esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
## 6 Conclusions  
In this study, the crack detectability performance of LCR and HCR gears was compared numerically. The gear geometries were created using the rack-type cutting tool. The crack initiation point of the gears was defined according to the static bending stress analysis. An artificial initial crack was defined where the maximum stress occurred on the tooth root. Crack propagation analysis was performed to obtain realistic crack paths. As a result of the crack propagation analysis, the gear geometries with realistic crack propagation paths were determined. The STS of these geometries were calculated using the finite element analysis. The TVMS was calculated for both LCR and HCR gears by using the STS of the gears. A parametric six degrees of freedom dynamic model was proposed to define the dynamic response of the LCR and HCR gears. The variation of dynamic force obtained from the dynamic model was used for crack detection. Statistical indicators like kurtosis, RMS, crest factor, mean, standard deviation, and variance were taken into consideration for the detection of tooth root cracks. The results obtained from this study can be summarized as follows:  
* The cracks propagated along the tooth width of both LCR and HCR gears. The TVMS of the HCR gears was higher than the LCR gears. When the contact ratio increased, the TVMS was decreased for the HCR gears. * The dynamic forces were lower for the HCR gears than the LCR gears. When the crack size increased, the TVMS decreased. However, the peak value of the dynamic forces increased sharply. Kurtosis, RMS, crest factor, mean, standard deviation, and variance were used as a statistical failure indicator. The % change of the statistical indicators increased continuously with the increase in crack size. * The crest factor was defined as the most sensitive statistical indicator for early crack detection in both LCR and HCR gears. Variance and kurtosis were defined as the second and third sensitive indicators. On the other hand, the RMS and mean were the most insensitive indicators for crack detection in the LCR and HCR gears. * Although HCR gears have a higher contact ratio and higher load-carrying capacity than LCR gears, they do not give better results than LCR gears in detecting cracks. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data is contained within the article. **Conflicts of Interest:** I confirm that there are no conflicts of interest related to this publication, and no significant financial support has been received for this work that could have impacted its outcomes. **Nomenclature**
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 4 Test Method  
The experimental characterization of the prototype has been done by adopting two main types of tests: component-level tests and board-level functional tests.
## 3 Results and Discussion
### Metabolic Changes Associated with Pest Control Methods  
The impact of pest control methods on phenolic content, phytic acid, and antioxidant activity of rice reflects the interactions of these strategies with plant metabolic and physiological processes.  
* Phenolic compounds: Biological control promotes phenol synthesis by activating the shikimic acid pathway, strengthening plant defenses and antioxidant capacity. In contrast, chemical methods tend to inhibit these responses by reducing the natural stimuli that activate these metabolic pathways [29].
* Phytic acid: Biological treatments enhance the interaction with soil microorganisms, optimizing nutrient uptake and favoring the accumulation of this compound. On the other hand, chemical pesticides can alter its synthesis by interfering with enzymatic processes related to phosphorus metabolism [32].
* Antioxidant activity: Biological controls stimulate systemic acquired defense responses (SARs), which enhances the presence of antioxidant compounds such as phenols and flavonoids. However, chemical control may limit these responses by reducing plant interaction with environmental factors that activate these natural mechanisms [29].
## 4 Discussion
### Computational Cost Analysis of the Proposed Redundancy Removed DTDWT (RR-DTDWT)
& & & & & & \\ \hline \multirow{3}{*}{[60]} & \multirow{3}{*}{2019} & Fuzzy-Rules-Based Sub-Sand & \multirow{3}{*}{99\%} & \multirow{3}{*}{96\%} & \multirow{3}{*}{4.8/h} & \multirow{3}{*}{96.12\%} & \multirow{3}{*}{98\%} & \multirow{3}{*}{0.9754} \\  & & Directed Acyclic Graph SVM & & & & & & \\ \hline \multirow{3}{*}{[57]} & \multirow{3}{*}{2019} & DWT + Sigmoid Entropy + SVM & \multirow{3}{*}{94.21\%} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{94.38\%} & \multirow{3}{*}{-} \\ \cline{1-1} \cline{5-5}  
\(m\) is the parameter used for sample entropy calculation (as aforementioned, \(m=2\) in this work). Feature selection methods can be divided into two categories: filter-based and embedded-based methods [63]. Filter-based methods rank all potential features according to a predefined feature score based on the intrinsic property of data, which is independent of the following classification procedure. Embedded-based feature selection methods integrate feature selection into the learning procedure, involving more parameters to be fine-tuned. Therefore, embedded-based methods are computationally expensive. The AGRM algorithm used in our work is a filter-based method which is computationally efficient. Moreover, the feature score was pre-calculated using training set in our work. To develop an online seizure detector, the AGRM-based feature selection adds no additional computational burden. In contrast, by selecting only those features with the minimal redundancy, the computational cost is further reduced. In the classification procedure, the computational cost of SVM model is only \(O(1)\) for a given feature vector of a specific EEG segment. In summary, the computational cost of the proposed RR-DTDWT is \(O(N)\), which can support its application to an online system.
## 4 Digital Model of the Area of Interest
### Computation of X, Y, and Z Coordinates in the Eci System  
The next MATLAB code (version R2022b or higher) calculates coordinates in the Earth-Centred Inertial (ECI) coordinate system (Figure 1). The input data are the GPS coordinates and the altitude (above mean see level height) of the peak of Mount Everest. The outputs are the coordinates of this point in the Euclidean coordinate system with the center at the center of the Earth ((4) point of the algorithm). The calculation requires the Mapping Toolbox of the MATLAB system. This toolbox contains the digital models of the globe (geoid). The results of the computations are given in meters.  
This code serves as an example for moving the origin of the coordinate system from the center of the earth to an arbitrary point whose GPS coordinates are known. Here we use the _geodetic2 ecef_ MATLAB command and the digital model of the earth (_referenceEllipsoid_('\(GRS80\)')) in MATLAB.  
1 at = 27.988056; lon = 86.925278; H = 8848; N = egm96 geoid(lat,lon); h = H + N  spheroid = referenceEllipsoid('GRS80'); [X,Y,Z] = geodetic2 ecef(spheroid,lat,lon,h)  
h = 8.8193e+03 X = 3.0274e+05 Y = 5.6360e+06 Z = 2.9795e+06  
\"Projections for large areas usually use a simple sphere; for smaller areas, where accuracy gains in importance, the projection uses an ellipsoid which locally corresponds to a _geoid_, the name for the shape with the most accurate correspondence to the actual oblate and irregular shape of the earth at a given time.\" [6]. The description of the _GRS 80_ reference model can be found in the publication [12,13].
## 2 Materials and Methods
### Model Proposal  
Lastly, the third phase of the methodology involves the proposal of the MaMoReS. In this stage, the MaMoReS integrates the findings from the second phase to identify model elements (levels, dimensions, and sub-dimensions). Specifically, the most common number of levels is selected, while all the dimensions found across the relevant documents are included in the model. The last factor possibly guarantees a higher level of completeness compared to MMs defined through nonsystematic approaches. The dimensions are also cross-validated through the SLNA, which also serves to identify sub-dimensions. The application of the MaMoRes follows four main phases shown, as shown in Figure 3. Considering Figure 3, the phases are reported on the left side while the outputs of each phase are shown on the right side.  
First, a structured questionnaire is designed using a 5-level Likert scale. The users must fill in the questionnaire, through which they assign a score to each sub-dimension (phase 1). Next, the scores of the sub-dimensions belonging to the same dimension are aggregated according to Equation (1) (phase 2). This leads to the identification of a score for each sub-dimension. Subsequently, the maturity level is obtained by aggregating the score of each dimension following Equation (2) (phase 3).  
\[D_{d}=\frac{1}{S}{\sum_{a}}S_{s,d}, \tag{1}\]  
\[L=\left\lceil\frac{1}{D}{\sum_{d}}D_{d}\right\rceil, \tag{2}\]  
Considering Equations (1) and (2), \(S\) represents the number of sub-dimensions within each dimension, while \(D\) identifies the number of dimensions of the model. \(L\) denotes the assessed maturity level. Within these equations, \(S_{s,d}\) represents the score obtained through the questionnaire for sub-dimension \(s\) within dimension \(d\), while \(D_{d}\) identifies the score for dimension \(d\). As a final step, the users can identify the weakest dimensions and sub-dimensions, directing efforts to improve their resilience maturity (phase 4). Indeed, following the approach described above, the score is presented both in an aggregate view as the maturity level and in more detail for dimensions and sub-dimensions. This allows for the identification of areas within the model that exhibit lower scores, thus providing specific guidelines for improvement. Such improvements can positively influence the scores of the identified dimensions and the overall maturity level concerning supply chain resilience. It is worth noting that these steps are usually the same for the other MMs as well. Differences may arise depending on the equations used to aggregate sub-dimension and dimension scores.
# Advancing Digital Twin-Based Collision Avoidance:
## 3 Evaluation of Results
### Efficiency Analysis through Analytical Extrapolation  
The efficiency of a virtual collision avoidance system is compared against other methods in an industrial intralogistics setting, involving a fleet of forklifts with a top speed of 15 km/h, which is the common top speed in Europe. Assuming a minimum clear line of sight of 2 m for the driver, a basic braking model is introduced for emergency situations, splitting the total braking distance into reaction and braking distances:  
\[d_{b}=\frac{v^{2}}{2a}\qquad d_{r}=vt_{\text{rct}}\qquad d_{\text{eb}}=d_{r}+d_{b} \tag{1}\]  
Here, \(d_{r}\) represents the reaction distance, \(d_{b}\) represents the braking distance, and \(d_{\text{eb}}\) represents the total effective braking distance. The reaction time \(t_{\text{rct}}\) is set at 1 s with \(v\) being the forklift's speed and \(a\) the deceleration, which is assumed to be \(-10\) m/s\({}^{2}\). The reaction distance is the space needed for the driver to respond to an obstacle. The forklift's maximum speed is calculated by solving for \(v\) in the equation with \(d_{\text{eb}}=2\) m, resulting in a top speed of 6.6 km/h. Efficiency is then gauged based on the ratio of drivable distance at this maximum speed, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Unit** \\ \hline \(v\) & Forklift speed & km/h \\ \(a\) & Deceleration & m/s\({}^{2}\) \\ \(d_{b}\) & Braking distance & m \\ \(d_{r}\) & Reaction distance & m \\ \(d_{\text{eb}}\) & Total effective braking distance & m \\ \(t_{\text{rct}}\) & Reaction time & s \\ \hline \(\tau_{d}\) & Maximum delay in decentralized communication & ms \\ \(\tau_{\text{ca}}\) & System latency in centralized scenario with location aggregation & ms \\ \(\tau_{\text{cw}}\) & System latency in centralized scenario with warning-only approach & ms \\ \(f\) & RTLS rate & Hz \\ \(\tau_{\text{rtH}}\) & Round-trip time in the 5G network & ms \\ \(\tau_{\text{edge}}\) & Edge communication and computation delay & ms \\ \(m_{\text{ca}}\) & Number of Radio Protocol Data Units (R-PDUs) on the channel for & - \\  & centralized with location aggregation & \\ \(m_{\text{cw}}\) & Number of R-PDUs on the channel for centralized with warning-only approach & - \\ \(P_{w}\) & Probability of warning & \% \\ \(n\) & Number of trucks & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of variables.  
This is contrasted with scenarios where trucks are limited to a speed of 6.6 km/h for safety without additional controls. Zoning, another method, restricts speed in areas with limited visibility, with an assumed 25% area limitation. The third method, Dynamic Braking, activates speed reduction in response to detected collision risks. Given the low frequency of collision risks (set at 1%), Figure 4 illustrates that dynamic speed reduction is more efficient than zoning and negates the need for zoning configurations. Therefore, accurate vehicle positioning information, provided by an RTLS via a 5G network, is crucial.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Optimization Algorithm-Based Methods  
Due to the complexities of bridge structures and the rise of long-span bridges, ordinary algorithms have been unable to handle the task of increasing bridge monitoring data. Therefore, many optimization algorithms have been applied to bridge damage identification. This section mainly reviews meta-heuristic algorithms and artificial neural network (ANN) algorithms in optimization algorithms. The Bayesian method will be detailed in Section 5.4.
# Linked Data Triples Enhance Document Relevance Classification
###### Abstract  
Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is \"about\", rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized newsfeed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.  
linked data triples; named entities; topic modeling; deep learning; relevance classification Article
# Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)
## 2 Conventional Kalman Filter  
The filter estimates, at each time, the a priori state of the system based on the a posteriori estimate for the previous moment and its dynamic equation. With this a priori estimate of the state, the corresponding predicted measurement \((H_{k}\Sigma^{-})\) that corresponds to that state is obtained. Then, it is compared with the actual measurement \((z)\) made at the same moment, then it corrects the a priori estimation as a function of its discrepancy and weight between the trust in the a priori estimates and the measurements represented by the gain \(K\) of the filter. The process performed by the filter can be separated into two phases: prediction and correction [19].  
Consider a discrete time model for a linear stochastic system:  
\[x_{k+1}=\Phi_{k+1,k}X_{k}+G_{k}w_{k}, \tag{3}\]  
\[Z_{k}=H_{k}x_{k}+v_{k}, \tag{4}\]  
where \(x_{k}\in\mathbb{R}^{nx1}\) is the state vector; \(Z_{k}\in\mathbb{R}^{mx1}\) is the measurement vector; \(w_{k}\in\mathbb{R}^{nx1}\) is the disturbance input vector; \(v_{k}\in\mathbb{R}^{mx1}\) is the measurement noise vector; and \(\Phi_{k+1,k}\in\mathbb{R}^{mxn}\), \(G_{k}\in\mathbb{R}^{mxn}\), and \(H_{k}\in\mathbb{R}^{mxn}\) are the system matrices. The covariance of \(w_{k}\) is \(Q_{k}\), known as the process covariance matrix, and that of \(V_{k}\) is \(R_{k}\), known as the measurement covariance matrix, and they are presumed to be zero-mean Gaussian white noise with the symmetric positive definite covariance matrices \(Q_{k}\) and \(R_{k}\), respectively. Then, \(w\sim N(0,Q)\) and \(v\sim N(0,R)\) at time \(t_{k}\), where a superscript \((-)\) indicates the a priori values of the variables and a superscript \((+)\) denotes values after the measurement update. The Kalman filter is described by following the recursive equations under the assumption that the system matrices are well known:  
Time extrapolation:  
\[\hat{x}_{k+1}^{-}=\Phi_{k-1,k}\hat{x}_{k-1}^{+}, \tag{5}\]  
\[P_{k+1}^{-}=\Phi_{k-1}P_{k-1}^{+}\Phi_{k-1,k}^{T}+\Psi_{k-1}Q_{k-1}\Psi_{k-1} ^{T}. \tag{6}\]  
Measurement update:  
\[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+K_{k}\Big{(}z_{k}-H_{k}\hat{x}_{k}^{-}\Big{)}, \tag{7}\]  
\[K_{k}=P_{k}^{-}H_{k}^{T}\Big{(}H_{k}P_{k}^{-}H_{k}^{T}+R_{k}\Big{)}^{-1}, \tag{8}\]  
\[P_{k}^{+}=(I-K_{k}H)P_{k}^{-}, \tag{9}\]  
where \(P_{k}\) is the covariance of estimation uncertainty, \(K_{k}\) is the Kalman gain matrix, and \(z_{k}-H_{k}\hat{x}_{k}^{-}\) are the innovations of the discrete Kalman filter.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants  
Maritime environmental disasters generally occur after collisions, grounding, stranding heavy weather, seismic events, explosions, or fire. What follows may be oil spillage, bunker, dirty water, or harmful chemical substances, with serious impacts on the environment and affected communities [92; 93]. Our focus here is not to survey water-monitoring USVs. Instead, we focus on major problems which may involve more than standard water sampling tasks. We have chosen to divide such events into three major topics: chemical (Section 4.1); biological (Section 4.2); and radioactive (Section 4.3) hazards.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 4 Experiments
### Databases  
Here, we briefly introduce the four databases used in the experiments. These are the Extended Yale B face database [59], AR face database [60], Binary Alphadigits database and PolyU 3D palmprint database [61]. These databases are used to address different kinds of recognition problems, i.e., face recognition in various lighting conditions, face recognition with occlusions, handwritten digits and letters classification, and palmprint recognition. In this way, we could make a thorough comparison between the proposed DB2 DPCA and related algorithms.  
The Extended Yale B face database includes 2414 cropped face images from 38 individuals, with about 64 images per individual. There exist 18 broken images in this database. The images were captured under various lighting conditions. The image size is 192 by 168. For computational convenience, the images are resized into 48 by 42. This database can be downloaded from [[http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html)]([http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html)) (accessed on 18 October 2016).  
The AR face database contains 3120 images of 120 subjects, with 26 images per subject. The images were taken with different facial expressions and illuminations, and some images were occluded with black sunglasses or scarves. Here, we use a cropped version of this database where the image size is 50 by 40. This database can be downloaded from [[http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html](http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html)]([http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html](http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html)) (accessed on 18 October 2016).  
The Binary Alphadigits database consists of 1404 binary images from 36 classes, with 39 images per class. The images are handwritten digits and letters. Specifically, this database contains digits \"0\" through \"9\" and capital letters \"A\" through \"Z\". The image size is 20 by 16. This database can be downloaded from [[http://www.cs.nyu.edu/~roweis/data.html](http://www.cs.nyu.edu/~roweis/data.html)]([http://www.cs.nyu.edu/~roweis/data.html](http://www.cs.nyu.edu/~roweis/data.html)) (accessed on 30 September 2018).  
The PolyU 3D palmprint database contains 8000 images collected from 400 different palms, with 20 images per palm. Each image contains a 3D region of interest (ROI) and the corresponding 2D ROI. In our experiments, only the 2D ROIs are used. The image size is 128 by 128. For computational convenience, the images are resized into 32 by 32. This database can be downloaded from [[http://www4.comp.polyu.edu.hk/~biometrics/](http://www4.comp.polyu.edu.hk/~biometrics/)]([http://www4.comp.polyu.edu.hk/~biometrics/](http://www4.comp.polyu.edu.hk/~biometrics/)) (accessed on 9 September 2018).  
Table 2 shows the statistics of the four databases. Figure 2 shows some sample images.
## 3 Unsupervised Learning
### Self-Organizing Maps  
Self-organizing maps (SOM) is a type of unsupervised learning algorithm that is also referred to as Kohonen maps. The key notion behind how SOM works is that it transforms multi-dimensional datasets into a typically non-linear two-dimensional representation in the form of a grid or map [18; 19].  
Self-organizing maps learn by matching the input datasets into the best nodes, and the nodes will be associated with other nodes that have a similar characteristic for a better fitting [19]. Initially, each input data sample or point will assign random weights to each node at first iteration. The weights are either the point representative or the coordinates of the input data sample and are used to determine the best node that fits with the data point. Euclidean distances between the input and the nodes are calculated to determine the best matching unit (BMU). The node with the shortest distance to the specific data point will be defined as the best matching unit. The weights inside the node will be updated to ensure a better match with the data point. The neighboring nodes will also be updated so that the nearby nodes will have a closer characteristic to the best matching unit node. As a result, the more identical nodes will be situated nearer to the grid. Meanwhile, the less identical will be located further away from the grid.