esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 3 The Proposed Adaptive Sparse Attention Network (ASAN)
### The Diagnosis Process  
Figure 4 depicts the proposed fault diagnosis method's flowchart. First, the original mechanical vibration signals are collected by sensors. Then, the vibration data are preprocessed to conform to the input format of the model, and training samples and test samples are made accordingly. The network obtains fault diagnosis results through training and learning, and the knowledge learned in the diagnosis process is well explained through corresponding visualization methods.  
Next, the network configuration is selected from the architecture presented in Section 3.2, based on the information in the dataset and the particular issue with fault diagnosis. The detailed network structure includes the number of layers, the number and size of convolutional kernels at the convolutional layer, the number of neurons, etc., which are mainly determined by model verification in experimental research, with vibration data as the input of the model. Network biases and weights are initialized with the Xavier standard initializer.  
The BP (Back-Propagation) algorithm was used to update all parameters in the network, and the target was minimized in small batches using the Adam optimization technique. The learning rate is 0.001, and after 300 training, the proposed network loss function converges on the whole. Table 1 displays the corresponding parameters.  
Finally, the test fault diagnosis results are obtained by incorporating the test samples into the proposed model following the completion of the training phase. At the same time, the fault feature region that is most relevant to the result can be located through the model, which lays a foundation for the subsequent difficult fault research and diagnosis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of training & 300 \\ Learning rate & 0.001 \\ Conv1 convolution kernel size & 3 \\ Conv1 number of convolution kernels & 8 \\ Conv2 convolution kernel size & 3 \\ Conv2 number of convolution kernels & 16 \\ LSTM hidden layer unit & 16 \\ Dropout & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network parameter settings.  
Figure 4: Flowchart of fault diagnosis method.
The following is an overview of the principal contributions:  
* An effective intrusion detection system was developed using two enhanced hybrid deep learning models, Transformer-DNN and Autoencoder-CNN. The transformer extracts contextual features for pattern analysis, while the DNN performs final classification. sification. The Autoencoder reshapes data, preparing it for precise classification by the CNN. * Enhanced hybrid ADASYN-SMOTE resampling is leveraged for binary classification, while enhanced SMOTE resampling is applied for multi-class classification. These techniques are combined with ENN to effectively address class imbalance and enhance model performance. * Integrating the enhanced local outlier factor (LOF) strengthens anomaly detection by detecting and removing outliers, significantly boosting the model's ability to identify minority class attacks and improving overall detection performance. * Evaluation on the CICIDS2017 and NF-BoT-IoT-v2 datasets demonstrated the superior performance of the proposed models compared with state-of-the-art approaches. This paper is structured as follows. Section 2 provides a comprehensive review of the related literature. Section 3 describes the methodology employed in this study. Section 4 presents the results obtained from the experiments, while Section 5 offers a detailed discussion of the findings. Section 6 addresses the limitations of the proposed approach. Section 7 concludes the study by summarizing its key contributions and insights. Finally, Section 8 outlines potential directions for future research.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 5 Conclusions  
In this study, we conducted a comparative assessment of the Plant Phenology Index (PPI) and Normalized Difference Vegetation Index (NDVI) in mountainous regions, specifically focusing on the European Alps. NDVI and PPI values were extracted from the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite constellation for the years 2018 to 2022.  
Regarding seasonal dynamics, NDVI exhibited notable signal noise during the snow seasons (late autumn to early spring) and for coniferous trees. In contrast, PPI demonstrated lower noise levels.  
In terms of seasonal parameter retrieval, we observed close concordance between the two indices, with minor discrepancies in start and end-of-season estimation for deciduous trees, grasslands, and shrublands. However, substantial differences were noted for peak-of-season estimation and all seasonal parameters pertaining to coniferous trees.  
Our findings align with existing literature and offer novel insights into vegetation monitoring in complex environments. Overall, this study's primary contribution lies in exploring the potential of PPI for vegetation dynamics monitoring in mountainous regions of the European Alps, providing a robust alternative to address NDVI limitations in these environments characterized by seasonal snow and coniferous forests. As such, the PPI represents a good candidate for linking long-term regional vegetation trends to year-to-year phenological and growth responses to climate change across diverse topoclimatic contexts and life forms in mountainous environments.  
Conceptualization, D.C., L.F. and G.G.; methodology, D.C., L.F. and G.G.; formal analysis, D.C.; data curation, D.C.; writing--original draft preparation, D.C., L.F. and G.G.; writing--review and editing, D.C., L.F. and G.G.; visualization, D.C.; supervision, G.G.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Dataset available on request from the authors.  
We would like to thank [PERSON] (CNRS) for his valuable advice on shrublands mapping.  
The authors declare no conflict of interest.
## 4 Early Warning of Rockburst Based on Microseismic Indices
### Multi-Index Comprehensive Early Warning  
By establishing the relationship between the microseismic index and the rockburst, the early warning of rockbursts can be quantitatively realized. However, because the microseismic indices are more or less incomprehensive or insufficient, it can easily cause misjudgment when used alone. Therefore, it is necessary to analyze the evolution law of the multiple microseismic indices and comprehensively judge the precursors for rockbursts. The realization of multi-index comprehensive early warning has become the mainstream research direction in the early warning of rockbursts.  
Based on the relationship between the spatiotemporal evolution characteristics of microseismicity and rockbursts, [PERSON] et al. [14] put forward four rockburst criteria based on the density of microseisms, the relationship between the magnitude and frequency of microseisms, the magnitude of microseisms, and the degree of energy concentration. [PERSON] et al. [62] established a functional relationship between the microseismic index and rockburst through typical rockburst cases, and realized the real-time early warning of the occurrence probability of strainbursts and strain-structure slip rockbursts with weighted coefficients. The microseismic indices selected were the cumulative microseismic event number, cumulative microseismic energy, cumulative microseismic apparent volume, microseismic event rate (day or hour), microseismic energy rate, and microseismic apparent volume rate. [PERSON] et al. [63] selected 74 examples of rockbursts as training samples and used the cumulative number of events, cumulative energy, and cumulative apparent volume as input indices to identify the scale of a rockburst. A neural network optimized by the genetic algorithm was used to predict the level of rockburst, and the prediction accuracy on the verification set was 83.9%. Based on a fuzzy model, [PERSON] et al. [64] used the F score of the performance index in the confusion matrix to determine the weight of each microseismic index and combined the maximum membership principle with variable fuzzy pattern recognition to quantitatively predict rockbursts.  
Summing up the methods and common elements used in these studies, they can be collectively referred to as multi-index comprehensive early warning. The multi-index comprehensive early warning for rockbursts involves establishing several mathematical models between the microseismic indices and rockburst law on the basis of rockburst mechanism research and field monitoring, to predict the location, level, and risk probability of rockburst. Moreover, it is necessary to correct the early warning model based on the actual rockburst feedback of a given project and realize the dynamic adjustment of theearly warning results. In addition, through the introduction of fuzzy mathematics, neural network, and other mathematical methods for modeling, we can also achieve intelligent early warning.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Soil Leves and Embankments Monitoring
#### 3.1.2 Distributed Strain Sensing for Soil Levees and Embarkments  
The assessment of anomalous strain field conditions within soil levees and embankments is a challenging task, as it requires fine spatial resolution and high sensitivity at the same time, as even a small displacement may lead to the structural collapse very rapidly. A typical setup is shown in Figure 11: basically, it is assumed that critical deformations of levee body caused by erosion, slope failure, water overtopping, or piping would determine soil displacement measured by DOFSs. Both cables and geotextiles are used, deployed longwise the levee, preferably at the downstream surface.  
In 2000, Brillouin DOFS was proposed to measure the strain exerted on three metallic sensor plates, 19 m long, installed at different levels along the downstream toe of a full-scale physical model of a river levee under artificial rainfall [147]. Temperature cross-sensitivity was tackled by subtracting the Brillouin shift at the sensor transducers to the Brillouin shift at a portion of fibre kept in strain-free condition. The experiment suggested the feasibility of collapse early detection. In the years following, other authors then claimed the feasibility of Brillouin strain sensing for embankment monitoring [148; 149; 150].  
In 2002, [PERSON] et al. [151] proposed the use of pieces of cloth fixed to an optical fibre cable at 1.5 m intervals to ameliorate the cable-soil coupling. The resulting cable with enhanced friction was then embedded in a U-shaped configuration in a river embankment and was able to detect soil movements of a few millimetres. The authors concluded that the sensor was able to provide warning of the collapse of a river embankment resulting from water penetration.  
In 2010, [PERSON] et al. [138] at the IJkdijk site described the use of geotextiles with embedded fibre-optics in several positions of 1:1 scale physical model of a levee. Fibres were installed at the crest, in the middle of the slope, at the downstream toe, and 2 m from the downstream toe. The geotextile embedded both single-mode and multimode fibres interrogated by BOTDA and  
Figure 11: A schematic representation of the application of distributed optical fibre sensors (DOFSs) for strain sensing in a levee, using geotextiles (from [144]).  
Raman-DTS interrogators, respectively. The experiment confirmed the capability of the system to detect and localise small soil strains in the structure (less than 0.02%).  
Finally, geotextiles integrating polymer optical fibres (POFs) [152; 153] and interrogated using \(\
u\)-OTDR and I-OFDR were also proposed for the monitoring of embankments [154]. Figure 12 shows some samples of geotextiles with embedded fibres. One of the main features of POFs is the very large sustainable strain--up to 16% and more (Figure 12). Although promising, the short range of measurement attainable with current POF fibres (up to 500 m for low-loss perfluorinated POF) poses serious limitations to embankment monitoring.
# Metallurgical Effects of Niobium and Molybdenum on Heat-Affected Zone Toughness in Low-Carbon Steel
## 3 Results
### Mechanical Characteristics of HAZ Sub-Zones
Particularly the Nb-free alloy softens  
Figure 5: Heat-affected zone simulation of alloy V3: (**a**) Evolution of hardness and yield-to-tensile ratio (YTR) and measured transformation temperatures during up-heating; (**b**) microstructure in the inter-critical zone (ICZ) of the Nb-free alloy V3-a; (**c**) microstructure of alloy V3-d containing 0.10%Nb (etching with Le Pera agent). less, which is a known effect caused by Mo. The 0.1%Nb steel, on the contrary, shows a greater hardness loss in the CGZ compared to the base material. This can indicate less precipitation hardening by niobium in the CGZ. The YTR indicates less softening in the sub-critical zone of the Nb-free alloy while the dual phase character in the ICZ (YTR = 0.54) is identical to alloy V3. The YTR in the CGZ is somewhat lower as in alloy V3, regardless of the Nb content. Reducing the silicon content in alloy V6 leads to somewhat more softening of the Nb-free alloy in the ICZ as compared to alloy V4. The hardness is however still higher than in alloy V3 for the same condition. The 0.1%Nb containing steel V6 shows no softening in the CGZ. Regarding the YTR, a comparably higher minimum value of 0.57 is observed for the Nb-free alloy in the ICZ indicating a less pronounced dual phase character. For the 0.1%Nb containing steel, the YTR in the ICZ is also higher than in the previous alloys. Although these differences appear to be relatively modest, the observed effects can be attributed to the silicon alloy variation. In dual phase steel production silicon is standardly used to promote the partitioning of carbon to inter-critical austenite and, thus, to stabilize it for transformation into martensite upon quenching. This effect is based on silicon increasing the activity of carbon and also preventing the precipitation of cementite. The low-Si alloys exhibit the highest YTR values in the CGZ. The transformation temperature A\({}_{\text{c1P}}\) is very similar for the three Nb-free variants of V3, V4 and V6 ranging between 804 and 810 \({}^{\circ}\)C whereas the A\({}_{\text{3 cp}}\) temperature is lower for the Mo-alloyed variants. The addition of 0.1%Nb clearly shifts the A\({}_{\text{c1P}}\) temperature upwards, particularly in combination with Mo-alloying. This is also the case for the A\({}_{\text{c3P}}\) temperature in alloys V3-d and V4-d. Remarkably, the combined alloying of Mo and 0.1%Nb at reduced Si content leads to a much lower A\({}_{\text{c3P}}\) temperature, diminishing the transition range to only 37 K while it is between 110 and 130 K for the other alloys (Figures 5 and 6).
## 4 Results and Discussions  
### Effect of Compressive Strength on Bond Strength  
In this section, the effect of concrete compressive strength on the bond behavior of GPC is investigated. Three types of geopolymer concrete were conducted with three diameters reinforcing bar d12, d16, and d20. The specimens were cured at 60 \({}^{\circ}\)C for 4, 8, and 12 h. The correlation between compressive strength and bond strength is illustrated in Figure 7.  
According to Figure 7, the bond strength of geopolymer concrete increases with the increase of compressive strength. For specimens using d12, the bond strength increases about 2.16 times when the compressive strength increases from 20 MPa to 41 MPa. For a group of d16 and d20, the increase is about 2.56 and 1.97, respectively. In this study, the difference in compressive strength of the geopolymer concrete comes from the curing conditions. For all three groups of geopolymer concrete, only one mixture was chosen for making specimens. However, the curing condition was selected as 60 \({}^{\circ}\)C for 4, 8 and 12 h. Based on the previous research [44], more product of geopolymerization process is produced with a longer curing time. Thus, the structure of geopolymer concrete is denser and results in higher compressive strength. This trend is similar to the results of a previous study [45].
## 4 Hardware Architecture
### Hardware Architecture of PR-Trie and NHI Lookup Modules  
The PR-Trie Lookup module resolves the P-Trie data, as shown in Figure 8, and the P-Trie and R-Trie bitmaps are used to compute the OHT bitmap, to obtain the offset.  
Figure 6: Parallel architecture of CBHF module.  
Figure 7: Detail of Level 3 submodule.  
The P-Trie address is involved in calculating the NHI base address and potential on-chip imaginary root address, then the off-chip NHI address generated by the base address plus the offset is obtained.  
In the NHI Lookup module, as shown in Figure 9, the DDR3 interface employs a DDR3 memory controller generated by the Xilinx Memory Interface Generator (MIG). The NHI address is applied to query the off-chip memory, while the imaginary root address is used to query the on-chip imaginary root table and its result will be stored in a FIFO buffer until the off-chip NHI searching is completed. Then, the off-chip output port bitmap is selected as the correct NHI if it is not zero (i.e., 16b0), otherwise, the on-chip output port bitmap from the imaginary root table will be picked out.
## 5 Axial Piston Pump Simulation Test
### Performance of the Proposed DTW-RCK-IF Composite Method in This Article
#### 5.3.2 Parameter Sensitivity  
We investigated the effect of the number of random convolutional kernels (100, 200, 500, 1000, 2000, and 5000) on the overall performance of the proposed method. Figure 15 illustrates the model performance for different random convolutional kernel numbers. It can be observed that increasing the number of kernels effectively enhanced the model performance as long as the number remained below 1000. The model achieved optimal performance when the number of kernels reached 1000, demonstrating high classification accuracy. However, as the number of kernels continued to increase to beyond 1000, the variation in the model performance became relatively small. Therefore, we selected 1000 as the number of random convolutional kernels for this method. These results validate the sensitivity of the proposed method, which is essential for practical applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LOF**} & \multicolumn{2}{c}{**OCSVM**} & \multicolumn{2}{c}{**Isolation Forest**} & \multicolumn{2}{c}{**DTW-RCK-IF**} \\  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline normal & 100.00 & 63.41 & 77.61 & 100.00 & 65.85 & 79.41 & 100.00 & 73.17 & 84.51 & 100.00 & 97.56 & 98.77 \\ swashplate wear & 73.21 & 100.00 & 84.54 & 74.55 & 100.00 & 85.42 & 78.85 & 100.00 & 88.17 & 97.62 & 100.00 & 98.80 \\ sliding shoe wear & 73.21 & 100.00 & 84.54 & 74.55 & 100.00 & 85.42 & 78.85 & 100.00 & 88.17 & 97.62 & 100.00 & 98.80 \\ single-plunger loose & 73.21 & 100.00 & 84.54 & 74.55 & 100.00 & 85.42 & 78.85 & 100.00 & 88.17 & 97.62 & 100.00 & 98.80 \\ shoe & 79.91 & 90.85 & 82.81 & 80.91 & 91.46 & 83.92 & 84.14 & 93.29 & 86.51 & **98.22** & **99.39** & **98.79** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of anomaly detection using the DTW-RCK-IF method.
## 4 Discussion
### NOx and Ozone Measurements  
Exposure limits for a given airborne chemical, such as ozone, vary depending upon the regulator. Different countries have different limits, and these limits change over time as standards are redefined. Moreover, a given geographical location may be subject to more than one regulatory body, which can cause confusion. Internationally respected regulatory bodies include the U.S. National Institute for Occupational Safety and Health (NIOSH), U.K. Health and Safety Executive (HSE), European Parliament and The Council of The European Union, and the World Health Organisation. Previous work conducted by Adtec used the NIOSH and HSE standards.  
The NIOSH standards [18] define the nitrogen monoxide (NO) exposure limit as 25 ppm, nitrogen dioxide (NO\({}_{2}\)) exposure limit as 1 ppm, and ozone (O\({}_{3}\)) exposure limit as 0.1 ppm. The HSE standards [19] define the nitrogen monoxide (NO) eight-hour exposure limit as 25 ppm for the underground mining and tunnelling industries and 2 ppm for all other activities, there is no 15-min exposure limit; nitrogen dioxide (NO\({}_{2}\)) eight-hour exposure limit as 0.5 ppm, the 15-min exposure limit is 1 ppm, and ozone (O\({}_{3}\)) 15-min exposure limit as 0.2 ppm, there is no eight-hour exposure limit.  
Clearly, just comparing these two standards, there is inconsistency limits and the time periods over which those limits exist. The NOx measurements shown in this paper are a sum of the nitrogen monoxide (NO) and nitrogen dioxide (NO\({}_{2}\)) counts added together and are orders of magnitude lower than the safety limits defined above. The ozone (O\({}_{3}\)) measurements shown in Figures 7 and 8 show that 25 mm from the SteriPlas cage levels are below the NIOSH and HSE limits; it should be noted that the NOx and ozone limits apply to the inhalation of the gases and not to the exposure to human skin, as long as no patient or operator has their mouth and/or nose within 25 mm of the SteriPlas cage it will be completely safe.