esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Pruning  
There are a variety of network compression methods such as network pruning, network distilling [6], quantization [7], network decomposition [8], and compact network design [9]. Among them, pruning is the most widely used method [10; 11; 12], which can transform the original dense deep neural network into a sparse network without a significant reduction in the network accuracy. The pruning of DCNNs can be divided into two categories, including unstructured pruning and structured pruning.  
Unstructured pruning, which is also known as sparse pruning, sets some weight values to 0 for the sparsity of the convolution matrix. [PERSON] et al. [13] proposed an iterative method to cut the network weight below a certain threshold. [PERSON] et al. [14] proposed a convolution neural network clipping algorithm, which cuts the network in a layer-wise manner by using energy consumption as the ranking criterion and restores network accuracy by using the least-squares method for local fine-tuning of each layer.  
Structured pruning removes convolution kernels, filters, channels, and layers in the network and eliminates redundant connections in the network. L1 regularization [15] and L2 regularization [16] have been used to assess the correlation between convolution kernel and network performance and determine the filters to be pruned. In [17], scaling coefficient normalization in the batch normalization layer was used to guide the thinning and pruning of the neural network structure. Compared with traditional channel pruning, [PERSON] et al. [18] accelerated neural networks based on an acceleration-aware finer-grained channel pruning method. [PERSON] et al. [19] proposed a channel clipping method for ultra-deep CNN compression, in which the trimming of the channel was formulated as a search problem solved by a genetic algorithm. [PERSON] et al. [20] developed an effective channel-clipping method based on LASSO regression and least-squares reconstruction, which accelerated VGG-16 by five times with a 0.3% error increase. [PERSON] et al. [21] proposedAdaPrune, which adaptively switches pruning between the input channel group and the output channel to accelerate deep neural networks.  
The most important problem of network pruning is the determination of the redundant unit in the network, while there is no clear criterion to solve this problem. Most methods require repeated experiments, which results in high costs of human and material resources.
###### Abstract  
Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8 YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (\(RMS\) = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA\(\cdot\)cm\({}^{-2}\)). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0-48%vol H\({}_{2}\), 0-38%vol CO, 0-45%vol CH\({}_{4}\), 9-32%vol CO\({}_{2}\), 0-54%vol N\({}_{2}\), specific equivalent hydrogen flow-rate per unit cell active area 10.8-23.6 mL\(\cdot\)min\({}^{-1}\cdot\)cm\({}^{-2}\), current density 0-1300 mA\(\cdot\)cm\({}^{-2}\) and temperature 700-800 \({}^{\circ}\)C).  
fuel cells; SOFC; syngas; low-carbon fuels; modelling; controllers; artificial intelligence; neural networks; energy systems; electric
## 1 Introduction  
The European Commission recognized the relevance of digital competence (DC) among citizens by releasing many publications (e.g., DigCompEdu, DigCompOrg, DigCompConsumers) and aiming to bring a digital transformation to society while focusing on five areas of DC [1]: (1) information and data literacy, (2) communication and collaboration, (3) digital content creation, (4) safety, and (5) problem-solving. In a broader sense, DC \"involves the confident, critical and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society\" [2].  
The DC acquisition and assessment should be started early in primary schools [3; 4; 5]. Furthermore, it should be integrated into the formal educational curriculum [6; 7]. This approach would enable schools to identify a lack of specific DCs and introduce a plan for their implementation. More studies are found on the assessment of DC in higher education [8] than in primary and secondary education [9]. In general, validated assessments are essential in primary and secondary education because they help students improve their learning, develop necessary abilities, and measure their progress [10]. A three-year longitudinal study [11] further contributes to that matter by questioning whether the application of designed DC teaching can accelerate the natural course of DC development.  
Therefore, it is necessary to study the effectiveness of the implementation of DC development systems that could enable young students to acquire the desired set of DCs. In this respect, new cloud-based teaching methodologies and services that enable DC acquisition in primary and secondary schools in Europe (hereinafter referred to as the CRISS platform) were developed. The CRISS platform was piloted from 2017 until 2019 as a part of the Horizon 2020 project. This platform aims to deliver user-driven and adaptive technological solutions that allow guided acquisition, evaluation, and certification of DC in primary and secondary education. It was used both by students and teachers.  
This study aims to assess the implementation effectiveness of the CRISS platform as an online DC certification system in primary and secondary schools following the well-known DeLone and McLean [12; 13] Information System Success Model (D&M Model). In that sense, the following research questions (RQs) are prompted:  
* RQ1: What factors impact teachers' perspectives on the successful implementation of an online DC certification system in primary and secondary schools?
* RQ2: What are the relationships between those factors?
## 5 Conclusions  
This study develops an intelligent monitoring and early-fault-warning system for a wind turbine gear transmission system based on digital twin technology, multi-source operational data, and deep learning models. Through the collaborative optimization of multi-source data fusion and deep learning algorithms, the system's ability to perceive conditions and predict faults is significantly enhanced. The research findings indicate that the introduction of digital twin technology not only enables real-time interaction between the physical system and its virtual model but, more importantly, creates a multi-dimensional  
Figure 26: Fault alarm module.  
Figure 25: Residual plot of the different algorithm models.  
real-time monitoring and analysis framework. This led to a qualitative improvement in the visualization and predictability of the system's operational status. The proposed hybrid CNN-LSTM-Attention prediction model, by virtue of its mechanisms for joint spatio-temporal feature extraction and attention weight allocation, maintained high prediction stability even amid noise interference. The proposed model reduces RMSE and MAPE values by an average of 0.3945 and 0.546, respectively, while increasing the r2 value by an average of 0.061 compared to the other models, indicating that the proposed CNN-LSTM-Attention model achieves higher prediction accuracy and a better fit.  
Although the method proposed in this study has yielded significant results in monitoring wind turbine gear transmission systems, enabling more precise and efficient fault early warning, there are still several areas that need improvement. Since the training data is primarily sourced from a specific turbine model, the model's cross-model generalization capability is limited. Concurrently, the real-time dynamic updating mechanism of the digital twin model has not been fully automated, necessitating manual intervention in parameter optimization and model iteration. Therefore, future research will focus on developing cross-model adaptive algorithms based on transfer learning, constructing an intelligent framework for autonomous model updates, and enhancing the system's robustness in complex environments so as to promote the broader application of this technology in practical engineering.  
Conceptualization, T.X. and X.Z.; methodology, T.X.; software, X.Z.; validation, X.Z.; formal analysis, X.Z.; investigation, T.X.; resources, W.S.; data curation, T.X.; writing--original draft preparation, T.X.; writing--review and editing, T.X.; visualization, X.Z.; supervision, W.S.; project administration, W.S.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
Special Project for Local Science and Technology Development Guided by the Central Government (ZYYD2025 JD07).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Changes of Electrode Morphologies before and after Discharge  
To investigate the performance of the W electrode and Fe-Ni electrode under higher currents, scanning electron microscopy (SEM) was used to analyze the morphology of the two electrodes after 200 mA discharge. The results are shown in Figure 7, which presents the SEM images of the two electrodes after undergoing 10,000 cycles.  
Prior to discharge, the tungsten electrode had well-distributed spike surface structures after hydrothermal treatment, and no defects were observed (Figure 7a). After discharge, only minor changes were observed on the surface of the electrode (Figure 7b). These changes were attributed to the slight agglomeration of tungsten under high current. This small difference indicates that the tungsten electrode is capable of with  
Figure 6: The relationship between light intensity and current.  
The morphological characteristics of the Fe-Ni alloy electrode before and after discharge are illustrated in Figure 7c.d. Prior to discharge, the electrode surface was uniformly coated with the Cs\({}_{2}\)O electron powder coating; however, after discharge, the coating became lumpy and cracks appeared on the surface of the electrode.  
Among several commonly used metals, tungsten (W) has the highest melting point and the largest emission constant. Even if its surface is slightly damaged, tungsten can continue to act as the cathode and maintain the discharge. Therefore, as the current increases, the W electrode will not be damaged and the plasma density remains increased.  
However, for Fe-Ni electrodes, when the Cs\({}_{2}\)O electron powder coating is damaged, the base metal will not be able to maintain the discharge as a cathode. This is because the Si-containing nickel metal is doped with iron in this electrode. If it is used as a cathode directly, the iron will be distributed on the surface of the base metal, which will significantly reduce the emissivity of the electrode. Therefore, an excessive current will destroy the oxide coating and damage the Fe-Ni electrode. As a result, the plasma density generated will decrease and no longer change with the current.
###### Abstract  
The Karapinar basin, located in the Central Anatolian part of Turkey, is subjected to land subsidence and sinkhole activity due to extensive groundwater withdrawal that began in the early 2000s. In this study, we use Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level data to monitor and better understand the relations between groundwater extraction, land subsidence, and sinkhole formation in the Karapinar basin. The main observations used in the study are InSAR-derived subsidence velocity maps calculated from both Sentinel-1 (2014-2018) and COSMO-SkyMed (2016-2017) SAR data. Our analysis reveals broad areas of subsidence with rates exceeding 70 mm/yr. The InSAR-derived subsidence was compared with GNSS data acquired by a continuously operating GNSS station located in the study area, which show a similar rate of subsidence. The temporal characteristic of both InSAR and GNSS time series indicate a long-term subsidence signal superimposed by seasonal variability, which follows the overall groundwater level changes, with over 80% cross-correlation consistency. Our results also indicate that sinkhole activity is limited to slow subsidence areas, reflecting strong cohesion of near-surface rock layers that resist subsidence but yield to collapse in response to aquifer system deformation induced by groundwater extraction.  
InSAR; subsidence; sinkhole; COSMO-SkyMed; sentinel 1; groundwater overpumping  
1  
[PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Land Subsidence and Its Relations with Sinkhole Activity in Karapinar Region, Turkey: A Multi-Sensor  
10.3390/s201030774  
18 December 2020  
21 January 2021  
1
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.5 Acids
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{6}{c}{**Differences in Concentration**} \\ \cline{4-13}
**Co.** & **Volatile Compounds** & **ID** & \(\text{LRI}_{\text{e}}\) & \(\text{LRI}_{\text{l}}\) & & \multicolumn{6}{c}{**One-Way ANOVA**} & \multicolumn{6}{c}{**Two-Way ANOVA**} \\ \cline{4-13}  & & & & & \multicolumn{2}{c}{**2021**} & \multicolumn{6}{c}{**2022**} & \\ \cline{4-13}  & & & & & & & & & & & & & \\ \cline{4-13}  & & & & & & & & & & & & & \\ \hline AL48 & 2-Phenylethanol (mg/L) \(\ddagger\) & A, B, C & 1891 & 1893 & 34.56 & 30.45 & **19.07 \({}^{\text{a}}\)** & 14.64 \({}^{\text{b}}\) & SCE & **21** & n.s. \\ AL49 & 1,4-Butanediol & B, C & 1918 & 1911 & 1.11 & 1.37 & 0.45 & 0.52 & n.s. & **21** & n.s. \\ AL50 & 2-Ethyl-2-(hydroxymethyl)-1,3- & B & 1926 & - & 0.20 & 0.23 & n.d. & n.d. & - & - & - \\ \cline{4-13}  & propaneediol & B, C & 1968 & 1973 & 1.90 & 1.41 & **1.97 \({}^{\text{a}}\)** & **1.54 \({}^{\text{b}}\)** & SCE & n.s. & n.s. \\ AL51 & 1-Dodecanol & B, C & 2092 & 2090 & n.d. & n.d. & n.d. & 0.13 & 0.09 & - & - & - \\ AL53 & 2-Phenoyethanol & B, C & 2147 & 2144 & 0.93 & 0.54 & 3.36 & 1.29 & n.s. & n.s. & n.s. \\ \hline \hline \end{tabular} Abbreviations: Co.—compound’s code; ID—identification of compounds: A—retention time accordant with that of a pure standard; B—mass spectra accordant with that from NIST 2.0, Wiley 8, and FFNSC 2 mass spectra electronic libraries or literature; C—linear retention index (LRI) accordant with an index from literature (compounds with only B in the ID column were considered tentatively identified); LRI—experimental linear retention index; LRI\({}_{\text{l}}\)—linear retention index from literature; n.s.—not significant; n.d.—not detected. One-way ANOVA section: different superscript lowercase letters represent statistically significant differences between the two investigated wines determined by one-way ANOVA at \(p\) < 0.05 for each harvest year separately. Two-way ANOVA section: designations are reported representing yeast (SCE, SPA) and harvest year (21, 22) with higher concentration determined by two-way ANOVA at \(p\) < 0.05 with several (\(\gamma\)) and harvest year (H) as factors. An asterisk indicates a significant interaction between the two factors (Y \(\times\) H). Significant differences are highlighted in bold.  
\end{table} 
Table 8: _Cont._
## 3 Results  
### Behaviour of the Substrate during Composting  
For technological reasons, the process was conducted until the waste was biologically stable, i.e., the AT4 value did not exceed 10 g O\({}_{2}\cdot\)kg\({}^{-1}\) DM. Thus, the composite process lasted for 45 days.  
Generally, the most important indicator of composting process efficiency is the temperature, which increases as a direct result of the exothermic decomposition of organic substances in the reactor. During the experiment, the temperature peaked twice: at 63 \({}^{\circ}\)C on the 4 th day, followed by a lesser peak at 36 \({}^{\circ}\)C on the 32 nd day (Figure 2A).  
During the 45 days of composting, the mass of the substrate decreased by 78 kg, which is equivalent to 40.37% of the total initial mass (Figure 2B). The main reason for this mass reduction was water evaporation: the changes in moisture and LOI during this period indicate that the water loss from the reactor was approximately 60 kg, and the loss of organic matter was approximately 21 kg (note that the mass of the mineral compounds increased by approximately 3 kg). The final mass balance is presented in Table 2; however, it should be noted that an accurate mass balance was not possible to calculate because some of the water in the reactor may have been produced in the process of organic matter decomposition.  
While the water mass in the reactor gradually decreased, the moisture content of the waste in the reactor fluctuated. Initially, the moisture decreased from 65% to 52.5%, then, starting on the 20 th day of the experiment, it slowly increased to 57% and then remained stable at that level (Figure 2C).  
During the progressive decomposition of organic matter in the reactor, its particle size distribution changed. Initially, there were five size fractions: <10 mm, 10-40 mm, 40-60 mm, 60-80 mm, and 80-100 mm; however, by the 30 th day of the study, there were only three:  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Timepoint** & **Mass** & **Moisture** & **LOI** & **Water** & **Organics** & **Minerals** \\  & **[kg]** & **[\%]** & **[\% DM]** & **[kg]** & **[kg DM]** & **[kg DM]** \\ \hline
0 d & 193.20 & 65.00 & 81.00 & 125.58 & 54.77 & 12.85 \\
45 d & 115.00 & 57.00 & 68.00 & 65.55 & 33.63 & 15.82 \\ \hline Difference & -78.20 & -8.00 & –13.00 & –60.03 & –21.15 & +2.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mass balance of the process.  
Figure 2: (**A**) Temperature, (**B**) mass, (**C**) moisture, and (**D**) size fractions of the substrate material at sampling points during the 45 days of composting in the passively aerated bioreactor.  
\(<\)10 mm, 10-40 mm, and 40-60 mm (Figure 2D). Over the course of the 45-day process, the proportion of the \(<\)10 mm fraction increased from 15.5% to 32.7%, that of the 10-40 mm fraction increased from 58.3% to 70.1%, and that of the 40-60 mm fraction decreased from 20.6% to 1.8%.
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 3 Mf Signal Impairments  
The propagation of MF signals is characterized by a variety of impairments which need to be considered when these signals are used for positioning purposes. From this point of view, the main two challenges in obtaining an accurate positioning performance in the MF R-Mode system are the following: mitigating the sky-wave reflection arriving from the ionosphere at night, and predicting the wave-propagation delay induced by the variation of the electrical properties of the atmosphere and ground along the propagation path. In the following sections, the two issues will be briefly described. Furthermore, a third issue concerning the possible transmitter instability is also discussed.
## 4 Numerical Results and Discussions  
In this section, we provide simulation results to evaluate the proposed joint power control and resource allocation (JPCRA) scheme. Based on the above derivation, we notice that the system parameters such as interference threshold, power and PU interference value have an effect on the max-min achievable rate \(R_{\text{min}}\). Thus, in the simulation part, we first simulate and analyze the effects of these parameters on \(R_{\text{min}}\) in Figures 2-4. To better demonstrate the superiority of the proposed JPCRA scheme, we compare it with the end-to-end achievable rates in Figure 5 and compare with three traditional optimization schemes in Figures 6-8. The three traditional optimization schemes are the joint optimal power splitting and power allocation with fix time (FT-JoPSPA) scheme, joint optimal time and power allocation with fix power splitting (FPS-JoTPA) scheme and joint optimal time and power splitting with fix power allocation (FPA-JoTPS) scheme.  
Figure 4: The max-min achievable rate vs. the interference to the cognitive network caused by PU.  
Figure 5: The max-min achievable rate vs. the end-to-end achievable rates aiming at maximizing the achievable sum rate.  
Figure 3: The max-min achievable rate vs. the maximum transmit power constraint of \(S_{1}\).  
Figure 8: The max–min rates of different optimization schemes vs. the transmit power constraint of \(S_{1}\).  
Figure 6: The max–min rates of different optimization schemes vs. the interference threshold of PU.  
Figure 7: The max–min rates of different optimization schemes vs. the transmit power constraint of \(S_{1}\).
## 5 Results and Discussion
### Evaluation of the Time Complexity Estimates versus the Parallel Execution Time  
The goal of parallelization is to execute processes in parallel by distributing the computing workload across numerous processors or computers. This makes it possible to perform multiple tasks at the same time. It has the potential to cut down on the total amount of time required for the algorithm's execution and to improve its adaptability to larger networks. However, this does not have a direct effect on the algorithm's asymptotic complexity. The complexity analysis typically considers the sequential execution of the algorithm, without accounting for parallelization. So, the theoretical complexity of the algorithm remains the same. However, parallelization can offer a practical performance improvement by exploiting the available parallel processing resources. This improvement is often reflected in reduced execution time, which is valuable for large-scale networks. Figures 7 and 8 represent the comparison of the derived time complexity and the actual running time for Lancichinetti-Fortunato-Radicchi (LFR) datasets [70] of varying sizes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Network (LFR(N, k, mark, mu, min_c, max_c))** & **Proposed Method** & **GA with NED Index** & **GA with Community Score** \\ \hline (128, 16, 16, 0.1, 32, 32) & 1.0000 & 1.0000 & 0.9901 \\ (333, 10, 16, 0.2, 10, 30) & 0.8836 & 0.8532 & 0.8701 \\ (1500, 15, 15, 0.1, 20, 50) & 0.7530 & 0.7980 & 0.7461 \\ (5000, 20, 40, 0.1, 30, 60) & 0.7594 & 0.7422 & 0.7321 \\ (10,000, 20, 30, 0.2, 100, 200) & 0.7236 & 0.7254 & 0.7198 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized mutual information (NMI) obtained for the synthetic benchmark datasets with the ground truth available.  
Figure 7: Derived time complexity.  
It can be observed that when parallel execution was introduced to the proposed model, there was a considerable reduction in the execution time for each of the networks. This observation highlights the positive impact of parallelization on the overall performance of the algorithm.