esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Multidimensional nuclear magnetic resonance (NMR) spectroscopy plays an important role in the fields of biomedicine and chemistry [1; 2; 3]. However, long data acquisition time [4; 5; 6] has to be solved, and one of the effective approaches to reduce the time is to acquire partial data with non-uniform sampling (NUS) [7; 8; 9; 10; 11; 12]. To obtain a full spectrum, reconstruction needs to incorporate priors, and the most common ones are sparsity [13; 14] and low rankness [15; 16; 17]. Sparsity [18; 19; 20] assumes the spectrum has few non-zero values in the spectrum but will not be satisfied at low-intensity broad peaks [21]. The low rank approach transforms the time-domain NMR signal (called free induction decay (FID)) into a Hankel matrix, and explores the low rankness of this matrix. Therefore, it is also called a low-rank Hankel matrix (LRHM) method. Since the peak width will not affect the rank, LRHM offers better reconstructions for these challenging peaks, even though low-intensity peaks, referring to small singular values in the matrix rank minimization, may be compromised or even lost in LRHM reconstructions [22]. This problem also exists in general low-rank matrix reconstruction and has been alleviated by introducing the truncated nuclear norm (TNN) to preserve the small singular values in the reconstruction [23].  
Recently, we proposed a self-learning subspace (SLS) [24] method to mitigate the spectra degradation of LRHM at high acceleration factors. Beyond the singular values, we found that the subspace of the Hankel matrix corresponds to spectral peaks, and the TNN provides one approach to incorporate the subspace information. With the help of TNN, we establish the concept of signal subspace reconstruction in the low-rank Hankel matrix. The signal space is divided into two subspaces, an obvious, easily-restored strong signal subspace and an unstable, hard-to-estimate weak signal subspace [24]. This method includes two iterations loops: The outer loops update the signal subspace with prior constraints, and the inner one performs updates of signal reconstruction under the given subspace. Experimental results show that SLS achieves as high-quality reconstructions as does LRHM, and notably, produces better reconstructions of low-intensity peaks [24].  
The SLS approach, however, requires intensive computations of singular value decomposition (SVD). In the iterative reconstruction, SVD will be called approximately 100 to 200 times, which is very time-consuming. In addition, since the computational complexity of SVD is proportional to the three power of the matrix size [25], the lengthy SVD computations may be impractical due to the data size and dimensions rapidly increasing in high-dimensional NMR spectroscopy.  
In this work, we propose an accelerated algorithm for the state-of-the-art self-learning subspace method by introducing matrix factorization [26; 27] to avoid SVD. Results on synthetic and realistic NMR data show that compared with the SLS, the fast SLS approach saves considerable time without sacrificing spectrum quality and enables faster reconstruction with parallel computing.
## 5 The Pushover Analysis of the Center Column  
### Analysis Methods  
When subjected to seismic loading, the deformation mode of underground structures differs greatly from that of ground buildings. When a vertical member of a ground building (such as a bridge pier) is statically pushed over, a certain vertical pressure is first applied to achieve a predetermined axial pressure ratio, and then the member is loaded step by step through force control or displacement control until damaged. As shown in Figure 8 (left), the top of the member is often set without any restraints. For underground structures, the rotational degrees of freedom at the top of the center column are constrained to a large extent due to the restraining effect of the overlying soil and the top plate. Therefore, in order to truly reflect the deformation behavior of the center column during the earthquake, the top of the model is imposed with rotation constraints during pushover, and its loading  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Layer** & \begin{tabular}{c} **Thickness** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Shear Wave Speed** \\ **(m/s)** \\ \end{tabular} &
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} & \(A\) & \(B\) & \(\gamma_{0}\) **(10\({}^{-4}\))** \\ \hline Soil layer I & 4 & 1900 & 200 & 0.3 & 1.02 & 0.35 & 4.0 \\ Soil layer II & 4 & 1950 & 260 & 0.3 & 1.05 & 0.34 & 3.5 \\ Soil layer III & 4 & 1980 & 310 & 0.3 & 1.10 & 0.35 & 3.8 \\ Soil layer IV & 8 & 1950 & 335 & 0.3 & 1.10 & 0.35 & 3.8 \\ Soil layer V & 10 & 2000 & 430 & 0.3 & 1.10 & 0.35 & 3.8 \\ Soil layer VI & 10 & 2100 & 520 & 0.3 & 1.20 & 0.35 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of soils.  
Figure 7: Material parameters of concrete. (**a**) Compressive behavior of concrete. (**b**) Tensile behavior of concrete.  
pattern is shown in Figure 8 (right). In the pushover analysis of the center column, there are two analysis steps. Firstly, the axial pressure is applied to the top of the center column. The axial pressure considers two working conditions, one is considering only the self-weight of the soil and structure, and the other is considering both the self-weight and the vertical inertia force. In the second analysis step, the horizontal displacement is applied to the top of the center column. As shown in Table 5, the pushover analysis of the center column includes 16 cases.
## 3 Results and Discussion
### Mechanical Property Investigation Results  
#### 3.4.1 Hardness Test Results  
Due to hardness tests' simplicity and the fact that hardness is an important criterion in material selection, it is one of the mechanical properties which are primarily investigated in material enhancement studies such as SPD studies. Given these characteristics, this study was primarily aimed at investigating how the hardness of the material changed. In this context, hardness tests were performed according to standards, and the results are given in Figure 18. When the obtained results were examined, it can be seen that the hardness values for annealed AA6082 were acquired at about 50 HV. Similar results for annealed AA6082 workpieces at 53 HV have been reported in other studies [35; 58; 59; 60] and can be easily found in the literature. This close result is a confirmation of the hardness values obtained in this study. Again, when other studies in the literature were examined, the hardness value for AA6082 processed with multipass ECAP was found to be 115 HV, which is an increase over the annealed material [59; 60; 61]. However, after the one-pass Vo-CAP process, this enhancement is reached and the average hardness value of 113.2 HV was obtained after only one pass, which is approximately double when compared to the annealed material. Furthermore, when examining the figure, it can be seen that more hardness is gained when moving away from the center. The reason for this is that the frictional resistance, which prevents the material from flowing under the applied pressing load, is greater at the edges. Accordingly, the stress value applied per particle is higher. When the values of the hardness results obtained are analyzed, it is seen that the distribution correlates with the effective strain results.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 2 Materials and Methods
### Modeling the Load Balancing on a Set of Tunnels as a Multiplayer Game
#### 2.2.2 Auction-Based Load Balancing
The difference between this algorithm and the previous one is that here all packets belonging to one flow are carried by the winning tunnel. A flow remains attached to a tunnel until its global quality degrades so much that it is more beneficial to that flow to change the tunnel (even \"paying\" the penalty). The algorithm's complexity is \(\mathcal{O}(N)\) for each arriving packet. As in the situation of the Selfish Routing LB algorithm, the amount of computation required can be decreased by defining some timers attached to each flow and executing the algorithm only when the timer expires and not for each new packet arriving at the AP. The downside of such a solution is the slower response of the system to the changes in the input parameters.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.5 Throughput  
Maximization of throughput is the critical issue in the design of energy constrained wireless sensor networks. Throughput optimization of energy sharing wireless sensor networks has been proposed in [61] for the design of energy sharing technique by using ultra-capacitor based energy harvesting system. Solar power sensor network design approach has been proposed in [62] to maximize the throughput in order to better utilize the solar power and to ensure fairness for all nodes across the network. The design of a cloud-integrated sensor network architecture has been proposed in [63] by using a multi-objective optimization algorithm to maximize the throughput and minimize the bandwidthand energy consumption. The design of efficient spectrum sensing and power allocation techniques have been proposed in [64] to maximize the throughput and minimize the interference.
# A Scalable Parallel Architecture Based on Many-Core Processors for Generating HTTP Traffic
## 5 The High Concurrency Strategy
### Traffic generation layer
#### 5.3.4 Dynamic Load-Balancing Based on the Minimum Number of Requests (DMR)  
Due to the multiple traffic generation engines, dynamic load balancing is an important step to condition the parallel performance [32]. When the virtual users engine produces a mass of requests, it is necessary to predict which traffic generation engine to respond. To overcome this issue, very common approaches need to be raised based on the polling algorithm, weighting algorithm and hash algorithm to solve the load-balancing problem [33].  
When the node performance of the traffic generation engine is basically the same, we can use a simple polling algorithm for load balancing. However, the traffic generation engine is based on the request object as a basic management unit, and the life cycle of each request object is not the same, leading to the evolution of the load being unpredictable. Furthermore, fast recovery of load-balancing can be very inefficient using a simpler approach, especially when it comes to a sudden increase in traffic or an abnormal process. In this paper, we present a dynamic load-balancing algorithm which accepts the dynamic change of the traffic generation engine based on the minimum number of requests (DMR). Each traffic generation process sends its own number of active requests to the virtual user layer in real-time. Then the load-balancing module updates the history record, and selects a traffic generation process with the smallest number of requests to send request messages. It can be classified as the weight class algorithm. The weight factor is the number of active requests in the process and the lower the weight, the higher the probability of selection.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 3 The Proposed Gate Driver for Buck Converter
### Gate Driver  
As shown in Figure 2, the high-side gate driver consists of stacked E-mode and E/D-mode GaN transistors, and an off-chip bootstrap capacitor, \(Cb\), is also employed to increase the holding voltage when the high-side transistor is turned on. The lowest voltage of the driver was chosen to be \(-5\) V in order to completely turn off the GaN power transistor, M1, with low threshold voltages. The low-side driver employs a conventional D/E stacking architecture [21].  
The overall operation of the gate driver can be expressed as follows. In the beginning switching period, after VH turns on M3 through the buffer stage, M4 is turned on. At this moment, the gate voltage of M1 is quickly pulled down low close to \(-5\) V and M1 is turned off. The external freewheeling diode, \(D1\), is then turned on, and the voltage at the Vsw node is pulled down low. Then, VL can turn off M6 through the buffer stage, and the 5 V voltage source charges M2 through M7. While M2 is turned on, in addition to increase the falling speed of Vsw, the bootstrap capacitor, \(Cb\) is also charged to 5 V. In the later switching period, when [PERSON] turns on M6, M2 is turned off and then [PERSON] can turn off M3. After that, the charged \(Cb\) can directly pull up the gate of M1 through M5, and M1 turns on to pull up Vsw high to charge the output inductor \(L1\) for one complete cycle. Since the low-side power switch is switched on/off complementarily after the high-side switch through the dead-time control, the on-time of VH and VL can be set to avoid the shoot-through current of the two GaN power transistors. M4 can be designed using GaN transistors with longer LGD to increase resistance to high voltage switching at the gate node of M1, Vgu. Conversely, M3 can use GaN transistors with shorter LGD to increase switching speed.
## 3 Hierarchical Frequency Control Scheme
### Overview of Hierarchical Control Scheme  
The block diagram of hierarchical coordinated control framework is shown as Figure 1. In this paper, the inverter ACs are integrated into traditional automatic generation control (AGC), whereby both inverter AC and regulation generators participate in secondary frequency control and other regulation resources are assumed to have no available capacity.  
The coordinated control scheme is stratified into three control layers: the control center layer, the coordinated control layer and individual demand layer. The operating states of the large-scale interconnected power grid are monitored by the control center in real time, especially the frequency and tie-line power. Area control error (ACE) can be calculated as the core indexes for the evaluation of system security. Based on ACE, the total regulation power can be obtained through the PI controller. Taking the system operating states and available generator capacity into consideration, UPCE is calculated to evaluate the available ability of traditional regulation generators. Both UPCE and the total regulation power are delivered to the next level. The coordinated control layer mainly contains two categories of dispatchable units: regulation generators and the aggregated agents of numerous ACs. According to the proposed coordinated control strategy, the control signals from the upper level are allocated to dispatchable units. Due to the relatively small capacity of individual AC devices, the virtual leaders are employed to receive allocated control signals and send the control signal to the individual ACs. The communication network including the numerous ACs is taken into consideration in the individual demand layer. The distributed pinning consensus control strategy is conducted for fair participation of heterogeneous ACs. Compared with the commonly utilized consensus approach, the finite-time control protocol is appended to significantly improve the convergence of the proposed distributed pinning control strategy [29].  
Compared with existing studies, there are two highlights of this paper: (i) an adjustable control performance index and the related coordinated control strategy are developed to incorporate multiple dispatchable units; (ii) a distributed pinning finite-time consensus control algorithm is introduced to control a large population of individual ACs. The detailed exposition is implemented in the following subsections.
## 1 Introduction
The [PERSON] [24] equation uses the green band and the NIR band, optimizing vegetation moisture reflection and minimizing water bodies, whereas Dr [PERSON] [25] determines the NDWI by means of the relationship between NIR and SWIR. [PERSON] [26] later proposed the modified normalized difference water index (MNDWI), considering the green and SWIR bands. However, [PERSON] et al. [27] state that soil moisture can cause side effects when using the SWIR band because its absorption is constrained to a reasonable extent. A soil-adjusted vegetation index such as the SAVI (soil-adjusted vegetation index) is used to reduce the soil effect, minimizing the related brightness by considering first-order soil vegetation interaction with soil-adjustment parameters [28]. [PERSON] et al. [29] use this index to map soil moisture by means of data mining, finding that SAVI is an important covariate in predicting soil moisture retention properties. Another common methodology to estimate soil moisture by means of remote sensing is the trapezoid method, based on thermic and optical data regarding the Earth's surface [30]. This methodology has the problem that land surface temperature varies significantly with the ambient atmospheric parameters, while optical reflectance does not [31]. Starting from this assumption, some indices using optical observations have been proposed for soil moisture and drought monitoring based on triangular spaces from pixel distributions of optical observations in different electromagnetic frequency bands [31]. One of these triangular indices is the PDI (perpendicular drought index), designed by [PERSON] et al. [32], which determines soil moisture for bare soils and low covers by means of the near infrared correlation of pixels. [PERSON] and [PERSON] [22] evaluated the PDI, finding that it has some limitations that challenge its performance in areas with dense vegetation, but that it is highly effective for bare soils. In this study, four indices (NDVI, NDWI, SAVI, and PDI) are evaluated to estimate soil moisture (SM) from high resolution images obtained by means of remote optical sensors and UAV flights in the highest part of the Las Palmas catchment area in Envigado, Colombia (See Figure 1). Soil moisture was evaluated according its land use on Andosol to determine an algorithm to correlate the studied indices with the soil moisture field data at different spatial resolutions. The four indices were evaluated to estimate soil moisture for three land uses (potatoes, bare soil and pasture). In addition, we analyze these indices in several spatial resolution using re-sampled imagery from UAV. We demonstrate that the performance of these indices is conditioned to both land uses and spatial imagery resolution.
## 5 Application of Self-Sensing Materials for
### Deicing and Snow Melting
The 2 cm compacted layer of snow was completely melted in 27.31 min, and the surface of the thermally conductive concrete was dried after 28.46 min. The study also revealed that the speed and rate of snow melting depend on the thickness and condition of the snow layer. In another study, heating concrete doped with steel fibers and graphite was successfully implemented in the ramp road of a parking area to address the accumulation of frozen ice and snow [258]. In a recent investigation conducted by [PERSON] et al. (2024) [259], it was demonstrated that for maximizing the heat efficiency of electrically conductive cementitious composites, it is advisable to minimize the thickness of the slab. This recommendation is based on the understanding that the primary heat generation occurs at the interface between the electrodes and the concrete. As previously discussed, the configuration of electrodes can affect the performance of multifunctional cementitious composites [259]. Therefore, the installation of electrodes in the field must be executed with precision to ensure the high performance of electrically and thermally conductive pavements. Generally, factors such as electric voltage, electrical resistance, type and percentage of conductive fillers, distance between electrodes, depth of electrodes, and dimensions of pavement slabs should be considered during the design stage of heating pavements [260]. To further elucidate the factors affecting the heating rate of conductive pavements, discussing the theoretical mechanism of resistive heating is beneficial. The conversion of electrical energy (E) to thermal energy (Q) is fundamental in electrically conductive concrete (ECON) [261]. When ECON is subjected to electric power, heat is generated based on resistive heating (Joule heating) [262]. Electrical energy (E) and thermal energy (Q) can be calculated using Equations (2) and (3), respectively. By equating electrical energy to thermal energy, the mechanism of resistive heating can be understood through Equation (4). According to Equation (4), the rate of temperature increase is exponentially related to voltage, while it has a linear relationship with other parameters [261]. \[E=\frac{V^{2}\cdot A}{\rho\cdot L}\Delta t \tag{2}\]  
\[Q=m\cdot C\cdot\Delta T \tag{3}\]  
\[\frac{\Delta T}{\Delta t}=\frac{V^{2}\cdot A}{\rho\cdot L\cdot m\cdot C} \tag{4}\]  
where \(E\) is electrical energy, \(V\) is voltage, \(A\) is the conductive cross section area in the current direction, \(\rho\) is electrical resistivity, \(L\) is the conductive length in direction current, m is the mass, \(C\) is specific heat capacity, \(\Delta T\) temperature increase in the material, and \(\frac{\Delta T}{\Delta t}\) is the temperature increase rate.
## 1 Introduction
To evaluate these techniques in realistic educational scenarios, researchers often rely on structured datasets that contain detailed records of student interactions. One such dataset is Experience API (xAPI), which structures learning records according to a standardized framework [17]. Unlike traditional LMS logs, xAPI enables the collection of learning records across a wide variety of settings, including simulations, mobile devices, and offline activities, thus providing a more comprehensive insight into student behavior [18]. Due to its thorough coverage and depth of behavioral information, the xAPI dataset is a suitable resource for applying and assessing ML techniques in educational data analysis contexts. Given this context, the main goal of this study is to explore the effectiveness of a fully automated DL framework pipeline for student performance prediction, leveraging AutoML to optimize model selection, hyperparameter tuning, and feature processing. We compare this approach with a recent semi-supervised FS method proposed by [PERSON] et al. [19], using the same dataset and evaluation conditions. While the semi-supervised method enhances interpretability, our findings indicate that the AutoML-optimized DL framework demonstrated better predictive performance under the tested conditions, likely due to its ability to uncover nonlinear patterns and complex interactions within the data [10, 17, 20]. To promote transparency and reproducibility, all implementation details and results of this study are publicly available at GitHub (version 1) ([[https://github.com/jalagrod/DL-ensemble-models-in-EDM](https://github.com/jalagrod/DL-ensemble-models-in-EDM)]([https://github.com/jalagrod/DL-ensemble-models-in-EDM](https://github.com/jalagrod/DL-ensemble-models-in-EDM)) (accessed on 15 April 2025)). The remainder of this paper is structured as follows: Section 2 provides an overview of related work and establishes the context and relevance of our study. Section 3 details the proposed methodology, including aspects such as data preprocessing, model architectures, and evaluation metrics. Subsequently, Section 4 presents and discusses the experimental results. In Section 5, the data acquisition process is described in detail. Finally, Section 6 summarizes the main conclusions and outlines potential directions for future research.