esa-sceva/satcom-chunk-collection huggingface dataset:
# New On-Line MFL Testing Method and Apparatus for Winding Mine Hoist Wire Rope
## 2 In-Service WMH Wire Rope and Its NDT Challenge  
The WHM is a traditional hoisting equipment, with one end of the wire rope wound around a winch, and the other end around a guide wheel to adjust the direction of the connection to the transport cage. In the case of wire rope work, as shown in Figure 1, the wire rope is released or retrieved by rotating the winch forward and backward to produce relative movement along the wire rope axis. As the wire rope rotates around the winch, the rope body shakes from side to side and deviates from the central axis, producing the deviation angle \(\theta\). Moreover, with changes in tension and speed, the rope sways frequently from the groove of the drum, creating a specific amplitude of \(\delta\). Accordingly, along the wire rope axis direction, to establish a spatial coordinate system, the wire rope motion state can be divided into linear motion along the X axis, horizontal wobble along the Y axis, and vertical vibration along the Z axis.  
In addition, the surface of the wire rope is coated with oil for protection, and the traditional leakage detection methods (leakage detection devices with magnetic yokes) are not suitable for online high-speed detection due to the small spacing between the wire rope and the wire rope. Otherwise, it will cause scraping oil and wire rope wear, and cannot withstand the friction and impact caused by the relative high-speed movement of the wire rope and the probe, thereby reducing their service life. Therefore, it is necessary to use non-contact measurement methods, and the lifting distance (the distance between the wire rope surface and the probe) should be maintained in order to ensure normal operating conditions in a non-contact setting. Essentially, there are several issues that must be addressed for the on-line detection of wire ropes, as follows:  
(1) Probes must be able to open and hold wire ropes for closed-loop structures; therefore, 360-degree closed-loop structures and devices (including excitation and receiving devices) are not available.  
(2) Ropes can run at about 15 m/s, and the rope has twining movements at the same time, so any detection method must satisfy the requirements of high-speed operation in addition to tracking movement and ensuring safety.  
(3) The dusty environment, dirty oil, complicated vibration movements, and anti-explosion requirements prevent the use of heavy equipment. As a result of maintenance or replacement, a rope's posture may change. Thus, the testing equipment should be capable of automatically adapting to a variety of rope positions.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 2 Analysis of the Current Situation and Limitations of Bottom-Up Monitoring Methods
### Fire Radiation Power Method
As the bottom-up approach relies primarily on the IPCC inventory method, it is challenging to accurately capture the dynamic changes in emission sources due to the lag in updating statistical data and emission factors. Furthermore, there is a lack of third-party independent verification of \"sky-ground-space\" data, which could enhance the reliability of the results. This results in considerable discrepancies between the various carbon emission inventories. Furthermore, the definition standards and statistical quality of activity data vary from country to country, which makes it unfeasible to effectively establish a reliable global biomass burning emission inventory.
## 2 Related Work  
In this section, we initially examine the related work on computer-aided technology in ICH segmentation, followed by a primary focus on the research progress on neural networks in medical image segmentation.  
Over time, various traditional segmentation algorithms utilizing diverse techniques such as region growing [9; 10; 11], thresholding [12], watershed [13], active contours [14], clustering [15], and more have been introduced. In recent times, deep learning-based techniques have gained increased prominence for achieving superior segmentation accuracy [16; 17; 18]. [PERSON] et al. [19] employed the Distance Regularized Level Set Evolution (DRLSE) method to fit the active contour of the hemorrhagic region. Following preconditioning, the hemorrhagic areas of IVH and IPH were processed with an enhanced DRLSE. [PERSON] et al. [20] integrated fuzzy C-means (FCM) clustering with an improved range-regularized level set evolutionary algorithm (MDRLSE) to detect and isolate cerebral hemorrhage regions. [PERSON] et al. [21] suggested an approach based on spatial fuzzy C-means and region-based active contour. [PERSON] et al. [22] proposed a CT image hemorrhage segmentation method relying on fuzzy C-means clustering, automatic cluster selection, skull removal, threshold segmentation, and an edge-based active contour technique. [PERSON] et al. [23] introduced the white matter fuzzy C-means clustering method and employed wavelet threshold technology to segment the hemorrhagic area.  
In recent years, due to the continuous advancement of deep learning, an increasing number of neural networks have been applied to the domain of medical image segmentation. These networks can extract the features of focal areas from training images and autonomously set them, thereby avoiding manual partitioning operations. Fully convolutional networks (FCNs) [24] are utilized in image semantic segmentation, representing the first end-to-end fully convolutional network aimed at pixel-level prediction. Since the network's introduction, it has served as the foundational framework for semantic segmentation, with subsequent algorithms primarily improving upon this network framework. Launched in 2015, U-Net has gradually become the most prominent medical image segmentation network, integrating various levels of semantic information to achieve optimal segmentation results for medical images with simple semantics. However, for more complex medical image segmentation tasks, its performance falls short of clinical practice requirements. Currently, most medical image segmentation networks are enhanced based on the U-Net [25] network, including U-Net++ [26], Attention U-Net [27], and ResU-Net [28]. With the rise of the transformer [29] structure, the TransU-Net [30] network has also emerged for medical image segmentation. Among them, [PERSON] et al. [31] utilized a Dense U-Net framework for the automatic segmentation of intracranial hemorrhage, while [PERSON] implemented a 2D U-Net for segmenting intracranial hemorrhage, achieving a Dice coefficient of 0.31 [32].
## 4 Experimental Results  
The performance of video retrieval on MAR is tested upon UCSB dataset [29] and AVD8 dataset [10]. In order to measure the performance of the proposed approach we computed the efficiency and mean Average Precision (mAP). The average precision (AP) is computed based on a single query (image or video) to validate the retrieval performance whereas, mean Average precision (mAP) is  
Figure 3: Proposed feature descriptor, Pyramid Ternary Histogram of Oriented Gradient (PTHOG).  
measured by computing the mean value over multiple queries, which was the final measurement of the video retrieval performance.
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
###### Abstract  
Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet's carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebraz Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.  
chiorophyll fluorescence; wetlands; vegetation monitoring; machine learning; biodiversity; Sentinel-2 2023152392[[https://doi.org/10.3390/rs15092392](https://doi.org/10.3390/rs15092392)]([https://doi.org/10.3390/rs15092392](https://doi.org/10.3390/rs15092392))  
1
## 3 Results and Discussion  
EDM die sinking of tungsten carbide was attempted using ten different tool designs. The tool designs are mainly of three types named as conventional design (D\({}_{\text{C}}\)), relief angle design (D\({}_{\text{R}}\)), and relief angle with land design (D\({}_{\text{RL}}\)). The machining performance was evaluated in terms of machining time (MT), taper angle (\(\omega\)), radial overcut (ROC), radial undercut (RUC), and surface roughness (SR) of the eroded holes. Experimental results revealed that each of the response characteristics significantly varied as the tool design is changed. For example, the machining time to complete the 4 mm through a hole in WC varied from 172 to 338 min, and the taper angle varied from 3.84 to 21\({}^{\circ}\). The micrographs of the machined holes are shown in Figure 4. These micrographs consisted of enlarged images of the hole entrance and hole exit corresponding to each electrode design. The effect of each of the electrode designs and their variants on the set responses was analyzed through comparative bar-charts as discussed in the following section.  
Figure 3: Measurements of hole diameters and surface roughness: (**a**) schematic and actual measurement on coordinate measuring machine (CMM) and (**b**) schematic and actual measurement on Surtronic 128.
## 2 Model-Based Analysis of MSK Function
### Model Customisation  
First, a customised MSK model needs to be generated. This is typically done by scaling a generic model to match the subject's gross anthropometry or by utilising subject-specific geometry obtained from medical imaging.  
Scaling of a generic model is performed with the aim of matching the gross anthropometry of a subject. This process linearly scales each body (e.g., femur and pelvis) to match measured segment lengths, anthropometric table values, or most commonly, experimentally acquired motion capture marker positions [8; 9; 10]. Along with model geometry, other parameters including joint positions, segment mass and inertia, and MTU pathways are also scaled.  
Alternatively, personalised models can be used [11; 12; 13; 14; 15]. Using medical imaging segmentation, bone and joint (i.e., cartilage and ligament) geometries and positions, and MTU pathways can also be personalised [11; 16; 17]. Different types of medical imaging allow the inclusion of different personalised parameters, for example, using computer tomography (CT) information regarding bone geometry can be obtained, however for information regarding soft tissues (i.e., muscle, ligaments, and cartilage) magnetic resonance imaging (MRI) is required. In the absence of complete medical imaging segmentations, statistical shape modelling methods can be utilised to reconstruct complete subject-specific geometry from incomplete data [16; 18; 19; 20]--see Section 9. The use of 2D ultrasound (US) can provide information regarding MTU parameters (e.g., tendon resting length), and combined with motion capture (i.e., 3D US), information regarding MTU and bone geometry can be obtained [21; 22; 23; 24; 25; 26]. The collection of muscle strength data, via dynamometry allows for the customisation of the maximal force-generating capacity (i.e., maximal isometric force) of muscle groups. Further, these dynamometry data can be used to further refine parameters within the previously mentioned Hill-type muscle model [27; 28; 29; 26]. Despite the vast number of parameters which can be personalised, the inclusion of the above personalised parameters is uncommon and building fully subject-specific MSK models is rare given the complexity of the additional data collection. Typically, a selection of these personalised data is used to customise existing generic models.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 4 Conclusions  
In this paper, Machine Learning models were applied to a dataset available online. The data set used was published by Microsoft, in [9], in a Notebook for Predictive Maintenance and Machine Learning. The use of this data set was justified. In the implementation carried out in the present project, until the final phase of feature engineering, the steps presented in that Notebook were followed. However, from that moment on, as it is considered that the approach presented in [9] is too simplistic (no validation technique is used and only a single model is applied), it was decided to deepen the analysis with the implementation of the Holdout validation, which divides the data set into three subsets (Training, Validation and Test), as well as various Machine Learning models, thus showing how to fine-tune the models and respective hyper-parameters using the validation set.  
The fact that it is a multi-class classification problem added complexity to the analysis and, perhaps, starting with a binary classification problem may be advisable for a better understanding of the basic concepts of Machine Learning, fundamental to the success of any application.  
It is possible to address the imbalance between classes, very common in maintenance applications, since the occurrence of failures is rare during the life cycle of a given machine, when compared to its normal operation.  
At the outset, and knowing that a sensible choice of which data to use and how to handle it is crucial for the performance of Machine Learning algorithms, good results would be expected based on the result obtained. However, more important than any result was the demonstration of a methodology, starting from data of different types and sources (very common in maintenance applications), that allowed us to show how it is possible to visualize and treat them in order to apply Artificial Intelligence tools in the analysis of maintenance data, in this case, Machine Learning.  
Although the results obtained compare well with those presented so far in the literature, the biggest disadvantage in using the presented methodology lies in the definition of the features. If the selection of features is not the most correct, the results obtained can lead to wrong predictions. For future work, the application of feature learning concepts will be considered instead of feature engineering, which appears to be promising to improve the results obtained [35; 36].  
D.C.: developed and performed the work during is M.Sc. thesis; [PERSON]: supervised the work and prepared and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 4 Research Method  
The collected rockburst samples were divided into three different sizes (40, 70 and 100), among which the proportion of samples in the training set and test set was 7:3. Four dimensionless methods were selected to process the prediction indexes, and the processed data were used as the input layer of the BP neural network for training and testing. The gradient descent algorithm and four optimization algorithms were used to construct training functions, and different BP neural network models were established. Under different sample sizes, the 1#-3# optimal models were selected initially, and then the best optimal model was finally selected from the three optimal models for engineering applications. Figure 3 shows the flow chart of model optimization.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Level** & **Characteristic** & \(\sigma_{\theta}\)/\(\sigma_{c}\) & \(\sigma_{c}\)/\(\sigma_{t}\) & \(W_{\text{et}}\) \\ \hline \multirow{4}{*}{I} & mean & 0.20 & 24.49 & 2.99 \\  & median & 0.20 & 23.00 & 2.00 \\  & minimum & 0.10 & 6.70 & 1.40 \\  & maximum & 0.43 & 47.90 & 7.40 \\  & standard deviation & 0.20 & 36.00 & 2.30 \\ \hline \multirow{4}{*}{II} & mean & 0.43 & 20.77 & 2.94 \\  & median & 0.42 & 20.40 & 2.90 \\  & minimum & 0.22 & 6.80 & 0.90 \\  & maximum & 0.67 & 34.30 & 5.10 \\  & standard deviation & 0.37 & 24.00 & 5.10 \\ \hline \multirow{4}{*}{III} & mean & 0.57 & 24.20 & 4.41 \\  & median & 0.56 & 21.85 & 4.30 \\  & minimum & 0.28 & 9.50 & 2.00 \\  & maximum & 0.84 & 55.00 & 7.10 \\  & standard deviation & 0.40 & 14.70 & 7.10 \\ \hline \multirow{4}{*}{IV} & mean & 0.72 & 19.08 & 6.18 \\  & median & 0.68 & 17.25 & 6.25 \\ \cline{1-1}  & minimum & 0.42 & 11.20 & 2.00 \\ \cline{1-1}  & maximum & 1.41 & 32.20 & 10.90 \\ \cline{1-1}  & standard deviation & 0.58 & 13.20 & 6.30 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical parameters of rockburst prediction indexes.  
Figure 2: Box plot of prediction indexes: (**a**) \(\sigma_{\theta}\)/\(\sigma_{c}\); (**b**) \(\sigma_{c}\)/\(\sigma_{t}\); (**c**) \(W_{\text{et}}\).
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 3 Results
### Details Recovery
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **T** & **E** & **OC** \\ \hline Original (\(\chi\)) & 0.011 \(\pm\) 0.022 & 0.0013 \(\pm\) 0.035 & \(\sim\)144 \\ Corrected (\(\Upsilon\)) & 0.141 \(\pm\) 0.012 & 0.0215 \(\pm\) 0.003 & \(\sim\)867 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Texture (T), Entropy (E), and Object Count (OC) table. The table represents the average metrics for both the original (\(\chi\)) and the output images (\(\Upsilon\)). Figure 6: Segmentation Masks (SM). The upper part represents the Correctly-Exposed Mask of the original images (\(M_{CEx}\)). The lower part depicts the same mask on the pipeline results (\(M_{CEy}\)). HSI (Hue Similarity Index): Hue Similarity Index is a metric used to measure the similarity between the hue values of the original and corrected images. We calculated HSI as the Pearson correlation coefficient [38] on the Hue channel of the HSV version of the original and corrected images. Figure 7 shows the box plot of the HSI values for the original and corrected hue channels. Our results demonstrate a linear relationship between the two pairs of images, indicating that our pipeline effectively preserves colour consistency. Visual Inspection: To demonstrate the effectiveness of our procedure, we compare the results (\(Y\)) obtained by applying the pipeline to the initial images (\(X\)) with the reference images (\(R\)), as shown in Figure 8.  
Figure 8: Visual comparison. The illustration shows a comparison of the original image (\(X\)), the image produced by the pipeline (\(\Upsilon\)), and the benchmark image (R). Figure 7: The figure reports the box plot of the Hue Similarity Index (HSI) for the original and corrected hue channels.
# Germanium Single Crystals for Photonics
## 1 Introduction
The electrophysical and optical parameters of Ge are closely interrelated, which is due to the dependence of the absorption coefficient on the concentration of electroactive impurities, even at relatively low concentrations. Due to this feature, it became possible to successfully test Ge blanks for IR photonics based on measurements of specific electrical resistance (\(\rho\)) and conductivity type [8; 10; 11]. Taking into account that natural germanium is a mixture of its stable isotopes, the optical properties of all five germanium isotopes in the range of 0.2-3000 \(\upmu\)m were studied in [47; 48; 49]. For isotopes, an isotopic shift of phonon absorption bands in the IR region was recorded, discovered for the first time. Germanium has a number of nonlinear properties. It should be especially noted that germanium is an acousto-optic material with unique properties, including in the THz range [8; 50; 51; 52; 53; 54; 55; 56]. Ge is currently used to manufacture transparent optics components for thermal imaging devices; moreover, germanium is a system-forming material for IR photonics for the atmospheric window of 8-14 \(\upmu\)m. Ge components are used in a large number of optical devices where mechanical strength, good thermal properties, and climatic resistance are required. The share of various applications is about 25-30% of the total consumption of this material [8].
## 3 Maintenance and Lifespan Management
### Advanced Decision-Making Systems
#### 3.3.2 DER, DR and Efficiency Energy  
[PERSON] and [PERSON] [212] present a MAS-based distributed SEDMS to perform optimal energy allocation and management for grids comprising of DER, storage and DG. By controlling the state of charge (SoC) of storage it is possible to guarantee a balance in power. All control is done through a fuzzy system.  
As [PERSON] et al. [213] shown, DR is possible at the household level, so they define an intelligent algorithm for correct decision-making aimed at achieving DR.  
In many countries, specifically in the United States, the relationship between season and peak demand is different between sectors (residential, commercial and industrial (C&I), etc.), and this is analyzed by [PERSON] [214].  
[PERSON] and [PERSON] [215] focus on which of those SG functionalities are going to facilitate the ever-growing need for enhanced DR and load control.  
[PERSON] et al. [216] propose four criteria for the decision making of seven actions in a _SG_. The authors use a method based on fuzzy AHP multi-criteria for the improvement of energy efficiency.