esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Setup  
This section focuses on collecting measurement data based on the cycling of battery cells and measuring impedance spectra over many cycles.  
First, four commercial Li-ion cells of type Sony US18650 VT3 have been cycled 400 times. Each cell is charged and discharged using Constant-Current Constant-Voltage (CCCV) with a rate of 1C (1.5A) using an embedded device ICharger106+. Between each cycle, 15 min of relaxation time were assured. To ensure a charge balance between the cells, passive balancing is fulfilled every 20 cycles. An example of the voltage and current profile during the CCCV charging procedure is shown in Figure 3.  
Afterward, and at every 50 cycles, impedance spectroscopy is carried out for each cell. The impedance spectroscopy is done using a compact Data Acquisition system (NI cDAQ 9881), a custom-made control circuit, a voltage-controlled current source (VCCS) based on Servowatt DCP 130, and a computer running a custom LabVIEW script. A simplified measurement block is shown in Figure 4. Here, a sinewave-based current excitation signal is injected into the battery, which is afterward, together with the response voltage signal from the battery, sampled using an ADC module in the NI-cDAQ9881. The system can measure the impedance spectrum of 4 cells in series, with 81 logarithmically distributed frequencies within the frequency range of 10 mHz to 1 kHz. To filter the noise and enhance the signal, the final impedance spectrum measurements are processed using MATLAB.  
Figure 3: Voltage and current profile of the 4 cells, during CCCV charge.  
The cycling and impedance spectroscopy are repeated until the 400 th cycle is reached. A complete overview of the workflow can be found in Figure 5.
## 2 Related Work and Technologies
### Lte-M  
LTE-M technology (also LTE-Machine Type Communication (MTC) and LTE Cat M) also operates as an LPWAN, which allows for the the reuse of an installed base LTE (mobile network) with extended coverage. LTE-M, which stands for LTE-Machine Type Communication (MTC), is also an LPWAN technology developed by 3 GPP to enable devices and services specifically for IoT applications. LTE-M offers a data rate of 1 Mbps for 3 GPP Release 13, increasing to 4 Mbps for Release 14, leading to greater mobility and voice capacity on the network [43].
## 3 Results  
A total of 330 patients, 88 males and 242 females, with a mean age of 16.98 years at surgery, were included. The mean outpatient follow-up time was 2.9 years (median, 934 days). Eighty-four patients were excluded due to a lack of adequate X-rays, complete medical records or a sufficient follow-up time. Descriptive statistics of primary variables are described in Tables 1 and 2. Testing the association between the flexibility of the curve (flexible or nonflexible) and the occurrence of material failure revealed no statistical association, despite the apparent excess of failures among patients with nonflexible curves (Table 3a). Additionally, there was no clear association between the degree of flexibility and material failure when evaluated as the absolute difference in degrees or in percentages (Table 3b). \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Variable** & **Category** & \(n\) & **\% (95\% CI)** \\ \hline
**Sex** & Female & 242 & 73.33 (68.79–78.31) \\  & Male & 88 & 26.67 (22.12–31.65) \\ \hline  & T4-L4 & 66 & 20.00 (15.45–24.77) \\  & T3-Iliac & 56 & 16.97 (12.42–21.74) \\  & T2-Iliac & 25 & 7.58 (3.03–12.35) \\
**Instrumented levels** & T4-L3 & 24 & 7.27 (2.73–12.04) \\  & T5-L4 & 19 & 5.76 (1.21–10.53) \\  & T4-L1 & 16 & 4.85 (0.30–9.62) \\  & T3-L4 & 13 & 3.94 (0.00–8.71) \\ \hline
**Ambulation** & Yes & 232 & 70.30 (65.45–75.37) \\  & No & 98 & 29.70 (24.85–34.77) \\ \hline  & Idiopathic & 187 & 56.67 (51.21–62.14) \\
**Pathology** & Neuromuscular–Cerebral Palsy & 79 & 23.94 (18.48–29.42) \\  & Neuromuscular & 55 & 16.67 (11.21–22.14) \\  & Congenital & 9 & 2.73 (0.00–8.20) \\ \hline
**Pelvic obliquity within normal range (preoperative)** & Yes & 203 & 61.52 (56.36–67.10) \\  & No & 127 & 38.48 (33.33–44.07) \\ \hline
**Pelvic obliquity within normal range (postoperative)** & Yes & 236 & 71.52 (66.67–76.43) \\  & No & 94 & 28.48 (23.64–33.40) \\ \hline  & Kept in the normal range & 202 & 61.21 (56.06–66.75) \\
**Comparison of pre- and postoperative normal-range** & Kept out of the normal range & 93 & 28.18 (23.03–33.72) \\
**pelvic obliquity** & Entered the normal range & 34 & 10.30 (5.15–15.84) \\  & Out of the normal range & 1 & 0.30 (0.00–5.84) \\ \hline
**Main curve flexibility** & Flexible & 168 & 50.91 (45.45–56.58) \\  & Nonflexible & 162 & 49.09 (43.64–54.76) \\ \hline
**Secondary curve flexibility** & Nonflexible & 289 & 87.58 (84.24–91.01) \\  & Flexible & 41 & 12.42 (9.09–15.86) \\ \hline
**Material failure** & No & 309 & 93.64 (91.52–96.34) \\  & Yes & 21 & 6.36 (4.24–9.07) \\ \hline  & Without failure & 309 & 93.64 (91.52–96.20) \\
**Need revision surgery** & No & 12 & 3.64 (1.52–6.20) \\  & Yes & 9 & 2.73 (0.61–5.29) \\ \hline \hline \end{tabular} Legend: number of patients per category (\(n\)) and percentage with 95% confidence interval (CI).  
\end{table} 
Table 1: Descriptive statistics of qualitative variables.
## 1 Introduction  
### Background and Context  
The increasing adoption of AI in education has transformed traditional pedagogical methods, enabling adaptive and personalized learning experiences. In mixed-ability classrooms, students exhibit diverse levels of prior knowledge, cognitive abilities, and learning speeds, leading to conceptual learning gaps--disparities in understanding fundamental concepts that hinder academic progress. These gaps are particularly pronounced in STEM and language education, where cumulative knowledge-building is essential for long-term learning success [1]. Traditional classroom instruction, often constrained by time and resources, struggles to address the individual needs of students, resulting in learning inequities [2].  
AI-driven adaptive feedback systems have emerged as a promising solution to this challenge. These systems use learning analytics and machine learning algorithms to analyze student interactions, detect misunderstandings, and provide real-time personalized feedback [3]. Unlike static, one-size-fits-all teaching approaches, AI-driven adaptive feedback dynamically adjusts instructional content, ensuring that struggling learners receive targeted support while advanced students progress at an appropriate pace. However, the effectiveness of AI-driven feedback in closing conceptual learning gaps remains an area requiring empirical validation, particularly in higher education settings where diverse learning needs are prominent.  
AI-driven feedback plays a crucial role in closing learning gaps by providing personalized learning enhancements tailored to students' needs. As illustrated in Figure 1, AI-based interventions lead to targeted support for struggling students, increased engagement, improved understanding, and enhanced knowledge retention. By leveraging adaptive learning strategies [4], AI-driven feedback systems ensure that students receive timely interventions [5], fostering a more interactive, data-driven, and effective learning environment. These elements collectively contribute to a more inclusive educational framework that optimizes student performance in mixed-ability classrooms.  
Figure 1: AI-driven feedback closes learning gaps in education.
## 1 Introduction
Because the EEG signal is nonstationary, the most suitable way to extract features from raw data is to use time-frequency domain methods, such as STFT (Short-Time Fourier Transform) [41; 42; 43]. STFT is a time-dependent Fourier Transform that may be calculated by sliding a window over the signal to compute the FFT [41]. It is represented by a two-dimensional time-frequency plot, called a spectrogram. The major drawback of STFT is the compromise between time and frequency resolution. If a short window is applied, information derived from each FFT will be well-localized in time, but the frequency resolution will be poor. Feature extraction is an important task in signal processing. DFT (Discrete Fourier Transform) can convert an equally sampled signal into a list of coefficients. These coefficients comprise a finite combination of complex sinusoids ordered by frequencies; in other words, DFT converts a digital time-domain signal to a frequency domain [44]. To enhance DFT estimation, the FFT algorithm may be applied. As the name implies, FFT is a fast version of DFT [45]. Fourier Transform is one of the nonparametric feature extraction techniques for EEG signals [46; 47]. In addition, the electrical activity of the brain (EEG) exhibits significant complex behavior with strong nonlinear and dynamic properties [48; 49]. Nonlinearity in the brain is introduced even at the cellular level because the dynamic behavior of individual neurons is governed by threshold and saturation phenomena [50]. This implies that nonlinear methods can be applied to investigate EEG dynamics [49].The Lempel-Ziv complexity (LZC) [51] uses symbolic techniques to map a time series into a sequence that retains its dynamics. LZC has been widely used in biomedical applications to estimate the complexity of discrete-time signals [52]. Normalized complexity can be used to quantify nonlinear and nondeterministic data [53]. This study aimed to develop a method to monitor bovine brain electrical activity by noninvasive techniques in animals in an experimental paddock. Another objective was to apply techniques such as PSD (Power Spectral Density), STFT, and LZC to characterize the obtained signals, a necessary step to expand this field of study.
## 3 Interference Automatic Recognition and Tamping Path Planning
### Automatic Control of the Tamping Device Hydraulic Cylinder  
(1) Construction of the hydraulic drive physical model for the tamping device.  
For example, a hydraulic drive physical model was established for the tamping device cylinder. As the main driving component, the hydraulic cylinder mainly involved hydraulic pressure \(F_{y}\) and its corresponding hydraulic pump station, relating to hydraulic pressure P. The hydraulic drive model ensured a stable hydraulic pressure provided by the hydraulic pump station and controlled the tamping angle and path, as well as the stability of its angular and linear velocities, through the internal oil pressure \(F_{y}\) of the cylinder. The analysis process is as follows:  
\[F_{y}=m_{y}a_{y}+Bv_{y} \tag{3}\]  
where \(B\) is the damping coefficient; \(v_{y}\) is the velocity of the cylinder motion; \(a_{y}\) is the cylinder motion acceleration; \(m_{y}\) is the payload quality of the cylinder; and \(F_{y}\) is the internal liquid pressure of the cylinder.  
\[\begin{array}{l}\dot{x}_{1}=a_{y}\\ x_{1}=v_{y}\\ \dot{x}_{2}=x_{1}\\ F_{y}=U\end{array} \tag{4}\]  
Figure 5: Automatic recognition and regulation algorithm of SIB process interference.  
Therefore:  
\[U=m\dot{x}+Bx \tag{5}\]  
The equivalent equation has the following form:  
\[\begin{array}{l}\dot{x}_{1}=-\frac{B}{m}x_{1}+\frac{U}{m}\\ \dot{x}_{2}=x_{1}\end{array} \tag{6}\]  
where \(x_{1}\), \(x_{2}\), and \(U\) are the conversion variables.  
(2) Algorithm for automatic control of hydraulic cylinder position and motion.  
To guarantee the stable and efficient navigation of the tamping device past obstacles towards its desired position, we used the common one (PID) control algorithm to build a PID closed-loop negative feedback controller for the position/motion control of the hydraulic cylinder, and the PID algorithm contained proportional, integral, and derivative parts. In the real control process, achieving the anticipated control effect involved continuous adjustments to the ratio coefficient \(K_{p}\), integral time constant \(K_{i}\), and differential time constant \(K_{d}\) parameters. The control principle diagram is shown in Figure 6[24].  
In Figure 6, \(e(t)\) is the deviation between the input and output values, where \(r(t)\) in \(e(t)=y(t)-r(t)\) is the input value, \(y(t)\) is the output value, t is the time interval between the start of the adjustment and the output of the current control quantity, and \(y(t)\) is the input control signal at the moment when the PID starts adjusting. The adjustment process is a fixed value.  
The hydraulic-cylinder-drive physical and PID control models were run using the MATLAB platform. Following a period of closed-loop control, the tamping angular velocity, angular acceleration, linear velocity, and linear acceleration of the tamping path of the tamping device all entered a stable state. The debugging results are shown in Figure 7.  
Figure 6: PID control system schematic diagram.  
Figure 7: Hydraulic drive simulation model of Sant-angle cylinder
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.5 Multifractal Detrended Fluctuation Analysis  
Multifractal detrended fluctuation analysis (MFDFA) [49] is a powerful method for quantifying the complexity and scale-invariant properties of non-stationary signals. MFDFA provides a robust framework for extracting nonlinear and multifractal characteristics from EEG signals. MFDFA captures both global and local fluctuations by computing the generalized Hurst exponent \(H_{q}\) across different moments \(q\). Thus, in MI tasks, it reveals the multifractal nature of brain activity. The method begins by constructing the integrated profile \(X(i)\) of a mean-centered signal \(x(i)\), as in Equation (13):  
\[X(i)=\sum_{k=1}^{i}(x(k)-\langle x\rangle) \tag{13}\]In this context, \(\langle x\rangle\) is the mean of the signal. The profile is then divided into non-overlapping segments of scale \(s\), and a least-squares polynomial fit \(P_{\texttt{D}}(k)\) is computed for each segment \(v\). The root mean square (RMS) fluctuation is determined, as in Equation (14):  
\[F_{\texttt{D}}^{2}(s)=\frac{1}{s}\sum_{k=1}^{s}[X((v-1)s+k)-P_{\texttt{D}}(k)]^ {2} \tag{14}\]  
The fluctuation function \(F_{q}(s)\) for different orders \(q\) is then computed, as in Equation (15):  
\[F_{q}(s)=\left[\frac{1}{N_{s}}\sum_{v=1}^{N_{s}}\left(F_{v}^{2}(s)\right)^{q/2 }\right]^{1/q},\ q\
eq 0 \tag{15}\]  
For \(q=0\), the fluctuation function is given by the geometric mean, as in Equation (16):  
\[F_{0}(s)=\exp\left(\frac{1}{N_{s}}\sum_{v=1}^{N_{s}}\log F_{\texttt{D}}^{2}(s )\right) \tag{16}\]  
The generalized Hurst exponent \(H_{q}\) is estimated as the slope of the log-log plot of \(F_{q}(s)\) vs. \(s\), as in Equation (17):  
\[H_{q}=\frac{\Delta\text{log}\,F_{q}(s)}{\Delta\text{log}\,s} \tag{17}\]  
The mean of \(H_{q}\) across different \(q\)-values is often used as a single representative measure of fractal complexity.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 4 Experiments and Evaluations
### Experiments Parameters  
In our study, we employed a widely utilized UWA channel simulator developed by [29]. This simulator had undergone validation using real data collected from four experiments, ensuring its ability to generate accurate UWA channels:  
\[H(f,t)=\bar{H_{0}}\sum_{l}h_{l}\tilde{\gamma_{l}}(f,t)e^{-\beta\pi f\tau_{l}}, \tag{17}\]  
where \(\bar{H_{0}}\) was the nominal frequency response, and where \(h_{l}\) and \(\tilde{\gamma_{l}}(f,t)\) were the large-scale path gain and the small-scale fading of the \(l\)-th UWA path, respectively. Here, we adopted the channels of KAU1 and NOF1 as the simulation environment, and some parameters, summarized in Table 1, to configure the UWA physical environment. Table 2 presents the main simulation parameters of the UWA-OFDM system. For channel samples, the CWGAN-GP generator was used to generate the KAU1 and NOF1 channels, and a CSI matrix of 10,000 pilot positions was obtained after frequency-domain conversion and pilot channel estimation at the receiver. Here, we generated CSI matrices unified at a Signal-to-Noise Ratio (SNR) of 15 dB. Of these, 80% were randomly divided into the training set, with 10% the validation set and the last 10% the test set. Furthermore, we set the scale factor to 10, set the initial learning rate to 0.001, and then decay by 0.1 times every 40 epochs. The maximum epoch was set to 100, but if the value of the loss function did not drop within 5 consecutive epochs, the training was stopped prematurely.  
As depicted in Table 2, there were 512 subcarriers, and each frame was composed of 14 symbols, resulting in a CSI matrix size of 512 \(\times\) 14. In our simulation, we evaluated the effectiveness of our proposed algorithm, using four different algorithms: the LS algorithm [10], a DNN-based algorithm [16], the ChannelNet algorithm [30], and FullCSI. The specifics are outlined below:  
* _LS_: Select two or four symbols as pilot symbols, and then estimate the CSI of the pilot location and data position by using the LS algorithm and the spline interpolation method, respectively.
* _DNN_: Select two or four symbols as the pilot symbols, and then obtain the CSI matrix by using the DNN-based algorithm.
* _ChannelNet_: Select two or four symbols as the pilot symbols, and then obtain the CSI matrix based on ChannelNet. The algorithm is mainly divided into the process of expansion and denoising reduction.
* _FullCSI_: Suppose that the channel is determined to be known.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **KAU1** & **NOF1** \\ \hline Water depth & 100 m & 10 m \\ \hline Transmitter depth & 20 m & 10 m \\ \hline Receiver depth & 50 m & 10 m \\ \hline Transmission distance & 1080 m & 750 m \\ \hline Spreading factor & 1.7 & 1.7 \\ \hline Sound speed in water & 1500 m/s & 1500 m/s \\ \hline Sound speed in bottom & 1200 m/s & 1200 m/s \\ \hline Tx vehicle speed & \(N(0,1)\) m/s & \(N(0,1)\) m/s \\ \hline Rx vehicle speed & 0 m/s & 0 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: UWA Channel Parameters.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Prediction Models
#### Ensemble Methods  
The basic idea of the ensemble learning method is developing a prediction model by integrating a number of simple models. The two most common ensemble learning methods are boosting [25] and bagging [26].  
Bagging is a method that integrates several individual base models into one to generate an inclusive ensemble model. A new prediction model can be developed from separate prediction models to form an ensemble, for instance, by averaging regression. Averaging individual models means reducing the variance; and thus, bagging can be applied for a model with high variance and low bias. As opposed to bagging, boosting is a common method to generate an ensemble model from a single model such as, decision trees. It is a sequential technique that integrates a set of weak learners and provides a more accurate model estimation. The boosting model produces strong models with low bias. The new outcomes of the developed model have weights based on the earlier outputs of the model. If the outputs are predicted properly, a smaller weight is assigned; otherwise, the assigned weight will be higher.
## 5 Future Work Discussions
### Prototype Implementation  
Prototype implementation can inform and enable the systems approach described in Section 5.1. The implementation-based approach can expand our understanding of the system beyond the theoretical models and thus improve the practicality and utility of the system modeling. This is especially important for the UAV flying base station, which is a relatively novel concept and is currently undergoing engineering development, dynamically affecting our knowledge of the system.  
For such research benefits, we built a UAV flying base station prototype based on a DJI Matrice 300 RTK drone for the mobility functionality and USRP B210 software-defined radio for the base station functionality. Figure 4 shows the hardwares and the corresponding simulated entities in 5G networking for our prototype. From left to right (loosely), the UAV flying base station connects to the stationary ground/terrestrial base station (the traditional base station), which in turn connects to the core network at the backend, as described in the 5G architecture in Figure 1. Our prototype also includes a monitoring system for digital networking analysis. The ground base station and the core network are connected via a switch, not shown in Figure 4. We plan to use such a prototype to better model the flying base station system and validate and test our security solutions. For example, based on a preliminary prototype-based study, for a rotor-based drone, the hovering operation dominates the lateral movements of the drone in energy, and most of the power consumption from the hovering operation is significantly larger than the marginal power consumption from adding lateral movement. We only made this observation after our prototype implementation, and the observation informed our modeling and research afterward. The observation also motivates the flying base station to land while serving as a base station as frequently as possible (as opposed to hovering in the air) and discretize and separate between the movement vs. stationary phases due to the overhead of launching itself into the air (as opposed to hovering and moving continuously).
## 3 GA Hybrid-Fog Model  
This paper introduces the GA Hybrid-Fog model, a novel fog computing framework designed to enhance the capabilities of IoD systems. Unlike traditional IoD infrastructures that primarily rely on cloud computing, fog base stations, or edge computing nodes, GA Hybrid-Fog integrates multiple computing resources, including FBSs, fog UAVs, edge UAVs, and cloud computing. By leveraging the onboard computing capabilities of UAVs and the resources of FBSs, the GA Hybrid-Fog model significantly expands the available computational capacity.  
The GA Hybrid-Fog model allows drones to seamlessly access the computing resources of both mobile and stationary fog nodes, thereby improving computational efficiency. Furthermore, it reduces latency by enabling direct interactions between UAVs and fog UAVs, FBSs, and cloud platforms. Structured with four distinct layers--an edge layer, a mobile fog layer, a static fog layer, and a cloud layer--the proposed model enhances reliability and minimizes latency, particularly in real-time applications. Additionally, it supports a wide range of computing functions, including data processing, storage, and networking.