esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Accuracy Rate Analysis  
The original model and the improved model were trained and tested. In the experiment, the two models experienced overfitting in the later training stage, so the best checkpoints were recorded as the test results. The accuracy rates of the two models in the task are shown in Tables 4 and 5.  
The experimental results indicated that the GraphDIVA model significantly improved the accuracy rate of the marked tasks compared with the original model. In the /film/film/written _by task, GraphDIVA increased the accuracy rate by 4.7% compared with the original model. In the/tv/tv/program_languages task, the original model had an extremely high accuracy of 96%, and GraphDIVA had an accuracy of 98%. The improvement in the personborninlocation task was the most obvious: the original model obtained an accuracy of 78.4%, and GraphDIVA obtained an accuracy of 85.3%, which is 6.9% greater. Finally, the average accuracy rates of GraphDIVA in the high-discrimination tasks were 3.2 and 3.7% higher than those of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **GraphDIVA** & **DIVA** \\ \hline /film/director/film & 0.565 & 0.565 \\ /film/film/language & 0.728 & 0.728 \\ /film/film/written\_by & 0.756 & 0.709 \\ /people/person/nationality & 0.862 & 0.834 \\ /tv/tv/program\_languages & 0.980 & 0.960 \\ Average value & 0.778 & 0.759 \\ Average value of the high-discrimination tasks & 0.866 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FB15K-237 test results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline guided\_learn\_rate & 0.001 \\ guided\_max\_epoch & 25 \\ guided\_max\_path\_width & 5 \\ unified\_max\_epoch & 20 \\ unified\_posterior\_learn\_rate & 0.01 \\ unified\_likelihood\_learn\_rate & 0.0001 \\ unified\_prior\_learn\_rate & 0.001 \\ unified\_max\_path\_width & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **GraphDIVA** & **DIVA** \\ \hline /film/director/film & 0.565 & 0.565 \\ /film/film/language & 0.728 & 0.728 \\ /film/film/written\_by & 0.756 & 0.709 \\ /people/person/nationality & 0.862 & 0.834 \\ /tv/tv/program\_languages & 0.980 & 0.960 \\ Average value & 0.778 & 0.759 \\ Average value of the high-discrimination tasks & 0.866 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 5: NELL-995 test results.  
original model; this showed a significant performance improvement and proved that graph structure features have a significant impact on inference tasks. Additionally, for noisy tasks, the accuracy rate of GraphDIVA was the same as that of the original model, and there was no excessive sensitivity to noise as a result of the addition of graph structure features, reflecting the practicality of the improvement. The test results are shown in Figure 4.
# NiO-Based Electronic Flexible Devices
## 3 NiO-Based Flexible Devices
### Resistive Random Access Memory  
Resistive random access memory (ReRAM) is considered the most promising among the flexible non-volatile memories (NVMs) due to its simple metal-insulator-metal (MIM) structure, high switching speed, low power consumption, low operating temperature, and high packaging density. Flexible ReRAM is essential in smart wearable electronics and all-in-one fully flexible electronic system, since flexible memory is a fundamental component for data processing, storage, and communication with external devices.  
NiO based ReRAM has the advantage of stable switching properties at specific conditions, lower operation set/reset current and forming voltage in comparison to other metal oxides. A flexible NiO ReRAM device with good operation characteristics was achieved, based on a solution process using NiO nanocrystals (NC), on flexible PET substrate [104]. The construction of the ReRAM devices consisted in the deposition of a 100-nm-thick platinum (Pt) and a 20 nm-thick titanium (Ti) adhesion layer on a polymer substrate using an e-beam evaporator. A dispersion of NiO-NCs and isopropyl-alcohol (IPA) was dropped on the flexible PET substrate, and the NiO-NC solution pressed by a poly--dimethylsiloxane (PDMS) mold while heating at 80 \({}^{\circ}\)C. The excess of coated NiO-NC solution leaking out, as well as the residual layer, were removed and the PDMS mold was demolded from the substrate, leaving a NiO nanopillar array fabricated on the substrate without NC residue.  
Figure 3: Flexible NiO-based plastisol free-standing electrode. (**A**) Photograph of the flexible, corropable electrode. (**B**) An example of a customized shape of NiO-plastisol sheet and an indication of the dimensions. (**C**) SEM images of front and back sides the NiO-plastisol electrodes obtained with NiO of different morphologies (Reproduced from [103], with modifications).  
The electric signal and bias voltage were probed by a Pt/Ti-coated Si AFM tip contacted on nanoscale ReRAM devices, by applying a voltage on the top electrode, while the bottom electrode was grounded, and the resistance values of the NiO ReRAM were read at 0.01 V. The flexible NiO ReRAM device exhibited unipolar resistive switching behaviors corresponding to those of an evaporated NiO thin film. The switching behavior was initiated at 11 V with 0.04 V and 1 \(\upmu\)A current compliance by a conductive filament forming operation. For the reset switching of the NiO flexible ReRAM, the voltage was swept from 0 V to 5 V with 0.02 V. The resistance of the NiO ReRAM device changed from 102 \(\Omega\) to 107 \(\Omega\) at 2.4 V for reset switching, and the set switching was operated in reverse at 3.4 V. The flexibility of the device is demonstrated by a nearly 180\({}^{\circ}\) bending as shown in Figure 4, along with the SEM imaging indicating the NiO-NC patterning of the PET substrate.
# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Terrain Model Generation  
Tests are performed on the Innsbruck East triplet data set where the DSM is derived with 1 m GSD. For DSMs with lower or higher resolution, the processing parameters very likely have to be appropriately tuned to achieve optimal filtering results. For all tests the values given in Table 13 have been used.  
Figure 24: Selected DSM subsets with \(800\times 800\) m\({}^{2}\) and 1 m GSD of the Ljubljana test site. Height ranges from 410 m to 500 m. Shown are (**a**) LiDAR reference, (**b**) Pleiades DSM based on all stereo pairs with convergence angles in the range of \(20^{\circ}\) to \(30^{\circ}\), (**c**) Pleiades DSM based on stereo pair 1–2 and (**d**) Pleiades DSM based on stereo pair 1–3. Dark blue pixel indicate non-reconstructed regions.
## 1 Introduction  
As unmanned aerial vehicle (UAV) [1] and Internet of Things (IoT) technologies are developing, they are used for weather observation [2], agriculture [3], and military purposes [4]. However, not only physical signal attacks, such as GPS Signal Spoofing and Jamming Signals [5] but also malware and malicious communication attacks, such as spoofed signals, are being launched [6]. These attacks are classified as a major threat because they can also be used against unmanned aerial vehicles (UAVs). Accordingly, cyber-attack attempts targeting unmanned aerial vehicles (UAVs) are increasing [7], and research to detect them is actively underway [8; 9]. These include spoofing, Denial of Service (DoS), and Replay attacks on CAN protocols against UAVs [10].  
Research using machine learning (ML) and deep learning (DL) models to detect network intrusions occurring in the CAN protocol [11] is also underway, but the performance of these models is not stable because the feature learning process of ML/DL models cannot be verified. Unfortunately, researchers cannot utilize the patterns analyzed by the ML/DL model [12; 13; 14; 15; 16]. In addition, stacking techniques that use an ensemble of different types of models are being used to improve detection, but this results in poor performance. Recently, it has become possible to analyze the Feature Importance and SHAP value of ML/DL models using [PERSON]'s explainable artificial intelligence (XAI) research [15; 16] and the SHAP technique. Solved cases [17; 18] are also emerging.  
This study validates an ensemble model that can simultaneously detect more than one intrusion in a drone network. Related studies have typically addressed studies on single-model intrusion detection verification in drone networks [19; 20; 21]. Our hypothesis proposes a way to effectively identify multiple needle types through ensemble models. This ensemble model uses a stacking method that combines a single model trained for the binary classification of each attack. Therefore, this study will extend the detection range by utilizing an assembled ensemble model based on performance evaluation and feature importance analysis on ML/DL models trained on individual attacks. Additionally, this study will validate an effective ensemble method for detecting multiple intrusions by analyzing ML/DL models using SHAP value analysis.
## 3 Results and Discussion
### Bioprinting  
Based on the results of the printability assessment (Section 3.5), the cell-laden SA-G bioink was printed at a speed of 10 mm/s and a pressure of 25 kPa. Post-printing cell viability was assessed by staining living (green) and dead (red) cells. High cell viability (>98%) was observed after 1 day of culture (Figure 8a,b) and was maintained at day 5 (Figure 8c), with no abnormal cell death detected. This is consistent with the in vitro cytotoxicity findings (Section 3.4), confirming that the SA-G bioink does not negatively affect cell viability. In addition, the high post-printing viability indicates that the shear stresses generated during bioink printing did not cause major cell death. Ultimately, Figure 8 demonstrates a homogeneous cell distribution throughout the printed constructs.  
Figure 7: (**a**) Representative microscopy image of a single printed filament. Scale bar = 200 μm. (**b**) Average line width at different printing speeds and pressures using a 22G conical nozzle. Error bars represent the standard deviation. (**c**) Picture of the result of a filament fusion test. Scale bar = 5 mm. (**d**) Picture of porous circular discs. Scale bar = 5 mm. All the structures were printed using SA-G bioink. SA-G: sodium alginate–gelatin.  
These results pave the way for incorporating more relevant cell types, such as primary stem cells, into the bioink. In this study, a well-established immortalized cell line (L929 cells) was selected for preliminary biocompatibility screening, as there is no photoreceptor cell line able to comprehensively recapitulate photoreceptor phenotypes [4]. Further investigations are needed to assess the impact of the SA-G bioink on the behavior of primary cells.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 4 Discussion
The station land covers are a mixture of grasslands, temporary crops followed by harvest and bare soil periods, and woody savanna characterized by forest canopy cover between 30% and 60% and vegetation height exceeding 2 m. In this land cover context, the vegetation volume impacts the radar signal and complicates the soil moisture retrieval. We observe the vegetation impact within many stations in the SMOSMANAIA network, such as the Mazan-Abbaye, Cabriers Avignon, and Ville Vieille stations occupied by trees or shrubs. Furthermore, the use of _NDVI_ as a vegetation descriptor may induce other limits, such as the availability of data in regions with temperate climates. The presence of clouds contaminates the surface reflectance, which damages the radiometric information. As a result, many time series suffer from gaps and lack data, which complicates the training and validation of the proposed model, such as the case of some stations of the USCRN network, where the mean _RMSE_ value is equal to 0.06 m\({}^{3}\)/m\({}^{3}\). By considering the GROW network data, the _RMSE_ is equal to 0.07 m\({}^{3}\)/m\({}^{3}\). This relatively low accuracy in retrieving soil moisture may be linked to the predominant cold climate of the considered stations. This low-temperature climate may impact the radar signal, especially with the freeze-thaw phenomenon. This change in the physical state of the soil water content generates a fast variability in the Sentinel-1 signal, as discussed for agricultural plots in metropolitan France by [PERSON] et al. [83] and [PERSON] et al. [84].
# The Influence of Co-Firing Coal with Biomass Syngas on the Thermodynamic Parameters of a Boiler
## 3 Case Study  
A subcritical 300 MW tangentially fired boiler, the type of which is SG-1025/17.50-M749, is applied for the case study. The intricate boiler structure can be seen from Figure 2. The main design parameters of the boiler under BMCR, 75% THA (turbine heat acceptance), 50% THA, and 30% THA are shown in Table 1. The design parameters are adopted under (BMCR), with the main steam output of 1025 t h\({}^{-1}\), and the outlet temperatures of both superheated steam andheated steam of 540 \({}^{\circ}\)C. The height, width, and depth of the furnace are 50,868, 14,020, and 12,350 mm, respectively. The wall reheater, platen superheaters, and high-temperature superheaters are sequentially arranged in the upper part of the boiler, while the horizontal convective superheater and economizer are installed in the tail flue vibration from top to bottom. The chosen biomass syngases in this study are palm syngas, straw syngas, and wood syngas from previous studies [30; 31; 32]. The biomass syngas can be obtained through the gasification of the biomass by a gasifier. The processes consist of desiccation, volatile release, oxidation, and reduction. Then, the tar and dust removal are required at high temperatures. Finally, the biomass syngas should be cooled at 450 \({}^{\circ}\)C before entering the furnace since the temperature of biomass syngas should be controlled below 500 \({}^{\circ}\)C [2]. The biomass syngas are injected into the furnace at the bottom level of the reburn zone in this study since NO emissions were at their lowest value in this condition according to the results in our previous study [8]. The details about the coal and biomass syngases are listed in Table 2.  
Figure 2: Boiler structure.  
Since the fuel burnt in the boiler changes from coal to biomass syngas and coal, the thermal calculations are performed with the verification method. The comparisons of thermodynamic parameters for various biomass syngas consumption rates are conducted under a full load. The biomass syngas consumption rates coupled with coal vary from 0 to 5 \(\times\) 10\({}^{4}\) m\({}^{3}\) h\({}^{-1}\). Moreover, the comparisons of thermodynamic parameters between burning coal alone and co-firing coal with 3 \(\times\) 10\({}^{4}\) m\({}^{3}\) h\({}^{-1}\) of wood syngas (taking the safety and economy into consideration) are performed with different boiler loads. The results of thermal calculations are similar to that the boiler manufacturer provided, which means the thermodynamic parameters calculated by our developed program are reliable and valid. The following assumptions are made after the introduction of biomass syngas:  
1. The excess air coefficient remains unchanged at the exits of the furnace and convective heating surfaces;
2. The temperatures of the cold air and hot air, and the proportions of primary and secondary air remain unchanged;
3. No variations happen in the fuel characteristics, boiler structure, and boiler capacity.
## 4 Prediction Accuracy and Efficiency Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Model** & **WS0102** & **WS0103** & **WS0104** & **WS0105** & **WS0106** & **WS0107** \\ \hline \multirow{6}{*}{RMSE (mm)} & T-GCN & 2.917 & 3.558 & 3.256 & 4.542 & 2.984 & 1.725 \\  & T-GCN (rain) & 2.001 & 2.405 & 3.775 & 6.383 & 3.713 & 1.429 \\ \cline{1-1}  & T-GCN & 1.922 & 1.754 & 1.591 & 4.132 & 2.371 & 1.184 \\ \cline{1-1}  & T-GCN & 1.407 & 2.299 & 2.795 & 6.621 & 1.801 & 1.170 \\ \cline{1-1}  & (rain + reservoir water level) & & & & & & \\ \hline \multirow{6}{*}{MAE (mm)} & T-GCN & 2.223 & 2.995 & 2.679 & 2.763 & 2.285 & 1.330 \\  & T-GCN (rain) & 1.575 & 1.727 & 3.245 & 4.506 & 3.208 & 1.101 \\ \cline{1-1}  & T-GCN & 1.356 & 1.202 & 1.235 & 2.294 & 1.988 & 0.937 \\ \cline{1-1}  & T-GCN & 1.093 & 1.630 & 2.328 & 4.945 & 1.544 & 0.914 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Displacement prediction accuracy of T-GCN model with different external influencing factors. Figure 9: Displacement prediction results of T-GCN model with different external influencing factors.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 4 Results and Discussion
### Results for Frequency Analysis  
In this section, the results of the frequency analysis, which was conducted to identify the most relevant frequency components for each fault type, are presented. Different types of engine faults often exhibit distinct frequency components. These components emerge due to the engine's physical structure and operational principles, with each fault generating unique sounds or vibrations at specific frequency ranges. Therefore, examining these frequency components is crucial for accurate fault diagnosis using sound analysis. Table 7 comprehensively outlines the relationship between the fault categories and their associated frequency groups.  
For spark plug issues, the most relevant frequency bin is 5.2 to 5.3 kHz, indicating that monitoring this high-frequency range is crucial for accurate detection. Airflow irregularities are most prominently indicated by the 3.0 to 3.1 kHz bin, highlighting the need for precise analysis in this low-frequency range. Engine/turbo problems are best identified by the 0.7 to 0.8 kHz bin, suggesting these faults manifest through specific low-frequency sounds. Front-end problems are primarily associated with the 6.6 to 6.7 kHz bin, essential for identifying issues related to components such as the suspension or chassis. Electrical malfunctions affecting the engine show the highest relevance at the 2.1 to 2.2 kHz bin, possibly due to distinctive noise patterns. These findings underscore the importance of focusing on these key frequencies for accurate fault detection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multicolumn{5}{c}{**Most Relevant Frequency Bins (kHz)**} \\ \cline{2-6}  & **Bin 1** & **Bin 2** & **Bin 3** & **Bin 4** & **Bin 5** \\ \hline Spark Plug Issues & 5.2–5.3 & 9.4–9.5 & 12.4–12.5 & 13.2–13.3 & 14.1–14.2 \\ Airflow Irregularities & 3.0–3.1 & 9.1–9.2 & 9.2–9.3 & 10.0–10.1 & 12.2–12.3 \\ Engine/Turbo Problems & 0.7–0.8 & 4.8–4.9 & 9.5–9.6 & 12.1–12.2 & 12.5–12.6 \\ Front-End Problems & 6.6–6.7 & 8.5–8.6 & 8.9–9.0 & 11.6–11.7 & 13.5–13.6 \\ Electrical Malfunctions & 2.1–2.2 & 11.2–11.3 & 11.4–11.5 & 12.1–12.2 & 14.3–14.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The most relevant frequencies for each fault type.  
Figure 6: Confusion matrix for decomposition level of 3 and sym8 wavelet using DWT-based features.  
Overall, the frequency analysis confirms that different fault types are associated with specific and most relevant frequency bins. The identification of these relevant frequency bins provides valuable guidance for future diagnostic efforts, suggesting that including these specific frequencies in the analysis can lead to better and more reliable fault detection outcomes. This insight into the frequency components of various faults enhances our understanding and ability to diagnose vehicle issues more accurately and efficiently.
## 4 Prototype Analysis of Specim Failure
Based on the method of [PERSON] and [PERSON] [30;31], et al., the brittleness index of the above limestone samples was quantitatively calculated, and the brittleness degree of the failure of each limestone sample was represented as data using the following calculation formula:  
\[B=B_{d1}+B_{d2} \tag{2}\]  
\[B_{d1}=\frac{\lg(\frac{1}{\varepsilon_{p}})}{K} \tag{3}\]  
\[B_{d2}=\frac{\left(\sigma_{p}-\sigma_{r}\right)}{\sigma_{p}t}+\frac{\left( \varepsilon_{r}-\varepsilon_{p}\right)}{\varepsilon_{p}t} \tag{4}\]  
where \(B\) is the rock brittleness indicator, \(B_{d1}\) is the indicator of brittleness before rock peaks, \(B_{d2}\) represents the indicators of brittleness after rock peaks, \(\sigma_{p}\) is the uniaxial compressive strength of the rock, \(\sigma_{r}\) is the residual strength of the rock, \(\varepsilon_{p}\) is the corresponding strain when the stress reaches its peak, \(\varepsilon_{r}\) is the strain corresponding to the residual strength of the rock, \(t\) is the time interval between the peak strength point on the stress-strain curve and the starting point of the residual strength, and K is a constant (\(K=5\)). The parameters of the limestone samples with different water content rates are shown in Table 5.  
The brittleness index B values of the calculated limestone are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Test Piece Number** & **E1** & **E8** & **E9** & **E10** \\ \hline \(B_{d1}\) & 0.40 & 0.50 & 0.39 & 0.39 \\ \(B_{d2}\) & 0.77 & 0.64 & 0.67 & 0.65 \\ B & 1.17 & 1.14 & 1.06 & 1.04 \\ \hline \end{tabular}
\end{table} 
Table 6: Graystone brittleness index values. \begin{table}
\begin{tabular}{c c c c c} \hline
**Test Piece Number** & \(\sigma_{p}\) **(MPa)** & \(\varepsilon_{p}\) **(\%)** & \(\sigma_{r}\) **(MPa)** & \(\varepsilon_{r}\) **(\%)** & **t (s)** \\ \hline E1 & 74.11 & 1.04 & 7.86 & 1.38 & 1.5 \\ E8 & 64.73 & 0.94 & 21.02 & 1.42 & 1.7 \\ E9 & 63.70 & 1.08 & 17.23 & 1.36 & 1.4 \\ E10 & 57.60 & 1.09 & 15.39 & 1.23 & 1.3 \\ \hline \end{tabular}
\end{table} 
Table 5: Parameters required for calculating the brittleness index of limestone samples with different water content rates. The calculation results of the brittleness index B value were plotted as shown in Figure 7.  
According to the results presented in Table 6 and Figure 7, when the water content rate of the limestone is 0%, 0.07%, 0.10%, and 0.27%, the brittleness index B values are 1.165, 1.142, 1.060, and 1.044, respectively. The above analysis shows that with an increase in the limestone water content rate, the brittleness index of the limestone shows a downward trend. This result also verifies the conjecture that the brittleness of the limestone decreases gradually with an increase in the limestone water content rate. Whether or not the plastic deformation of the limestone increases with an increase in the limestone water content rate will be discussed in the energy analysis section.