\) represents the average reference value of element \(i\) in all samples.
## 3 Results and Discussion  
### Hg Concentration in Fossil Animals' Hair  
Mercury concentrations determined in the prehistoric animals' hair varied from 0.017 to 0.177 ug/g; average concentrations and ranges of Hg content in different types of prehistoric and modern animals are presented in Table 4, together with reference values and intervals. Mercury coming directly from water, air, and food tends to accumulate in both plants and animals, being toxic to most life forms. WHO guidance established 2 ug/g for total Hg in human hair as the reference level for risk evaluation [44]. The US Environmental Protection Agency (EPA) sets the reference dose for human hair and wildlife toxicity at 1 ug/g [45]. All of the prehistoric animals' hairs have Hg concentrations significantly below these levels. Moreover, they do not exceed the background level of mercury in hair of non-seafood consumers (0.5 ug/g). As far as we are aware, there is no background assessment of Hg level in herbivore prehistorical animals. We can compare our results with Hg concentrations in the hair of modern herbivore animals and their reference intervals for Hg (discussed in Section 3.2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Power, W/\%** & **Time, min** & **Pressure, psi** & **Temperature, \({}^{\circ}\)C** & **Retention Time, min** \\ \hline
1 & 1200/100 & 5 & 20 & 85 & 5 \\
2 & 1200/100 & 5 & 80 & 140 & 0 \\
3 & 1200/100 & 5 & 160 & 180 & 0 \\
4 & 1200/100 & 5 & 190 & 180 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized parameters of microwave digestion of biotic samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Accuracy, ug/g**} & \multicolumn{2}{c}{**Spike Recovery, \%**} & \multicolumn{2}{c}{**Precision (\%)**} & \multirow{2}{*}{**Method Detection**} \\ \cline{2-2} \cline{5-6}  & **Measured Value** & **Certified Value \({}^{1}\)** & **CRM** & **Yak** & **Within Run** & **Between Runs** & **Limit, ug/g** \\ \hline Hg & 0.36 \(\pm\) 0.04 & 0.36 \(\pm\) 0.08 & 94.7 & 100.4 & 3\% & 5\% & 0.003 \\ \hline \hline \end{tabular} \({}^{1}\) Certified value is for human hair, Certified Reference Material (CRM) NSC DC 73347, China.  
\end{table}
Table 3: Analytical characteristics of method.