esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
###### Abstract  
Advanced technologies in modern industry collect massive volumes of data from a plethora of sources, such as processes, machines, components, and documents. This also applies to predictive maintenance. To provide access to these data in a standard and structured way, researchers and practitioners need to design and develop a semantic model of maintenance entities to build a reference ontology for maintenance. To date, there have been numerous studies combining the domain of predictive maintenance and ontology engineering. However, such earlier works, which focused on semantic interoperability to exchange data with standardized meanings, did not fully leverage the opportunities provided by data federation to elaborate these semantic technologies further. Therefore, in this paper, we fill this research gap by addressing interoperability in smart manufacturing and the issue of federation different data formats effectively by using semantic technologies in the context of maintenance. Furthermore, we introduce a semantic model in the form of an ontology for mapping relevant data. The proposed solution is validated and verified using an industrial implementation.  
predictive maintenance; Industry 4.0; semantic technologies; semantic interoperability; data federation; ontology engineering Article
## 3 YOHO LDC Dataset  
The considered dataset for this work is a specialized and public corpus, namely, the YOHO speech dataset managed and published by LDC [5]. This speech dataset contains a large-scale, high-quality speech corpus to support text-dependent speaker authentication research used in secure access technology. The data were collected in 1989 by ITT under a US government contract but have not been available for public use before. The number of trials is thus sufficient to permit evaluation testing at high confidence levels. The corpus is divided into \"enrolment\" and \"verification\" segments, containing data from all 138 speakers. The enrolment session is the training session, and the verification session is conducted in another period to test the dataset. Figure 1 details the dataset files. The speakers are numbered in the range of 101 up to 277 (138 speakers). Each speaker has four enrolment sessions per subject (as shown in the enroll part) with 24 phrases per session with a total of 96 files per speaker for training and ten test sessions per subject (as shown in the verify part) with four phrases per session with a total of 40 files per speaker for testing.  
In each session, a speaker was prompted with a series of phrases to be read aloud. Each phrase was a sequence of three two-digit numbers (e.g., 35-72-41, pronounced thirty-five,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Speaker Recognition**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{3-4}  & & & **SI** & & & & \\ \hline
[18] & 2020 & - & ✓ & & MFCCs, Pitch & Correlation & 92.00 \\ \hline
[11] & 2021 & VoxCeleb1 & ✓ & ✓ & Spectrogram & VGG ResNet &
\begin{tabular}{c} SV: 5.33 EER \\ SI: 93.80 \\ \end{tabular} \\ \hline
[12] & 2023 & - & ✓ & & MFCCs & CNN & 92.46 \\ \hline
[14] & 2023 & VoxCeleb1 & ✓ & & Two-branch network & SVM & 97.20 \\ \hline
[15] & 2023 & \begin{tabular}{c} RAVDESS \\ SUSAS \\ ESD \\ \end{tabular} & ✓ & \
\begin{tabular}{c} Voice Segregation, \\ Speech VGG \\ \end{tabular} & New pipeline &
\begin{tabular}{c} 85.20 \\ 87.00 \\ 86.60 \\ \end{tabular} \\ \hline
[19] & 2023 & VoxCeleb1 & ✓ &
\begin{tabular}{c} Pre-feed-forward \\ feature extractor \\ \end{tabular} & SANs & 94.38 \\ \hline
[20] & 2023 & DEMoS & ✓ &
\begin{tabular}{c} Spectrogram, \\ MFCCs \\ \end{tabular} & CNN & 90.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: YOHO dataset files (numbers from 101 up to 277 present the speaker index; below them the enroll and verify sessions are listed).  
seventy-two, and forty-one). Each speaker had four enrolment sessions of 24 utterances each and ten verification sessions of four utterances each, 136 utterances in 14 sessions per speaker, as illustrated in Table 2. The sample rate for the speech files was 8 kHz, and the total data were 1.5 gigabytes. Related speaker verification and identification work were evaluated on the selected dataset [21].
## 3 Latency-Classification-Based Deadline-Aware Task Offloading Algorithm (LCDA)
### Latency Classification Based on Deadline-Aware Task Offloading Algorithm  
We combine the task analysis, task selection, and server selection functions described above to create the proposed LCDA. Algorithm 3 represents the complete offloading and scheduling process. The task analysis and selection functions are responsible for analyzing and selecting tasks in the LCDA, respectively. The process in lines 1 to 6 is executed when the number of existing tasks changes by more than 20%, and the process in lines 7 to 10 is executed only when offloading occurs. The task analysis and selection functions are responsible for the task management for offloading in the LCDA. The server selection function is responsible for finding a target server for offloading. If an appropriate task cannot be identified by the task analysis function or task selection function, or if the server selection function cannot find a suitable edge cloud server, the LCDA determines that offloading a task to the edge cloud server is not possible--the LCDA will try to offload the task to the central cloud server or continue executing the task on the current user device. In the next section, we validate the LCDA's latency performance. Throughout this technique, we assume that LCDA does not take into account the scheduling of VMs in edge and central cloud servers. Because we focus on the rapid processing of latency-sensitive tasks, it is assumed that all VMs in the edge and cloud servers are running concurrently.  
```
1:for all task\({}_{i}\), where \(\textit{device}_{i}\in\textit{Device}\), \(\forall_{i}\in\{\)1,2,... \(n\}\);
2:Task_analysis_using (1) and (2);
3:Task_Selection ();
4:end for
5:Update the status of each task;
6:Store task status information;
7:for all \(\textit{server}_{i}\) in \(\textit{edge}_{pool}\)\(\forall_{i}\in\{\)1,2,... \(n\}\);
8:Server_Selection ();
9:if there is no suitable \(\textit{server}_{i}\) for offloading
10:if \(\textit{task}_{i}\) is latency-sensitive
11:for all \(\textit{server}_{j}\) in \(\textit{cloud}_{pool}\), \(\forall_{j}\in\{\)1,2,... \(n\}\);
12: Find the \(\textit{server}_{j}\) with lowest CPU utilization;
13:end for
14:else // \(\textit{task}_{i}\) is latency-sensitive
15: Continue to run on the current user device;
16:end if
17:endif
18:end for
```  
**Algorithm 3**_LCDA ()_
## 3 Results
### Bioactive Metabolites  
#### 3.5.1 Polysaccharide  
Polysaccharide, a principal bioactive metabolite in _G. lucidum_ with excellent bioactivities, has been the research focus of mushrooms in recent years [21]. Se application affected polysaccharide metabolism as follows: 50 ppm Na\({}_{2}\)SeO\({}_{3}\) culture increased polysaccharide production, the percentage was approximately 9-25%; 200 ppm Na\({}_{2}\)SeO\({}_{3}\) application showed no impact on polysaccharide yield on the 4 th day but a dramatic reduction in polysaccharide production was observed from the 5 th day in this process (Table 2), and the decrease was recorded by 39-79% and 51-77% lower than those in the no Na\({}_{2}\)SeO\({}_{3}\) and 50 ppm Na\({}_{2}\)SeO\({}_{3}\) addition samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Se Compound**} & \multicolumn{3}{c}{**50 ppm**} \\ \cline{2-4}  & **Day 4** & **Day 5** & **Day 6** \\ \hline Dimethyl selenide & 1.000 \({}^{\text{b}}\) & 1.748 \(\pm\) 0.027 \({}^{\text{b}}\) & 1.634 \(\pm\) 0.046 \({}^{\text{b}}\) \\ \hline Carbo selenide & ND & ND & 0.088 \(\pm\) 0.006 \({}^{\text{a}}\) \\ \hline Dimethyl selenone & 0.053 \(\pm\) 0.001 \({}^{\text{a}}\) & 0.115 \(\pm\) 0.004 \({}^{\text{a}}\) & 0.170 \(\pm\) 0.009 \({}^{\text{a}}\) \\ \hline
**Se Compound** & \multicolumn{3}{c}{**200 ppm**} \\ \cline{2-4}  & **Day 4** & **Day 5** & **Day 6** \\ \hline Dimethyl selenide & 0.689 \(\pm\) 0.097 \({}^{\text{a}}\) & 0.596 \(\pm\) 0.007 \({}^{\text{a}}\) & 0.134 \(\pm\) 0.017 \({}^{\text{a}}\) \\ \hline Carbo selenide & 0.765 \(\pm\) 0.027 \({}^{\text{a}}\) & 0.681 \(\pm\) 0.068 \({}^{\text{a}}\) & 0.245 \(\pm\) 0.002 \({}^{\text{b}}\) \\ \hline Carbon diselenide & 9.467 \(\pm\) 0.012 & 0.669 \(\pm\) 0.071 & 0.313 \(\pm\) 0.082 \\ \hline Methylselenoacetate & ND & 0.235 \(\pm\) 0.005 & 0.052 \(\pm\) 0.012 \\ \hline Dimethyl selenone & 0.132 \(\pm\) 0.009 \({}^{\text{b}}\) & 4.092 \(\pm\) 0.015 \({}^{\text{b}}\) & 4.990 \(\pm\) 0.601 \({}^{\text{b}}\) \\ \hline Methyl diselenide & 0.538 \(\pm\) 0.069 & 35.329 \(\pm\) 0.032 & 44.242 \(\pm\) 0.022 \\ \hline
1- (methylselanyl)propanamide- & 0.068 \(\pm\) 0.008 & 0.108 \(\pm\) 0.011 & 0.080 \(\pm\) 0.015 \\
2-one & & & \\ \hline Dimethyl & ND & ND & 0.058 \(\pm\) 0.006 \\ \hline Dimethyl & & & \\ diselenenyl\_sulfide & 0.081 \(\pm\) 0.002 & 6.925 \(\pm\) 0.013 & 6.813 \(\pm\) 0.068 \\ \hline Triselenothane & ND & 21.495 \(\pm\) 0.053 & 31.129 \(\pm\) 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volatile Se compounds of _Ganoderma lucidum_ in the liquid culture process.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [2009-2011]  
Up to this stage, the retinal motion correction was tackled without using additional imaging modalities. This changed after 2008, when more approaches started to include other modalities such as SLO or FP to support the data visualization and registration correction, as shown in Figure 7.  
Figure 6: Cumulative bar plot showing the number of reviewed publications that included OCT retinal motion correction handling the fast, slow, and coronal planes.  
**[PERSON] et al. [59] (2009)** proposed a method to correct coronal misalignments by registering the OCT volume to an SLO image. This study included volumetric scans of 48 healthy and 116 glaucomatous eyes. As pre-processing, a vessel segmentation algorithm proposed by [PERSON] and [PERSON] [86] was used. The registration procedure had two main steps. The first step used a non-rigid technique proposed by [PERSON] and [PERSON] [87], where motion between two images is described by an affine transformation. The second step corrects vessel discontinuities by finding the horizontal shift at each pixel in the en face OCT image that best aligns the result with the SLO image [88]. The validation was done qualitatively by visual inspection and quantitatively by computing the improvement in overlap between vessels on the SLO image and on the motion corrected en face image.  
**[PERSON] et al. [60] (2009)** proposed a feature-based method to register OCT volumes. It was tested on 12 SD-OCT scans of six normal volunteers. As pre-processing, retinal layers of each volume were segmented and flattened. Then, a vector of thousands of 2D Scale-Invariant Feature Transform (SIFT) features [89] was generated on each image, and the correspondence between points belonging to two volumes of the same patient was established by comparing the distances. Only translation and rotation transformations were considered, and the distance between features was used to guide the registration. The validation was done qualitatively by visual inspection of the vessel pattern on the OCT en face images, and quantitatively by simulating deformations applying scaling, and rotation to the 3D-OCT scans.  
**[PERSON] et al. [62] (2009)** proposed an automated system for eye motion correction in SD-OCT volumetric data. They used a shape context algorithm that aimed at preserving the natural contours of the optic disc. Forty-three eyes (19 healthy and 24 glaucomatous) of 39 subjects were enrolled in this study. The projected en face OCT data was registered to the corresponding SLO image using a global transformation, computed based on retinal blood vessel maps detected on both SLO and OCT. Visual inspection was performed by three different observers.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
###### Abstract  
At some frequencies, Earth's ionosphere may significantly impact satellite communications, Global Navigation Satellite Systems (GNSS) positioning, and Earth Observation measurements. Due to the temporal and spatial variations in the Total Electron Content (TEC) and the ionosphere dynamics (i.e., fluctuations in the electron content density), electromagnetic waves suffer from signal delay, polarization change (i.e., Faraday rotation), direction of arrival, and fluctuations in signal intensity and phase (i.e., scintillation). Although there are previous studies proposing GNSS Reflectometry (GNSS-R) to study the ionospheric scintillation using, for example TechDemoSat-1, the amount of data is limited. In this study, data from NASA CYGNSS constellation have been used to explore a new source of data for ionospheric activity, and in particular, for travelling equatorial plasma depletions (EPBs). Using data from GNSS ground stations, previous studies detected and characterized their presence at equatorial latitudes. This work presents, for the first time to authors' knowledge, the evidence of ionospheric bubbles detection in ocean regions using GNSS-R data, where there are no ground stations available. The results of the study show that bubbles can be detected and, in addition to measure their dimensions and duration, the increased intensity scintillation (\(S_{4}\)) occurring in the bubbles can be estimated. The bubbles detected here reached \(S_{4}\) values of around 0.3-0.4 lasting for some seconds to few minutes. Furthermore, a comparison with data from ESA Swarm mission is presented, showing certain correlation in regions where there is \(S_{4}\) peaks detected by CYGNSS and fluctuations in the plasma density as measured by Swarm.  
Keywords:ionosphere; scintillation; GNSS-R; equatorial plasma depletions; bubbles; CYGNSS; swarm +
Footnote †: journal: Remote Sensing, 2020, 12, 3782; doi:10.3390/rs12223782
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Anaerobic Digestion of Substrates under Thermophilic and Mesophilic Conditions  
In the AD process, temperature is recognized as one of the key important factors that affects the entire process performance [20]. As general rule, the performance of the process is increased with the increase in temperature [20; 24]. For instance, the rate of anaerobic degradation processes, and particularly enzymatic and coenzymatic activities responsible for the rates of hydrolysis and methanogenesis, are highly affected by the dominant temperature. Additionally, some other factors, such as gas transfer rates and settling characteristics of biosolids, are significantly affected by the temperature [25; 26].  
For the AD process, two main temperature regimes are usually applied--mesophilic (37 \({}^{\circ}\)C) and thermophilic (55 \({}^{\circ}\)C). Mesophilic bacteria are supposedly more robust and can tolerate higher changes in environmental parameters, including temperature. Because mesophilic digestion takes place at a lower temperature, digestion in this temperature regime requires longer retention time and yields less biogas; however, mesophilic digesters remain attractive because of their stability and lower heating energy costs compared to thermophilic digesters [14; 24]. On the other hand, thermophilic digestion operates at a higher temperature, and consequently reaction rates and kinetics are enhanced, leading to the possibility of higher loading rates, thus increasing biogas production [14; 27; 28]. Moreover, thermophilic digestion is known to have higher levels of pathogen removal [29]. Nevertheless, this method, is more sensitive to toxic substances and changes of operation parameters [24].
# Recent Advances in Geographic Information System for Earth Sciences
## 5 GIS for Natural Hazards  
Advances in GIS have popularized its application to spatial analysis of natural hazards. In particular, GIS has been widely used for landslide susceptibility mapping. Landslide susceptibility maps generated by GIS can be effectively used for future land planning and monitoring. [PERSON] et al. [11] review studies of rainfall-induced landslides in the Indian Himalayan region to provide a reference point for the first time for researchers working in this region, and a summary of the improvements most urgently needed to better address landslide hazard research and management. Their study reveals that the inclusion of climate change factors and the acquisition of basic input data of the highest quality for computational models is critical for landslide susceptibility mapping.  
[PERSON] and [PERSON] [12] present an example of GIS-based landslide susceptibility mapping using ensemble techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), and dagging-functional trees (DFT)). A landslide inventory map with 263 landslide events is established for Zichang County, China, and 14 landslide conditioning factors selected to analyze the correlation between the conditioning factors and the occurrence of landslides. The results show that the prediction rate of the BFT model is the highest when compared with the accuracy of the four ensemble models.
## 5 Performance Evaluation
### Authentication between Entities
#### 5.2.4 Storage Cost  
In the proposed protocol, the edge device only needs to store its identity \(\delta_{d}ID\), a token \(O_{\delta_{d}}\), a secret key \(K_{\delta_{d}}\), and a session key \(K_{\delta_{d},\xi_{s}}\). Fewer storage requirements mean more edge users can be accommodated. The edge user storage cost of 3-Tier AKA is 640 bits, which indicates that this protocol is more lightweight when compared to the other three protocols. The edge device storage cost after the Authentication and Key Agreement phases is presented in Table 11 and Figure 15.
# CNN-Based Pill Image Recognition for Retrieval Systems
## 3 Methodology
### Classification  
After extracting the features of an input raw pill image, the next step is to predict the pill type using a classifier. Classification is a supervised machine learning technique where the class (pill type) to be predicted is known in advance. Several classifiers exist  
Figure 5: Pill image imprint extraction process: (**a**) Canny edge detector. (**b**) Dilution. (**c**) Scale Invariant Feature Transform.  
Figure 6: An overview of the proposed CNN model architecture.  
in the literature, though the vary in accuracy and efficiency. Below are some of the most prominent classifiers.  
1. k-Nearest Neighbors (k-NN) [13; 14] is a non-parametric classifier that assumes similar objects (i.e., data points) are usually \"closer\" to one another in comparison to dissimilar objects. k-NN measures similarity between data points using distance metrics. One of the most common distance metrics is the Euclidean distance and is measured by the following function: \[d(X,Y)=\sqrt{\sum_{i=1}^{n}(y_{i}-x_{i})^{2}},\] (1) where \(X\) and \(Y\) are two data points in the \(n\)-dimensional space, and \(x_{i}\) and \(y_{i}\) are Euclidean vectors from the point of origin. When our proposed model receives a query image, the model converts the image to feature vectors, which the classifier will use to predict the pill type in the query image. We set k to 5 for all our experimental analysis in Section 4.
2. The Support Vector Machine (SVM) [48] is a classifier that, when given a set of input objects, creates an imaginary wall that separates dissimilar objects. This imaginary wall is called a _hyperplane_, because it can separate data points represented in spaces beyond three-dimensional. Given a set of input data points, there are several potential hyperplanes that the SVM can create. The SVM creates the best separation between the data points, i.e., it only keeps the hyperplane that minimizes the classification error.
3. Residual Network [31], or otherwise known as ResNet, is a neural network-based model that can be used as a final identifier in a convolutional layout. ResNet can accommodate more than 50 layers and be used to classify and extract features in an image. This technique makes use of skip connections to reduce the training error and help add the output of earlier layers to later layers without losing the image quality.
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 2 Materials and Methods
### Datasets  
The dataset comprised 100 images with a 1024 \(\times\) 1024-pixel resolution. All images were taken by the authors using a Samsung Galaxy Note 8 or Fujifilm X100. Note that other camera sensors can be used to extend the dataset diversity. In this study, there are four classes in the dataset, namely background, crack, spalling, and void, which are examples of commonly occurring damages on concrete surfaces. Background, crack, spalling, and void in every image were manually annotated using Adobe Photoshop and labeled as 0, 1, 2, and 3, respectively. To deal with the common overfitting problem in neural network training, the dataset was further expanded using the data augmentation method. Before being augmented, the dataset was shuffled and portioned as 80% for training and 20% for validation. In the augmentation process, all 100 images were rotated three times at 90\({}^{\circ}\). Every rotation produced another 80 images for the training dataset and 20 images for the validation dataset. After being rotated three times, the total training dataset comprised 320 images, while the total validation dataset held 80 images. The authors decided to augment the images only by using a 90\({}^{\circ}\) angle. It is important to augment the dataset with high variability, because rotating the images by less than 90 degrees would only make a more similar image, which corresponds to a highly correlated dataset [33]. The data augmentation technique is illustrated in Figure 2.  
Figure 1: Proposed methodology.  
Figure 2: Data augmentation using rotation.  
The dataset was also configured to deal with complex background scenarios, for example, hands, building accessories, non-focused objects, stones, leaves, and many more, which are very commonly occurring. The surface damages were taken from cylindrical concrete specimens, mortar specimens, beam test specimens, and concrete walls. Some examples from the dataset are depicted in Figure 3.  
Before being fed into the neural network, the resolution of the dataset was first resized to 512 \(\times\) 512 resolution, because the original 1024 \(\times\) 1024 resolution consumed too much GPU memory. Then, the image was normalized by dividing the pixel value by 127.5 and subtracting it by 1. Therefore, the pixel value of every image was between 1 and \(-\)1. The annotation was divided into 4 slices because there were 4 classes as depicted in Figure 4. This is necessary to deal with the SoftMax activation function in the network output. For decent visualization, the class of each damage was represented with vibrant colors, while the background was represented by black. Cracks were visualized in red, spallings were visualized in blue, and voids were visualized in green. The visualization is illustrated in Figure 5.  
Figure 4: Class slices. White pixel is the labeled class, and blue pixel is the other classes.  
Figure 3: (a) Dataset from cylindrical concrete specimen; (b) dataset from mortar specimen; (c) dataset from beam specimen; (d) dataset from concrete wall.